Subjective questionnaire.
\r\n\tThis book aims to address the new developments in the rapidly evolving field of evo-devo in the post genomics era. All recent biological and medical breakthroughs in the evo-devo field are welcomed. Finally, review articles encompassing recent advances, development current and future trends are also more than welcomed.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"4c17ab0c64ce206c75ad6cec64e05737",bookSignature:"Dr. Dimitrios P. Vlachakis, Prof. Elias Eliopoulos and Prof. George P. Chrousos",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10100.jpg",keywords:"genetics, genome, non coding RNAs, post translational modifications, methylation, ancestral genes, horizontal gene transfer, molecular regulators, gene expression levels, transcription factor, chromatin modifications, developmental biology, cell growth, cellular differentiation, stem cells, big data, algorithm design, cloud computing, next generation sequencing",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 20th 2019",dateEndSecondStepPublish:"October 11th 2019",dateEndThirdStepPublish:"December 10th 2019",dateEndFourthStepPublish:"February 28th 2020",dateEndFifthStepPublish:"April 28th 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"179110",title:"Dr.",name:"Dimitrios",middleName:"P.",surname:"Vlachakis",slug:"dimitrios-vlachakis",fullName:"Dimitrios Vlachakis",profilePictureURL:"https://mts.intechopen.com/storage/users/179110/images/system/179110.jpeg",biography:"Dr. Dimitrios Vlachakis is an Assistant Professor at the Genetics Laboratory at the Biotechnology Department of the Agricultural University of Athens, Greece. He leads the Genetics and Computational Biology Group and his main scientific interests revolve around the investigation of genetic polymorfisms, genetic variability in viral strains and the in silico drug design of novel antiviral and anticancer agents. To date, Dr. Vlachakis has published more than 90 original research articles in international peer-reviewed journals with impact factor, 100+ articles in international conference proceedings, 5 monograph ISBN books, 2 scientific patents and has been on the receiving end of numerous grants and awards. 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A biophysicist/crystallographer by training has considerable experience in biomolecular structure analysis, epitope mapping of protein receptors with experimental and computational methods, protein structure prediction, ligand and drug design, protein design, in silico antibody design and biosoftware development. Prof. Elias Eliopoulos has experience and international reputation on computational protein folding, ab initio and homology modeling of proteins derived from edge gene research and membrane protein modeling. 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Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"52398",title:"Improving Informational Bases of Performance Measurement with Grey Relation Analysis",doi:"10.5772/65286",slug:"improving-informational-bases-of-performance-measurement-with-grey-relation-analysis",body:'\nIn business practice, empirical data with causal relevance for financial performance generation are required for steering and controlling demands. Often there is a shortage of such data. Therefore, a severe problem has to be solved by the management. From the development and implementation of measurement and management systems, for example, performance management and measurement, a provision of causal-oriented data as a quantitative basis for steering and controlling purposes can be expected. PM as the quantitative database of management control operates as an information supply system for the performance management. The current relevance of the topic is shown by Rigby and Bilodeau [1] who expose the balanced scorecard (BSC) as one of the most popular management tools for strategically oriented performance management. A comprehensive BSC also requires the identification of causal interdependencies between indicators that drive the corporate’s financial performance. But in business practice, often a shortage of especially objective data exists. To derive causal hypotheses on financial performance generation, subjectively based data can be collected and used in the framework of a PM design as a surrogate. Afterwards, these data can be intersubjectivated groupwise and objectivated by statistical validation.
\nGenerating subjectively based data by interviews or questionnaires often leads to an excess of such data implying problems of handling. In this case, appropriate performance indicators have to be selected which contribute to the success of an organization. Identifying and ensuring an effective PM demands a focus on the cause-and-effect relations between these performance indicators. Their estimation and validation induce methodical questions how to cope with imperfect information. This challenge inter alia demands analytical decision support. Besides the known disciplines and methods to handle imperfect information, like stochastics, Fuzzy Mathematics or DEMATEL (as a technique of groupwise intersubjectivation), this chapter provides a partial view on Grey systems theory (GST) as a conception to improve poor data situations for PM and to operate already with a few data.
\nAs organizations often do not have sufficient objective databases for PM purposes, they must refer to subjective data usually filtered out of tacit knowledge stemming from employee interviews. These finally lead to a large number of performance indicators determined by the means and relations of the fixed corporate strategy and its usage of identified cause-and-effect relations which is indispensable for causally ambitioned performance control. This is the framework that demands an evaluation and reduction of the obtained variety of indicators to main key performance indicators (KPIs).
\nFor instance, 50 performance indicators are denominated as candidates by a company’s employees with expert status. Hypostatizing the causal interdependencies of these would leads to a challenge without any operations research (OR) support. In addition, how could an organization obtain quick but also valid information for the selection of the KPIs, without multicriteria decision support, if the Statistics require a sample size implying longstanding data collection?
\nDo there exist methods to transform subjectively based data into intersubjectivated ones reaching closer to quasi-objective data and therefore allowing more detailed conclusions for the PM context?
\nSuch methodology is made available by the GST that has been developed to handle situations with incomplete information that cannot be coped by other support disciplines. Thus, performance indicators can be selected by aid of the Grey relation analysis (GRA) based on subjective information. GRA analyses the geometric relationships of compared discrete objectives as well as of subjective indicators and is able to operate with a sequence length of minimally four data points. In situations with databases being too small for statistical analyses, processes of intersubjectivation or validation become possible. With GRA, PM would be enabled to prepare an order of the KPI priorities resulting from the geometrical similarity of the performance indicators’ time series to the sequence of the top strategic financial performance ratio. In addition, it is also possible to display the interdependencies between the residual indicators in a network or in a causally ambitioned map by GRA to steer and control the performance generation in the PM system context.
\nTo focus the whole company on a long-term financial success, it is necessary to reflect and if required to recombine the objectives of the corporate strategy on every single company level, in each business unit and in the cognitive systems of the employees. Thus, the integration and therefore the implementation of the corporate strategy ensures the value creation in an organization. This value creation is also known as the generation of financial performance. The term “performance” is much discussed and underlies no standardized definition. It only becomes clear by an individual corporate-specific description [2]. The special task of the PM is to provide an information supply system for the management by finding the causal relationships that are related to financial performance. The causes of financial performance are not only financially dimensioned. The challenge of a thriving business is to include nonfinancial performance measures often anchored in the intuitive implicit knowledge of the employees. The ability to respond to altered circumstances presupposes an update of critical success factors [3, 4]. An entire focus of an organization on a backward-looking financial performance indicator system as it was usual in traditional Management Accounting with its reference to the decomposed structure of financial ratios (e.g. DuPont scheme) is inconceivable in today’s dynamic business and Management Science. Instead, a new operational framework is necessary. Such a scope should include all relevant aspects of the corporate performance [4, 5]. This superior framework is tailored as a management and control system and is also known as the Performance Management of the organization. To provide an adequate information basis for the Performance Management, a measurement of the KPIs is necessary [6]. Therefore, the PM addresses three central functions: measurability of financial and especially nonfinancial indicators, identification and selection of the most important indicators that drive financial performance and lead to value generation. Thus, an additional transparency for the different members of the organization is provided. For this, a sound knowledge of employees about the process of financial performance generation is necessary [7].
