\r\n\tFrom the definition of Massive MIMO, the Book covers the important aspects of channel estimation, different efficiency parameters, and various practical deployment considerations. From the beginning, a very general, yet tractable, canonical system model with spatial channel correlation is required. This model is used to realistically assess the Spectral Efficiency and Energy Efficiency and is later extended to also include the impact of hardware impairments.
\r\n\r\n\tAs an overall framework, the authors and researchers who are working in the Area of Massive MIMO and 5G are expected to submit chapters covering these areas to give insight into research about MIMO.
",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:"f6e96802bc79d6b8b0bab9ad24980cbc",bookSignature:"Dr. Sudhakar Radhakrishnan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7638.jpg",keywords:"Multi Antenna Systems, Diversity, Space-time Codes, Rake Receiver, MIMO Wireless Communication, SVD, Equalising MIMO Systems, Predistortion, Beam Forming Principles, Increased Spectrum Efficiency, Interference Cancellation, Beam Former",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 18th 2019",dateEndSecondStepPublish:"March 6th 2020",dateEndThirdStepPublish:"May 5th 2020",dateEndFourthStepPublish:"July 24th 2020",dateEndFifthStepPublish:"September 22nd 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:"26327",title:"Dr.",name:"Sudhakar",middleName:null,surname:"Radhakrishnan",slug:"sudhakar-radhakrishnan",fullName:"Sudhakar Radhakrishnan",profilePictureURL:"https://mts.intechopen.com/storage/users/26327/images/system/26327.jpg",biography:"Dr. Sudhakar Radhakrishnan is a researcher in the field of Image processing and contributed a lot to the society. He is an editorial board member for 3 International journals namely International Journal of Computer Theory and Engineering, International Journal of Computer and Electrical Engineering and International Arab Journal of Information technology. He is currently an Associate editor of IEEE-Access a multidisciplinary Journal published by IEEE. He is a reviewer of 16 international journals namely IEEE Transactions on Systems, Man, and Cybernetics: Systems by IEEE, International Arab Journal of Information Technology coming from Zarqa University, International Journal of Computer and Electrical Engineering published by International Association of Computer Science and Information Technology Press (IACSIT),International Journal of Computer Theory and Engineering published by International Association of Computer Science and Information Technology Press (IACSIT), Journal of Electrical and Electronics Engineering Research, Iranian Journal of Electrical and Computer Engineering, Journal of Optical Engineering, Journal of Electronic Imaging, Imaging Science Journal, International Journal of Computational Science and Engineering (IJCSE), International journal of Image mining(IJIM), Int. J. of Biomedical Engineering and Technology (IJBET) , Journal for Image Analysis & Stereology from International Society for Stereology, ETRI journal from Korea,\tAEUE- International journal of Electronics and Communications and IET Image Processing. He wrote 2 books titled 'Research issues in Image compression using Wavelet variants” .'Practicing Signals and Systems Laboratory using MATLAB” and two Book chapters titled 'Wavelet based image compression” in book titled 'Computational Intelligence Techniques in Handling Image Processing and Pattern Recognition” 'Analysis of Hand Vein image s using hybrid techniques” in Hybrid Intelligence techniques for Image Analysis and Understanding”. He edited three books titled 'Effective video coding for Multimedia applications”, 'Applications of Digital Signal Processing through practical approach” 'Recent Advances in Image and Video Coding” Wavelet theory and its applications all published by IntechOpen.He has published 100 papers in international, national journals and conference proceedings. His areas of research include Digital Image Processing, Image Analysis, Wavelet Transforms, Communication Engineering, and Digital Signal Processing",institutionString:"Dr. Mahalingam College of Engineering and Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"5",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"280415",firstName:"Josip",lastName:"Knapic",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/280415/images/8050_n.jpg",email:"josip@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copy-editing and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"112",title:"Effective Video Coding for Multimedia Applications",subtitle:null,isOpenForSubmission:!1,hash:"09a9826a6f8e7d58cf8516c609b4fa05",slug:"effective-video-coding-for-multimedia-applications",bookSignature:"Sudhakar Radhakrishnan",coverURL:"https://cdn.intechopen.com/books/images_new/112.jpg",editedByType:"Edited by",editors:[{id:"26327",title:"Dr.",name:"Sudhakar",surname:"Radhakrishnan",slug:"sudhakar-radhakrishnan",fullName:"Sudhakar Radhakrishnan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5364",title:"Recent Advances in Image and Video 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by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"64812",title:"Geothermal Explorations on the Slate Formation of Taiwan",doi:"10.5772/intechopen.81157",slug:"geothermal-explorations-on-the-slate-formation-of-taiwan",body:'\nEnergy is the essential lifeblood of today’s national economy. Diversification of energy sources is reasonable and important in establishing policy for all countries in the world. Among various energy sources, geothermal energy offers a naturally free resource and less dependence on fossil fuels. Taiwan is a country in which the energy is very scant, and almost over 99% consumed ones, most of the fossil fuels, are imports from outside of this country [1]. Taipower, the only commercialized power company in Taiwan, has installed capacity of 40.79 GWe, which produces the gross electricity of about 219.2 billion kWh [2]. They are produced by fossil fuels including oils, coals, and natural gases, nuclear power, hydropower and renewable energy, etc. Among them, the majority is the fossil fuels (72.78%) followed by nuclear power (18.61%), while the renewable energy only occupies 2.86%. There are four nuclear power plants in Taiwan, namely, No. 1, No. 2, No. 3, and No. 4, three of which are in operation and the fourth is under construction, but the latter is sealed now due to it being considered as non-safe after the 2011 Fukushima nuclear disaster in Japan and because of the nuclear-free policy. Moreover, those three operating ones are old, more than 30 years old, and will be decommissioned from 2018 to 2025. The electricity will be less than 18.61%, about 40.79 billion kWh per year, and there is a need to find alternatives for them in the next 10 years in Taiwan. It, thus, provides an opportunity for developing geothermal energy.