\nHence, the interaction of Performance Management and PM shows that these two internal corporate systems cannot be separated. The PM may be viewed double-edged: First, as a feedback-oriented system that supplies Performance Management with norms and information on current processes by data measured in the past and presence. Thus, a base to derive counteractions exists. Furthermore, a feedforward tool is made available, which informs about failings of the conceptual framework so that a new causal model will have to be developed, validated, and implemented [4]. Without any knowledge about the interaction between those systems, the organization misses the opportunity to control and dominate the performance-generating process [2, 8].
\nThe performance-generation process has multidimensional aspects incorporated by the responsibility of multiple causes that lead to an unidimensional financial effect specified by the owners of an organization [4, 9]. Consequently, the interaction of the multidimensional PM and the Performance Management conduce to the improvement of the corporate performance. For quantifying the financial and nonfinancial measures, the PM serves support for a performance recording. Often a shortage of available, objective empirical data for the representation of performance indicators occurs, which has to be handled by the management control. In case of missing objective data, it is indispensable that the PM manages this problem by collecting subjectively based data on the basis of surveys or interviews which enable a quasi-objectivation of these measures [10, 11]. Even if organizations should have historical objective data, subjectively based data should not be ignored. Many times, historical data have been collected in varying frequencies and ranges. In this case, an usage within a PM seems to be inappropriate [12].
\nRevelations of the interdependencies between the KPIs that are essential for the value creation or rather the performance improvement can only be determined by sufficiently articulated knowledge. At this, it is necessary to differentiate between the explicit knowledge on the one hand which is simple to communicate and can be made available to all individuals that want to use it. On the other hand, there is the intuitive implicit knowledge that makes important performance-related causal relationships available [13, 14]. The implicit knowledge is characterized by four conditions: difficult to imitate, hardly to replace, only transmittable to a limited extent (not by the normal use of language), and scarce existence [13]. The tacit knowledge is, however, very difficult to create because it has been sharpened over years in extensive activities and experience of individuals. To evoke this dormant, subject-bound, intuitive knowledge, Abernethy et al. [10] propose to interview or rather execute subjective questionnaires so that the employees give partly insights into their tacit knowledge. This results in a variety of subjectively based data which first need to be reduced to a manageable level and can be intersubjectivated to work with. Here, the task of the PM should be based on an adequate—even optimal—complexity reduction [15]. Thus, the immense amount of subjective data has to be channelled, properly. Besides the PM has to concentrate on the essential factors with the aid of intersubjectivated data. All this is taking place to avoid that the PM System is more confusing than helpful.
\nThe PM should not only be understood according to the phase of validating the established hypotheses at the beginning of the PM process but rather by Bourne et al. [16] as a tool to identify appropriate indicators covering structure and processes of an organization in a dynamically changing environment. To focus on inadequate measures would constitute a resource-wasting framework. Hence, the organizational, multidimensional PM System requires a selection of such KPIs endogenously linked to the corporate strategy and thus able to improve the performance [17]. Various studies [18, 19] detected that systems that are constructed as too complex have a negative influence on the performance. Too complex systems lead to an overload of information and consequently cause an increase of administrative costs [20]. Therefore, the amount of KPIs has to be limited to a level cognitively manageable by the members of the organization [17, 21].
\nOn account of a lack of objective data, organizations may refer to subjective estimations stemming from samples being too small or too fragmented to apply statistical methods (Figure 1). Small or fragmented data sizes lead to incomplete information. This problem is to be solved by the GST. Fuzzy Mathematics, which focus on experience data of an individual, are characterized by a clear content (intension) but by unclear (not determined) quantitative boundaries of an expression—for example “very strong”—(extension of information). GST is more suitable with concepts of multiple meanings (e.g., performance), is additionally able to handle fuzziness situations and disposes of a clearly defined extension [22]. Thus, the above-mentioned problem of poor and incomplete information is almost impossible to solve with Fuzzy Mathematics or Statistics. The incomplete nature of the information needs to be managed in the PM context. A subjective query that was collected over a small number of periods can be considered as an incomplete information, if the experts of the organization deliver only a few estimations of the extent of an indicator [23]. Reducing the volume of performance indicators needs subjectively-based and thus poor information [24]. The organizational challenge is to solve this problem by providing valid results for PM also in case of small samples in situations of incomplete information. This would be possible by reference to support models for comprehending and decoding the problems of the system [25, 26].
\nImperfect information, situations and instruments.
In the PM context, it is important that a strategy is formulated as simple as possible [27]. If the extension of the strategy is then reduced to a manageable minimum, the organization possesses a list of factors most important for performance generation [28]. But this does not deliver a sufficient condition to control an organization successfully. Instead, it is essential to know how the factors are interrelated to actuate the right “lever” for an increase in financial performance [29]. Therefore, a causally ambitioned network of interdependencies of the KPIs seems to be useful [30].
\nThe performance of an organization can be interpreted as a result of past actions of the managers. To explain this performance, a causally ambitioned model with all relevant relations between the considered indicators is indispensable. Thus, the process leading to performance can be visualized. Such an illustration (e.g., a map) delivers – especially if structured—a blueprint for implementing the corporate strategy [31].
\nA map generally provides the visualization of a reference framework. In the 1970s, the political scientist Axelrod [32] spread the methodology of cognitive maps that should illustrate simplified social studies. A cognitive map provides an optical representation of the structures people perceive in their environment [9]. Cognitive maps serve management with a tool for evaluating alternative business situations in order to meet better an uncertain, dynamic corporate environment and to simplify complex issues [33]. Here, the organization should, however, focus on a visualization of tacit knowledge [10].
\nA simple list of the most important corporate strategy factors would point out the indicators the organization has to focus on. As an enumeration, such a list, however, would not represent the interdependencies within the system. To control as well as to monitor the performance generation, it is necessary to understand the causal relations between the KPIs [34, 35]. Therefore, it is fundamental to keep in view the cost–benefit ratio: a too detailed map costs a large amount of time [10], a graphical apposition of ovals does quantify dependencies in the system [32]. GRA as an OR management support is simple in usage and provides meaningful results already after a few periods. Additionally, it even enables a visualization of the outcome within a relational network [36].
\nIn contrast to parametric approaches like the correlation analysis, nonparametric mapping approaches are much more able to represent the multidimensionality of the performance generation. By avoiding assumptions, nonparametric approaches focus on mapped causal relationships among the measures based on their perceived environment [11]. Organizations tend to skip a statistical validation of their causal model. The reasons for this are the perceived obviousness of the model, the time exposure or rather the high validation costs [20, 37, 38]. The changing dynamic and competitive environment requires an adjustment of an organization’s causal model to adapt the strategy continuously. In order to meet this condition sufficiently, an ongoing customization of an organization’s cause-and-effect network is not manageable with regard to time and costs that appear by longstanding serial questionnaires [39].
\nA sole focus on subjectively based data can lead to systematic judgment errors by incorrect estimations of individuals. Thus, such data are to be considered as incomplete because of small or fragmented sample sizes [39]. In addition, subjectively based data in the PM context can imply errors in the described network of relations because of the occurrence of new environmental circumstances. On account of these changes, a resulting illustration of interdependencies can be inadequate to reality. Therefore, it is indispensable to improve these data with quantifying methods and consequently intersubjectivate them. So, there is a necessity of research in new mathematical applications with regard to measurement and especially to PM which is yet limited to the fundamental methodologies of sociology (survival analysis), psychology (various psychometric methods) and economics (econometrics) [11]. In such social economic systems with poor information, it is challenging to look for solutions in Statistics because of the system’s dynamic characteristics. In this case of incomplete and fast-changing information, the application of GRA may be advisable [40].
\nThe GST first appeared in 1981 by Deng [41]. According to that, a Grey system (GS) has the structure of a black box, which contains a system of both known and unknown variables. The unknown represents a “black”, totally incomplete information and the known a “white”, absolutely complete information. Hence, a (Grey) incomplete information can be understood as an information that is partially known as well as to some extent unknown [42]. Inconsiderably, whether it is the message format, the coordination mechanism or just the behaviour within a system: As soon as a lack of information within this system is disseminated, it is referred to as a GS [36]. In practice, as already mentioned in the previous chapter, it is difficult to concretely obtain all information about an examined object [40]. Systems with a lack of information can be found everywhere: for example, the biological limitations of the human senses, the constraints of important economic conditions or the unavailability of technical resources. The GS as a system of incomplete information is also known as an “indeterminate system” of which the fundamental characteristics are small samples and/or interruptions of time series [42].
\nOn the account of the small size of the samples problems within information systems with incomplete information cannot be solved with statistical methods [42]. With increasing sample size, the statistical power of a validation method grows [43]. Thus, sample sizes are preferable, in which the standard error is as low as possible. Various studies [44–46] consider large numbers of data points as necessary for the application of statistical support of time series as well as cross-sectional analysis in PM. For instance, according to McDonald and Ho [45], an organization needs to obtain quarterly data for a moderate time series analysis for almost six years in order to make a statement about possible causal relations by structural equation modelling. In social and economic systems, which are driven by the highest degree of dynamism and continuous changes, such problem solving demands for overextend the conditions of typical situations of business practice. Some variables in the system underlie a faster change of their environment conditions than the measurement lasts at all, so that the analytical results are irrelevant and therefore superfluous [41]. The resulting situation of incomplete information can be supported by GST [23].
\nThe enormous volumes of data arising from subjective questionnaires about the performance indicators (ki) need reduction. Table 1 shows the result of such a decimation to those indicators which are most essentially interlinked with the financial performance generation. For this, benchmarking of the most representative indicators is crucial [47]. The expression xit represents an opinion aggregated from the individual members of an expert group in period t to performance indicator ki. Here, GST disposes of a major advantage because of the ability to provide valid results already from a number of data points with t ≥ 4. Thus, the GST is able to work with incomplete information in terms of decimating the indicators to the KPIs [36].
\nPeriod t | \n\n | \n | \n | \n | \n | \n |
---|---|---|---|---|---|---|
Performance indicator (ki) | \nQ1 | \nQ2 | \nQ3 | \nQ4 | \n… | \nQt | \n
k1 | \nx11 | \nx12 | \n… | \n… | \n… | \nx1t | \n
k2 | \n… | \nx22 | \n\n | \n | \n | … | \n
k3 | \n… | \n\n | \n | \n | \n | … | \n
k4 | \n… | \n\n | \n | \n | \n | … | \n
… | \n… | \n\n | \n | \n | \n | … | \n
ki | \nxi1 | \n… | \n… | \n… | \n… | \nxit | \n
Subjective questionnaire.
The GST could be the way out for problems of incomplete and therefore inadequate data. The challenge for PM is especially the collection of performance relevant data often derived from the answers to subjective questionnaires within organizations. From time to time, this requires a certain number of subjective data as shown in Table 1. Nevertheless, in practice, it may occur that experts cannot answer their quarterly surveys (e.g., vacation, illness or simple absence). Therefore, the GST is providing a buffer operator, which makes it possible to complete missing information in fragmented queries, without this leading to informational distortion or loss. If two adjacent entries of a data sequence are described by x(n – 1) and x(k), then, x(k – 1) represents an old information, and x(k) operates as a part of a newer information. If there is a gap between entries within a data sequence, a lack of information because of the insufficient completion of an expert’s questionnaire occurs (e.g., X = (x(1), x(2) x(3), x(5)). A new value x(4) can be created as follows:
\nThe value of α represents the weighting of the informational content with regard to its currency. If α > 0.5, the researcher attaches more importance to the newer information than to the older one and vice versa [23]. For simplification, no preference with respect to the timeliness of information should be assumed in the following, so that old and new information should be weighted equally (α = 0.5).
\nIn cases of a blank first entry x(1) or a missing last entry x(n) of a sequence X—for example, measured customer contentment—the gap cannot be filled by the method of adjacent neighbour generation, but rather by methods called stepwise ratio generator
The challenge of GRA is to clarify which factors influence the PM system in a desirable extent, to strengthen and to focus those subsequently. In the past, this has been discussed in scientific articles and essays about system theory. However, this methodology still attends rare attention in the context of Performance Management [23, 48–50]. This model was chosen, as it tries to work as an ideal PM support with its consideration of both financially and nonfinancially dimensioned factors by analysing the system’s factors that display sufficient influence on the top strategic financial ratio but appear as incomplete [51]. By means of the Performance Management as well as by the efficient and effective KPIs identified by the PM, the entire organization could be aligned to its strategy and vision [52]. Therefore, GRA attempts to discover the sequences of the KPIs by determining the geometrically most similar sequences to the top strategic financial performance ratio to uncover the system’s most descriptive factors [23]. Therefore, an organization has to determine a reference sequence, which optimally represents the strategy of the organization and thus the behaviour of the entire system [53]. The strategy and hence the ultimate performance generation should be illustrated by the KPIs. Here, Paquette and Kida [27] showed in their study that it is important to reduce the extension of the strategy to a minimum. So, in order to reflect the strategy by a reference sequence, it is advisable to refer to a single factor and not to a variety of multiple sequences. Kasperskaya and Tayles [34] propose that both types of indicators (financial and nonfinancial) within a well-functioning PM system should be used, but, however, the financial measures dominate in practice. Kaplan and Norton [52] also consider that a financial measure should be attributed the most weight in a strategy-focused organization, so that it can monitor and control their operational and strategic budgeting. Thus, a financial measure should also be used as a reference sequence in the selection of the strategy-related KPIs in a PM System.
\nThe GRA is a part of the GST mentioned earlier and is based on all of its assumptions and conditions [47]. In this context, a Grey relation proposes the valuation between two autonomous systems or two indicators within a system over a determined time series. It is precisely this point where the examination method GRA can be used. The elements are examined for homogeneous or heterogeneous temporal behaviour which means the development of the considered indicator in terms of time. If the elements display a very similar, homogeneous development concerning the time series, a high relational degree is assumed and vice versa. First, a reference sequence
The value
Then, the Grey relational degree
That leads to
Though, the Grey relational degree by Deng functions as a historical basis of the GRA in this case, it is not applied in the further process because of its dependence of the sequence order in the calculation
First, the absolute degree of incidence is considered. Assuming
illustrates a fluctuating image and therefore a development of the indicator behaviour [23]. The area under the curve can therefore be quantified as follows:
\nAs a result, the sequences can occur by decreasing (A), increasing (B) and vibrating (C) temporal behaviour. To be able to compare sequences with each other, a zero-starting point operator is applied [23]:
\nConsequently, the comparison of two sequences appears possible, so that also statements about the area beyond the curves can be made (Figure 2) [23].
\nRelationship between two sequences. (A = xi0 is located above xj0; B = xi0 is located underneath xj0; C = xi0 and xj0 alternate positions).
Now, the area si between
Here, however, rather the area between the two curves,
Assuming the length of both sequences is the same (otherwise the sequences could be adjusted, as described in Subchapter 3.1), then the absolute degree of incidence of the sequences Xi and Xj can be determined by [23]:
\nIn the PM, the challenge is to associate both financial and nonfinancial measures [34]. However, in case of this endeavour, problems of differently scaled indicators can emerge. Likert scale estimations by experts of employee satisfaction, for example, can be set in relation. But this would be no normalization as demanded by the concept of the absolute degree of incidence [56]. On the contrary to this, the concept of the relative degree of incidence provides a quantitative description of the rate of change of two sequences to their initial values, thus enabling a sufficient normalization. The closer these rates of changes of the two sequences are, the greater is the relative degree of incidence rij between them. Assuming that Xi and Xj are two sequences of an equal length with initial values that are different to zero, then there is no connection or linkage between the absolute and the relative degree of incidence, so that the absolute degree
Subsequently, the zero-starting point is determined analogously to Eq. (5), so there is the possibility to calculate the areas
Using these formulas, it is possible to calculate the respective relative degree of incidence between the variety of performance indicators and the reference sequence, to disclose for example the ten most “important” sequences/indicators for the reference sequence, the KPIs. To get an overview of the dependencies within those 10 KPIs, also the relative degrees of incidence between the KPIs can be calculated so that an interdependency network emerges [55]. Since there is only the possibility of building a network of interdependencies between the KPIs by GRA, the cause-and-effect-relationships lack a detailed explanation. This network, however, is likely to be understood as a construct of the holistic organizational strategy which is determined by “highly correlated” KPIs. If then the strategy changes or rather is adjusted to altered circumstances, the indicators act to the same extent, so that their cause-and-effect relationships are inconsiderable [58]. Nevertheless, the KPIs in their combination must be selected providing sufficiently the strategy and therefore its means and relations.
\nThe following example of a PM relevant application shall illustrate the possibility of simplifying the indicator selection in the PM with GRA in case of poor data situations. Therefore, the estimations of 50 performance indicators, the possible KPIs, by five organizational experts over four quarters serve as initial data for the example. For the reference sequence, to reflect the corporate strategy as simple as possible, the cash flows over the four quarters are used. The 50 performance indicators show a pre-selected pool of indicators elicited, for example by interviews [10]. They can range from employee satisfaction over customer contentment to process quality, for instance. Then, the experts are encouraged to estimate the respective extent of the indicator kit in the considered period with regard to the Saaty scale (with 1 = very weak extent to 9 = very strong extent) [59]. After the other four experts have analogously estimated, the respective indicators in each period, an aggregated group matrix is created by the mean value of the experts’ estimations (Table 2). The corresponding cash flows of the considered periods should fictitiously serve as a compliant financial target indicator of the corporation and thus as the reference sequence of the application example.
\nAccording to the equal length of all sequences, the values of Table 3 can be normalized in a certain way by Eq. (10) in order to make the differently scaled sequences comparable (Table 3).
\nThe indicators 1–50 do not require to consist of subjective data. For example, customer satisfaction, as a performance indicator, could be represented by an objective measure such as the amount of product returns, if existing. Subsequently, the sequences of Table 3 need to be moved to an initial value of zero with the zero-starting point operator of Eq. (6) (Table 4).
\nAggregated experts’ estimations | \nPeriod t | \nQ1 | \nQ2 | \nQ3 | \nQ4 | \n
---|---|---|---|---|---|
Reference sequence j: cash flow | \n1,000,000 | \n1,500,000 | \n1,750,000 | \n1,250,000 | \n|
Performance indicator (ki) | \n\n | \n | \n | \n | |
k1 | \n4.0000 | \n4.4000 | \n2.6000 | \n4.6000 | \n|
k2 | \n5.4000 | \n6.6000 | \n4.8000 | \n2.8000 | \n|
k3 | \n5.0000 | \n4.0000 | \n3.4000 | \n5.2000 | \n|
k4 | \n7.0000 | \n5.4000 | \n4.8000 | \n3.0000 | \n|
… | \n… | \n… | \n… | \n… | \n|
k50 | \n5.2000 | \n5.6000 | \n5.4000 | \n4.4000 | \n
Aggregated experts’ estimations.
Normalized aggregated estimations | \nPeriod t | \nQ1 | \nQ2 | \nQ3 | \nQ4 | \n
---|---|---|---|---|---|
Reference sequence j: cash flow | \n1.0000 | \n1.5000 | \n1.7500 | \n1.2500 | \n|
Performance indicator (ki) | \n\n | \n | \n | \n | |
k1 | \n1.0000 | \n1.1000 | \n0.6500 | \n1.1500 | \n|
k2 | \n1.0000 | \n1.2222 | \n0.8889 | \n0.5185 | \n|
k3 | \n1.0000 | \n0.8000 | \n0.6800 | \n1.0400 | \n|
k4 | \n1.0000 | \n0.7714 | \n0.6857 | \n0.4286 | \n|
… | \n… | \n… | \n… | \n… | \n|
k50 | \n1.0000 | \n1.0769 | \n1.0385 | \n0.8462 | \n
Normalized aggregated estimations.
Then, it is possible to calculate the area between the abscissa and the respective sequence
Thus, it is possible to provide a ranking of the geometrically most similar sequences with regard to the cash flow reference sequence (Table 5). In this example, the number of KPIs is limited to a count of ten as proposed by Markóczy and Goldberg as the optimal number to work with in PM [60].
\nGRA not only provides a ranking of the most important indicators of complex systems, it also offers the possibility to reveal the dependencies between the considered indicators by a network map. For this purpose, the relative degrees of incidence between the ten KPIs are determined by Eq. (13) (Table 6).
\nImages with zero-starting point | \nPeriod t | \nQ1 | \nQ2 | \nQ3 | \nQ4 | \n|si| | \n|
---|---|---|---|---|---|---|---|
Reference sequence j: cash flow | \n0.0000 | \n0.5000 | \n0.7500 | \n0.2500 | \n1.6250 | \n1.0000 | \n|
Performance indicator (ki) | \n\n | \n | \n | \n | \n | \n | |
k1 | \n0.0000 | \n0.1000 | \n−0.3500 | \n0.1500 | \n0.0250 | \n0.3846 | \n|
k2 | \n0.0000 | \n0.2222 | \n−0.1111 | \n−0.4815 | \n0.0611 | \n0.6207 | \n|
k3 | \n0.0000 | \n−0.2000 | \n−0.3200 | \n0.0400 | \n0.4600 | \n0.6969 | \n|
k4 | \n0.0000 | \n−0.2286 | \n−0.3143 | \n−0.5714 | \n1.4000 | \n0.5590 | \n|
… | \n… | \n… | \n… | \n… | \n… | \n\n | |
k50 | \n0.0000 | \n0.0769 | \n0.0385 | \n−0.1538 | \n0.1154 | \n0.4056 | \n
Images with zero-starting point.
Key performance indicator | \nRelative degree of incidence (rij) | \nRanking | \n
---|---|---|
k15 | \n0.9219 | \n1 | \n
k10 | \n0.9167 | \n2 | \n
k14 | \n0.9129 | \n3 | \n
k13 | \n0.8958 | \n4 | \n
k23 | \n0.8857 | \n5 | \n
k21 | \n0.8847 | \n6 | \n
k31 | \n0.8552 | \n7 | \n
k43 | \n0.7780 | \n8 | \n
k24 | \n0.7719 | \n9 | \n
k12 | \n0.7572 | \n10 | \n
Relative degrees of incidence of the performance indicators and their ranking.
KPI | \nk15 | \nk10 | \nk14 | \nk13 | \nk23 | \nk21 | \nk31 | \nk43 | \nk24 | \nk12 | \n
---|---|---|---|---|---|---|---|---|---|---|
k15 | \n1.0000 | \n0.4995 | \n0.5629 | \n0.9219 | \n0.8400 | \n0.9422 | \n0.7285 | \n0.9904 | \n0.5635 | \n0.5948 | \n
k10 | \n\n | 1.0000 | \n0.8161 | \n0.4793 | \n0.5520 | \n0.5153 | \n0.6138 | \n0.5020 | \n0.8148 | \n0.7572 | \n
k14 | \n\n | \n | 1.0000 | \n0.5373 | \n0.6305 | \n0.5830 | \n0.7123 | \n0.5660 | \n0.9980 | \n0.9129 | \n
k13 | \n\n | \n | \n | 1.0000 | \n0.7842 | \n0.8725 | \n0.6862 | \n0.9137 | \n0.5378 | \n0.5663 | \n
k23 | \n\n | \n | \n | \n | 1.0000 | \n0.8857 | \n0.8458 | \n0.8469 | \n0.6312 | \n0.6708 | \n
k21 | \n\n | \n | \n | \n | \n | 1.0000 | \n0.7626 | \n0.9508 | \n0.5837 | \n0.6174 | \n
k31 | \n\n | \n | \n | \n | \n | \n | 1.0000 | \n0.7337 | \n0.7133 | \n0.7642 | \n
k43 | \n\n | \n | \n | \n | \n | \n | \n | 1.0000 | \n0.5666 | \n0.5983 | \n
k24 | \n\n | \n | \n | \n | \n | \n | \n | \n | 1.0000 | \n0.9145 | \n
k12 | \n\n | \n | \n | \n | \n | \n | \n | \n | \n | 1.0000 | \n
Network of KPI dependencies.
Table 6 shows the relative degrees of incidence between the KPIs, which can be interpreted as reciprocal as these degrees can be understood as a kind of “Grey Correlation” [42]. However, similar to the DEMATEL approach, it is important to limit the dependencies to the really “essential” and “significant” ones. Therefore, the shaded fields are not considered subsequently so that only those dependencies which exceed the threshold, the average of the matrix (mean value = 0.74161862), should remain for further analytical procedure.
\nThe GST shows considerable advantages, particularly in a complex system as the PM. At the present time, it is indispensable to involve the dynamic environment in management control. For this purpose, it is necessary to continuously focus the corporate strategy and objectives in order to create a long-term financial success. The problems that especially occur as a consequence of incomplete information and small sample sizes can be a huge hurdle. The PM requires a permanent update which cannot be enabled by mere application of the existing statistical methods. The PM represents a highly dynamical system with ever-changing environmental conditions. This prohibits an appropriate data measurement with analysis by common statistical methods. Data alter before statistic samples can provide any analytic results. Therefore, it is important to seek methods with minimum data size demands. According to that, the GST with its applications can be useful with its low requirements in sample sizes. Specifically, GRA offers important advantages for the selection of KPIs in poor data situations with the additional possibility of a visual representation of the revealed KPIs within a network of interdependencies.
\nIn conclusion, GRA provides the feasibility to support the performance generation process and to assist PM as a tool-selecting performance indicators in case of incomplete information with small sample sizes. Besides, GRA is able to visualize the performance generation in a map that facilitates steering and control of the organization in the framework of Performance Management [35]. The ability to include financial and non-financial measures provides further advantages for GRA. So, it definitely appears suitable as an OR tool for management control, in particular in PM.
\nGRA as one of the submethods of the GST will help to improve the informational bases of PM by its possibilities of flexible usage. Therefore, GRA should serve as a feedback as well as a feedforward-oriented PM support. Initially, it provides intersubjectivated data for the performance management, which then disposes of improved informational bases for counteraction measures. After structural breaks of the system, in which PM is implemented, GRA is supposed to inform about such defects and should operate as a feedforward-oriented support for deriving, validating and implementing a new causal model.
\nThe rising number of OR-publications on GST issues demonstrates the enhancing importance of this theory for the analysis of complex systems. However, there are only a small number of articles in the PM literature referring to GST [49]. GST with its wide range of applications is nevertheless an appropriate OR method to support PM. Because of its relevance specifically in poor data situations with incomplete information, PM literature should increasingly focus GST as an important support instrument.
\nThe path of drug discovery from small molecule ligands to drugs that can be utilized clinically has been a long and arduous process. Starting with a hit compound, the drugs need to be evaluated through multiple in vitro and cell-based assays to improve the mechanism of actions followed by mouse models to demonstrate appropriate in vivo and transport properties. Mechanistically, the drugs not only need to exert enough binding affinity to the disease targets, but also necessitate proper transport through multiple physiological barriers to enable access to these targets. Other problems like chemical toxicity, often induced by off-targets interactions with unintended proteins as well as pharmacogenetic, where genetic variation influences drug responses all need to be considered in drug design. Therefore, these multifaceted problems in drug discovery often posed significant challenges for drug designers. Recently, the rise of artificial intelligence approach saw potential solutions to these challenges. A sub-umbrella of artificial intelligence called machine-learning has taken a central stage in many R&D sectors of pharmaceutical companies that allows drugs to be developed more efficiently and at the same time mitigate the cost associated with the required experiments [1]. Given some observations of chemical data, machine learning can be used to construct a predictor by learning compound properties from extracted features of compound structures and interactions. Because this approach does not require a mechanistic understanding of how drugs behave, many compound properties like binding affinity and other transport and toxicity problems can be accurately forecasted in this way before they are synthesized [2]. Furthermore, by simultaneously tackling the Pharmacokinetics/Pharmacodynamics (PK/PD) problems using artificial intelligence, we can expect that the effort and time required to bring a drug from bench to bedside can be substantially reduced. In this regard, the artificial intelligence approach has now become an essential tool to facilitate the drug discovery process.
To facilitate the discussion on artificial intelligence and machine learning in drug discovery and design, it is necessary to understand the type of format and data presentation commonly used for chemical compounds in chemoinformatics. Chemoinformatics is a broad field that studying the application of computers in storing, processing and analyzing chemical data. The field already has more than 30 years of development with focuses on subjects such as chemical representation, chemical descriptors analysis, library design, QSAR analysis and computer-aided drug design [3]. Along with these developments, several popular chemical data formats for data processing has been proposed. Intuitively, the chemical compound is best represented by graphs, also known as “chemical graph” or “molecular graph” where nodes represent atoms and edges represent bonds. The molecular graph is useful for distinguishing different structural isomers but does not contain 3D conformation of the molecules. To store 2D or 3D coordinates of compounds, chemical file formats such as Structure Data Format (SDF), MDL (Molfile), and Protein Data Bank (PDB) formats can be used. In contrast to the PDB file that simply store structural data, the SDF format provides additional advantages of recording descriptors and other chemical properties thus offers better functionality for cheminformatics analysis. Due to the limited memory capacity for handling large compound database, several chemical line notations have also been introduced. One such format is the simplified molecular-input line-entry system (SMILES) format pioneered by Weininger et al [4]. Other linear notations include Wiswesser line notation (WLN), ROSDAL, and SYBYL Line Notation (SLN). Instead of recording compound coordinates directly, the SMILES format store compound structure using simpler ASCII codes. While memory-efficient, there is no unique strings for representing chemical compound particularly for large and structurally complex molecules. To address this, canonical SMILES was proposed that applied the Morgan algorithm for consistent labeling and ordering of chemical structures [5]. Another limitation is the loss of coordinate information and necessitate structural generation programs like PRODRG to predict native molecular geometry [6]. Recently, the need to exchange chemical data over the world wide web (WWW) also saw the development of chemical markup language (CML) similar to the XML format. Despite the development of multiple chemical file formats, many commercial and open source packages have allowed convenient file format conversion using Obabel and RDKit softwares [7, 8].
The ability to represent chemical compounds by machine-learning features that fully captured wide ranges of chemical and physical properties of the target molecule has been an active area of research in chemoinformatics and chemical biology [9, 10]. These chemical features, also known as chemical descriptors, provide the ability to extract essential characteristic of the compound and offer the possibility of developing predictor that can classify novel structures with similar properties. Broadly speaking, the chemical descriptors can be classified as 0D, 1D, 2D, 3D, and 4D [11]. 0D and 1D descriptors like molecular mass, atom number counts can be easily extracted from the molecular formula but does not provide much discriminatory power for compound classification. In practice, 2D and 3D chemical descriptors are the most commonly used molecular features for cheminformatics analysis [12]. Since chemical compound can be viewed as different arrangements of atoms and chemical bond, 2D descriptors can be generated from the molecular graph based on different connectivity of the molecules. Notable 2D descriptors include Weiner index, Balaban index, Randic index and others [1]. Beyond 2D descriptors, 3D descriptors leverage information from molecular surfaces, volumes, and shapes to provide a higher level of chemical representation. The dependency of ligand conformations also prompts the development of 4D descriptors, which accounts for different conformations of the molecules generated over a trajectory from the molecular dynamics simulation [13]. However, the requirement of correct 3D conformation makes 3D and 4D descriptors limited in several aspects. Another type of high dimensional descriptors is molecular interaction field (MIF) developed by Goodford and colleagues [14]. The MIF aims to capture the molecular environment of the ligand based on several properties by placing probes in a rectangular grid surround the target compound. At each grid point, hypothetical probes corresponding to different types of energetic interactions (hydrophobic, electrostatic) were evaluated. The comparison of MIF of compounds enables the identification of critical functional groups for kinase drug-target interactions and drug design [15]. Furthermore, correlating these field values to compound activity enable comparative molecular field analysis (CoMFA), an extended form of 3D-QSAR [16]. Altman’s group at Stanford University took a different approach by inspecting ligand environment using amino acid microenvironment. This Feature-based approach lead to direct applications in pocket similarity comparison for identifying novel microtubule binding activity of several anti-estrogenic compounds as well as kinase off-target binding activity [17, 18]. Chemical descriptors can likewise be generated based on the biological phenotypes. For example, drug-induced cell cycle profile changes of compound have been recently utilized to identify DNA-targeting properties of several microtubule destabilizing agents [19].
Besides chemical descriptors, the chemical fingerprint is another important chemical representation where the compounds are represented by a binary vector indicating the presence or absence of chemical features [20]. Common 2D chemical fingerprints include path-based fingerprint which detected all possible linear paths consisting of bonds and atoms of a structure given certain bond lengths. For a given pattern, several bits in a bit string is set. While path-based fingerprints like ECFP (Extended Connectivity Fingerprint) have a higher specificity, the potential limitation is “bit collision” where the number of possible patterns exceeds the bit capacity resulting in multiple patterns mapped to the same set of bits. Another type of fingerprint is substructure fingerprints. In the substructure fingerprint like (Molecular ACCess System) MACCS keys, the substructures are predefined and each bit in a bit string is set for specific chemical patterns. Although bit collision is less of an issue, the requirement to encompass all fragment space within a bit string often demands a larger memory size. Recently, the proposal of circular fingerprints represents the state-of-the-art in chemical fingerprint development [21]. In the circular fingerprint, each layer’s feature is constructed by applying a fixed hash function to the concatenated features of the neighborhood in the previous layer and the results from the hashed function were mapped to bit string representing specific substructures. A modified version of the circular fingerprint, known as graph convolution fingerprint, has recently been proposed where the hashed function is replaced by a differential neural network and a local filter is applied to each atom and neighborhoods similar to that of a convolution neural network. Many of the mentioned fingerprints has been implemented by several open source chemoinformatics package such as Chemoinformatics Development Kit (CDK) and RDKit and saw wide applications in compound database search and other computer-aided drug discovery tasks [22].
The rise of artificial intelligence and, in particular, machine learning and deep learning has given rise to a tsunami of applications in drug discovery and design [23, 24]. Here, we provide an overview of machine learning concepts and techniques commonly applied for chemoinformatics analysis. In a nutshell, machine learning aims to build predictive models based on several features derived from the chemical data, many of which are measured experimentally, such as lipophilicity, water solubility while others are purely theoretical, such as chemical descriptors and molecular fields derived from the chemical graph or 3D structure data. With chemical features on one hand, on the other hand of the equation is the properties that the model intended to learn, which can take on categorical or continuous values and usually pertaining to compound activity in question. Given every pair of features and labels, the model can be trained by identifying an optimal set of parameters that minimizes certain objective functions. Following the training phase, the best model can then be applied to predict the properties of new compounds (Figure 1).
Chemoinformatics prediction using artificial intelligence. Starting with a compound, the chemical feature is extracted from the compound 2D graph. The chemical features then serve as input for the machine learning model and trained based on the compound activity. The trained model with fitted parameters can then be used to predict activity of new compounds.
Although machine learning has just recently gained in popularity, its application in chemistry is not new. The pioneering work of Alexander Crum-Brown and Thomas Fraser in elucidating the effects of different alkaloids on muscle paralysis results in the proposal of the first general equation for a structure–activity relationship, which intended to bridge biological activity as a function of chemical structure [25]. Early QSAR models such as Hansch analysis were mostly linear or quadratic model of physicochemical parameters that required extensive experimental measurement. This model was succeeded by the Free-Wilson model, which considers the parameters generated from the chemical structure and is more closely resemble the QSAR model in use today. Machine learning techniques in cheminformatics analysis can be broadly classified as supervised learning, unsupervised learning, and reinforcement learning. However, new learning algorithms through a combination of these approaches are continuing being developed. Many of these approaches have already found wide application in QSAR/QSPR prediction, de novo drug design, drug repurposing, and retrosynthetic planning [26, 27, 28].
Supervised learning has a long history of development in QSAR analysis [29]. The supervised learning task can include classification, to determine whether a compound class belong to a certain class label, or regression, to predict the bioactivity of a compound over a continuous range of values. A well-known supervised learning approach is the linear regression model, and often the first-line method for exploratory data analysis among statistician. The goal of linear regression is to find a linear function such that a fitted line that minimizes the distance to the outcome variables. When the logistic function is applied to the linear model, the model can also be applicable for binary classification. A direct extension of linear regression is polynomial regression that model relationships between independent and independent variable as high-degree polynomial of the same or different combination of chemical features. In the case of model underfitting, polynomial regression provides a useful alternative for feature augmentation for the linear model. Both linear and polynomial regression formed the basis of classical Hansch and Free-Wilson analysis [30]. Interestingly, today’s situation is completely reversed. With the rapid explosion of chemical descriptors and fingerprints available at chemoinformatician’s disposal, twin curse of dimensionality and collinearity has now become a significant issue.
Several approaches have been developed to tackle high dimensional data. One potential solution is to exhaustively explore all the possible combination of features to identify the best subset of predictors. However, this approach is inevitably computationally infeasible for large feature space. To solve this, heuristic approach like forward and backward feature selection were developed where each feature was added to the predictors in a stepwise manner and only features that contribute greatest to the fit are kept [31]. An alternative approach for feature selection is dimensional reduction where a smaller set of uncorrelated features can be created as a combination of a larger set of correlated variables. One commonly used dimensional reduction technique is principal component analysis (PCA) that identifies new variables with the largest variances in the dataset [32]. Recently, variable shrinkage method like regularization and evolutionary algorithm has allowed feature selection during the model fitting phase. In the model regularization step, a penalty term is introduced to the objective function to control model complexity. The lasso regularization is one such approach that used an L1 penalty term to constraint objective function along the parameter axis, thus enable effective elimination of redundant features [33]. The evolutionary algorithm is another feature selection approach that encodes features as genes and through successive combination, the algorithm identifies the best set of features measured by a fitness score. Recently, elastic net combines penalties of the lasso and ridge regression and shows promise in variable selection when the number of predictors (p) is much bigger than the number of observations (n) [34]. Although linear regression analysis formed the backbone of early QSAR analysis, the simple linear assumption of feature vector space is a major limitation for modeling more complex system.
The requirement to parameterize the QSAR model in a non-linear way saw the widespread application of artificial neural network (ANN) in the chemoinformatic analysis. The ANN, first developed by Bernard Widrow of Stanford University in the 1950s, is inspired by the architecture of a human brain, which consisting of multiple layers of interconnecting nodes analogous to biological neurons. The early neural network model is called “perceptron” that consists of a single layer of inputs and a single layer of output neurons connected by different weights and activation functions [35]. However, it was soon recognized that the one-layer perceptron cannot correctly solve the XOR logical relationship [36]. This limitation prompts the development of multi-layer perceptron, where additional hidden layers were introduced into the model and the weights were estimated using the backpropagation algorithm [37]. As a direct extension of ANN, several deep learning techniques like deep neural network (DNN) has been introduced to process high dimensional data as well as unstructured data for machine vision and natural language processing (NLP). In multiple studies, DNN outperformed several classical machine learning methods in predicting biological activity, solubility, ADMET properties and compound toxicity [38, 39].
To handle high-dimensional data, several feature extraction and dimension reduction mechanisms has been integrated into diverse deep learning frameworks (Figure 2). In particular, the convolution neural network is a popular deep learning framework for imaging analysis [40]. A convolution neural network consists of convolution layers, max-pooling layers, and fully connected multilayer perceptron. The purpose of the convolution and max-pooling layer is to extracted local recurring patterns from the image data to fit the input dimension of the fully connected layers. This utility has recently been extended for protein structure analysis in the 3D-CNN approach where protein structures are treated as 3D images [41]. Other deep learning approaches include autoencoder and embedding representation. Autoencoder (AE) is a data-driven approach to obtain a latent presentation of high dimensional data using a smaller set of hidden neurons [42, 43]. An autoencoder consists of encoder and decoder. In the encoding step, the input signal is forward propagated to smaller and smaller sets of hidden layers thus effective map the data to low dimensional space. The training is achieved so that the hidden layers can propagate back to a larger set of output nodes to recover the original signal. A specific form of AE called variational AE (VAE) has recently been applied to de-novo drug design application where latent space was first constructed from the ZINC database from which novel compounds can be recovered by sampling such subspace [44]. In the context of NLP, word embedding such as word2vec implementation is a dimensional reduction technique to learn word presentation that preserves the similarity between data in low-dimension. This formulation has been extended to identify chemical representation in the analogous mol2vec program [45]. The requirement to model sequential data also prompted the development of recurrent neural networks (RNN). The RNN is a variant of artificial neural network where the output from the previous state is used as input for the current state. Therefore, this formulation has a classical analogy to the hidden Markov model (HMM), a type of belief network. RNN has been applied for de novo molecule design by “memorizing” from SMILES string in sequential order and generated novel SMILES by sampling from the underlying probability distribution [46]. By tuning the sampling parameters, it is found that RNN can oftentimes generated valid SMILES string not found in the original training set.
Deep learning architectures for drug discovery. Four common types of deep learning network for supervised and supervised learning including deep neural network (DNN), convolutional neural network (CNN), autoencoder (AE) and recurrent neural network (RNN).
In contrast to parametrized learning that required extensive efforts in model tuning and parameter estimation, instance-based learning, also known as memory-based learning, is a different type of machine learning strategy that generates hypothesis from the training data directly [47]. Therefore, the model complexity is highly dependent on the size and quality of the dataset. Notable instance-based learning method includes the k-Nearest Neighbor (kNN) prediction, commonly known as “guilt-by-association” or “like-predicts-like”. In the kNN algorithm, a majority voting rule is applied to predict the properties of a given data, based on the k nearest neighbor within certain metric distance [48]. Using this approach, the properties of the data can be inferred from the dominant properties shared among its nearest neighbors. In the field cheminformatics, chemical similarity principle is a direct application of kNN where the similarity between chemical structures can be used to infer similar biological activity [49]. For analyzing large compound set, chemical similarity networks, or chemical space networks, can be used to identify chemical subtypes and estimate chemical diversity [50, 51]. Furthermore, the similarity concept is commonly applied in computational chemical database search to identify similar compounds from a lead series [52]. A major limitation of kNN is the correct determination of the number of nearest neighbors since that too high or low of such parameter can lead to either high false positive and false negative rates.
In the case of binary classification, such as compound activity discrimination, support vector machine (SVM) is a popular non-parametrized machine learning model [53]. For given binary data labels, SVM intended to find a hyperplane such that it has the largest distance (margin) to the nearest training data point of two classes. Furthermore, kernel trick allows mapping data points to high dimensional feature space that are linearly inseparable. For multilabel classification problems, other instance-learning models such as radial basis neural network (RBNN), decision trees and Bayesian learning are generally applicable [54]. In RBNN, several radial basis functions, which often depict as bell shape regions over the feature space, are used to approximate the distribution of the data set. Other approaches like decision tree, such as the Classification And Regression Tree (CART) algorithm, can also be applied for multi-variable classification and regression and has been used to differentiate active estrogen compound from inactives [55]. In the decision tree model, the algorithm provides explanations for the observed pattern by identifying predictors that maximize the homogeneity of the dataset through successive binary partitions (splits). The Bayesian classifier is yet another powerful supervised learning approach that predicts future events based on past observations known as prior. In essence, Bayes’ theorem allows the incorporation of prior probability distributions to generate posterior probabilities. In the case of multi-variable classification, a special form of Bayesian learner known as the naïve Bayes learner greatly simplify the computational complexity with independence assumption between features. PASS Online is an example of a Bayesian approach to predict over 4000 kinds of biological activity, including pharmacological effects, mechanisms of action, toxic and adverse effects [56]. In another study, DRABAL, a novel multiple label classification method that incorporates structure learning of a Bayesian network, was developed for processing more than 1.4 million interactions of over 400,000 compounds and analyze the existing relationships between five large HTS assays from the PubChem BioAssay Database [57].
While instance-based learning encompasses a diverse set of methodology and present unique advantages in constantly adapting to new data, this approach is nevertheless limited by the memory storage requirement and, as the dataset grows, data navigation becomes increasingly inefficient. To address this, data pre-segmentation technique such as KD tree is a common approach for instance reduction and memory complexity improvement [58]. In another aspect, the ability to assemble different classifiers into a meta-classifier that will potentially have superior generalization performance than individual classifier also led to the development of ensemble learning. The ensemble learning algorithm can include models that combine multiple types of classifier or sub-sample data from a single model. A notable example of ensemble learning is the random forest algorithm, which combines multiple decision trees and makes predictions via a majority voting rule for compound activity classification and QSAR modeling [59].
Given a compound dataset, unsupervised learning can include tasks such as detecting subpopulation to determine the number of chemotypes to estimate chemical diversity and chemical space visualization. Putting in a broader perspective, the purpose of unsupervised learning is to understand the underlying pattern of the datasets. Another important problem stem from unsupervised learning is the ability to define appropriate metrics that can be used to quantify the similarity of data distributed over feature space. These metrics can be useful for chemometrics application including measuring the similarity between pairs of compounds.
For unsupervised clustering, one popular approach is K-means clustering [60]. K-means clustering aims to partition the dataset into K-centroid. This is achieved by constantly minimizing the within-cluster distances and updating new centroids until the location of the K-centroids converges. K-means clustering has the advantage of operating at linear time but does not guarantee convergence to a global minimum. Another limitation is the requirement of a pre-determined number of clusters, which may not correspond to the optimal clusters for the data. To identify the optimal k values, one solution is called the “elbow method”, which determine a k value with the largest change in the sum of distances as the k value increases. One study applied K-means clustering to estimate the diversity of compounds that inhibit cytochrome 3A4 activity [61]. Besides K-mean clustering, conventional clustering like hierarchical clustering is also commonly used. Hierarchical clustering can include agglomerative clustering, which merges smaller data objects to form larger clusters or divisive clustering, which generate smaller clusters by splitting from a large cluster. The hierarchical clustering has been demonstrated for their ability to classify large compound and enrich ICE inhibitors from specific clusters as well as for virtual screening application [62, 63].
Although hierarchical clustering is suitable for initial exploratory analysis, it is limited by several shortcomings such as high space and time complexity and lack of robustness to noise. Supervised clustering using artificial networks include the self-organization map (SOM), also known as Kohonen network [64]. The purpose of SOM is to transform the input signal into a two-dimensional map (topological map) where input features that are similar to each other are mapped to similar regions of the map. The learning algorithm is achieved by competitive learning through a discriminant function that determines the closest (winning) neuron. During each training iteration, the winning neuron has its weight updated such that it moves closer to the corresponding input vector until the position of each neuron converges. The advantages of SOM are the ability to directly visualize the high-dimensional data on low dimensional grid. Furthermore, the neural network makes SOM more robust to the noisy data and reduces the time complexity to the linear range. SOMs cover such diverse fields of drug discovery as screening library design, scaffold-hopping, and repurposing [65].
Recently, manifold learning has gained tremendous traction due to the ability to perform dimensional reduction while preserving inter-point distances in lower dimension space for large-scale data visualization. Manifold learning algorithm includes ISOMAP, which build a sparse graph for high dimensional data and identify the shortest distance that best preserves the original distance matrix in low dimensional space [66]. While ISOMAP requires very few parameters, the approach is nevertheless computational expensive due to an expensive dense matrix eigen-reduction process. More efficient approaches such as Locally Linear Embedding (LLE) has been proposed for QSAR analysis [67]. LLE assumes that the high dimensional structure can be approximated by a linear structure that preserves the local relationship with neighbors. A related approach is t-distributed stochastic neighbor embedding (tSNE), which relies on the pair-wise probability distribution of data points to preserve local distance [68].
The ability to measure data similarity is as important as the ability to discern the number of categories from a dataset. One approach for measuring data similarity is by determining the distance of two data points in the high-dimensional feature space. Intuitively, the similarity between two data points is inversely related to the measured distance between them. Commonly used distance metrics include Euclidean distance, Manhattan distance, Chebyshev distance [60]. All of these metrics is a specialized form of Minkowski distance, a generalized distance metrics defined in the norm space. Other important similarity measures such as the cosine similarity and Pearson’s correlation coefficient, are commonly used to measure gene expression data or word embedding vector, when the magnitude of the vector is not essential. For binary features, metrics that measured shared bits between vectors can be used. For example, Tanimoto index, also known as the Jaccard coefficient, is one of the most commonly used metrics to measuring the similarity between two fingerprints in many cheminformatics applications. Tanimoto index has been extended to measure the similarity of 3D molecular volume and pharmacophore, such as those generated from the ligand structural alignment [69]. A generalized form of similarity metric is the kernel such as RBF or Gaussian kernel, which is a function that maps a pair of input vectors to high dimensional space and is an effective approach to tackle non-linearly separable case for discriminating analysis. The selection of an optimal similarity metrics can be achieved by clustering analysis, including comparing the clustering result and assess the quality of the clusters by different similarity measures.
Reinforcement Learning came into the spotlight from the famous chess competition between professional chess player and AlphaGo that demonstrated the ability of AI to outcompete human intelligence [70]. Differ from supervised and unsupervised learning, the reinforcement learning focused on optimization of rewards and the output is dependent on the sequence of input. A basic reinforcement learning is modeled based on the Markov decision process and consists of a set of environment and agent state, a set of actions and transitional probability between states. At each time step, the agent interacts with the environment with a chosen action and a given reward. Several learning strategies have been developed to guide the action in each state. The most well-known algorithm is called the Q-learning algorithm [71]. The Q-learning predicts an expected reward of an action in a given state and as the agent interacts with the environment, the Q value function becomes progressively better at approximate the value of an action in a given state. Another approach for guiding the action for reinforcement learning is called policy learning, which aims to create a map that suggests the best action for a given state. The policy can be constructed using a deep neural network. Recently, deep Q-network (DQN) has been constructed that approximate the Q value-functions using a deep neural network [72]. One recent example of using deep reinforcement learning in de novo design is demonstrated by the ReLeaSE (Reinforcement Learning for Structural Evolution), which integrates both predictive and generative model for targeted library design based on SMILES string. The generative model is used to generate chemically feasible compound while the predictive model is then used to forecast the desired properties. The ReLeaSE method can be used to design chemical libraries with a bias toward structural complexity or toward compounds with a specific range of physical properties as well as inhibitory activity against Janus protein kinase 2 [73].
The path of drug discovery from small molecule ligand to drug that can be utilized clinically is a long and arduous process. The fundamental concept of artificial intelligence and the application in drug design and discovery presented will facilitate this process. In particular, the machine learning and deep learning, which demonstrated great utility in many branches of computer-aided drug discovery like de novo drug design, QSAR analysis, chemical space visualization.
In this chapter, we presented the fundamental concept of artificial intelligence and their application in drug design and discovery. We first focused on chemoinformatics, a broad field that studying the application of computers in storing, processing, and analyzing chemical data. This field already has more than 30 years of development with focuses on subjects ranging from chemical representation, chemical descriptors analysis, library design, QSAR analysis, and retrosynthetic planning. We then discussed how artificial intelligence techniques can be leveraged for developing more effective chemoinformatics pipelines and presented with real-world case studies. From the algorithmic aspects, we mentioned three major class of machine learning algorithms including supervised learning, unsupervised learning, and reinforcement learning, each with their own strength and weakness as well as cover different areas of chemoinformatic applications.
As AI techniques gradually become indispensable tools for drug designer to solve their day-to-day problems, an emerging trend is to learn how to flexibly integrate these algorithms in the computational pipelines suitable for the problem at hand. For example, the process can start with an unsupervised learning to discerning the number of chemotypes followed by a supervised learning approach to predict multi-target activities. Furthermore, with the increasing computational power, deep learning network with increasing number layers and complexity will be also developed. Another potential development is the marriage between chemical big data and AI to mine the chemical “universe” for drug screening applications. The potential extensibility of AI in drug discovery and design is virtually boundless and awaits drug designer to further explore this exciting field.
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