\nTaiwan is located at the ring of fire and is famous for the young orogenic belt (Figure 1), which has wide distributions of rapid uplifting terranes with few active volcanoes [3, 4]. Three geothermal play types have been distinguished in terms of geological controls [5, 6]. They are the magmatic-volcanic field type, the extensional domain type, and the orogenic belt/foreland basin type, which are correlated to the Tatun volcano group, the Ilan Plain, and the Central Mountain Range, respectively (Figure 1). Lot of geothermal explorations have been done in this island since the 1960s, and two pilot geothermal power plants had been built up in slate formation in northeast Taiwan [7]. In 2005, the Bureau of Energy of Taiwan revisited the slate formation for geothermal exploration and developed a plan for future power generation in the Chingshui geothermal field. The Ministry of Science and Technology (MOST) (formerly known as the National Science Council, NSC) of Taiwan initiated and promoted the geothermal explorations and developments as a major national energy project (NEP I and II) since 2008. The works include more precise geological, geochemical, and geophysical surveys with drillings [8]. Currently, over 90% of the on-working geothermal power plants are operated in the volcanic- or magmatic intrusion-related systems [9]. There are only few cases of metamorphic terrane, especially on the slate formation. The aims, thus, of this article are to introduce the techniques or methods we used for geothermal exploration in the slate formation of the Chingshui geothermal field of Taiwan.
\nThe tectonic framework of Taiwan.
Taiwan belongs to the ring of fire and is famous for active orogeny (Figure 1). Presently, the Philippine Sea plate is moving toward WNW at about 70 mm/yr. [10], and it is believed that the mountain-building process is still ongoing [11, 12]. A dominant collision zone frequently inducing folding and fault thrusting in the area may exist in central Taiwan and cause rapid uplifting rate being 6 mm per year [13]. Tectonically, the Philippine Sea plate is riding up over the continental shelf of the South China Sea in southern Taiwan. Moreover, an oceanic part of the Eurasian plate is subducting beneath the Philippine Sea plate along the Manila trench, which results in the bulldozing of shelf sediments both upward and westward. This island of Taiwan has been created in the last 5 million years [3, 14, 15], and rapid crystal movements and widely distributed active structures make up the geological characteristics of this young tectonic entity [16, 17, 18, 19]. In the north, the Philippine Sea plate subducts underneath the Eurasian plate, leading to the formation of Ryukyu arc (Figure 1). The Okinawa Trough is a back-arc basin, which extends from southwest Kyushu Island (Japan) to the Ilan Plain, which is the southwest-most tip of it. Three stages of opening have been identified since 15 Ma, and the latest phase of extension occurred in the southwestern part of the Okinawa Trough, which is characterized by normal faults with vertical offsets since the late Pleistocene [20, 21, 22]. The age of the normal stress affecting the Ilan Plain may be in the latest Pleistocene; based on the thermoluminescence (TL), age of 7.0 ± 0.7 ka obtained from a siltstone xenolith was found at Kueishantao, an offshore volcanic island 10 km away from the coast of Ilan Plain [23]. Several active volcanoes have been identified in inland and offshore of this island, and high uplift mountain range occurs in the eastern and central parts of Taiwan. Those tectonic settings provide a very good environment rich in geothermal energy.
\nThe slate formation, named the Lushan Formation, is widely distributed in the Backbone Range belt of Central Range, Taiwan (Figure 1). It is composed of argillite and slate of early to middle Miocene. The type locality of this formation is located in the Lushan area, east of Wushe in Nantou County. Miocene foraminifers were found in slates and marly nodules in this formation, which extend for an east–west width of at least 14 km on the western slope of Central Range [24, 25]. The Lushan Formation consists largely of black to dark-gray argillite, slate, and phyllite with occasional interlayer of gray sandstones. The estimated thickness is several thousand meters. This formation is exposed in north Taiwan at the mouth of the Lanyang River in the Ilan County and extends southward along the crest zone of the Central Range through Hohuashan, Nengkaoshan, to Hsiukuluanshan for a length of approximately 150 km and a width of several to 10 or more kilometers [26]. South of the Yushan Mountain, the Lushan Formation occurred east of the Laonong River southward to the eastern mountains of the Pingtung valley down to the Hengchun Peninsula. It also crops out in southeastern Taiwan in the area of Chihpen and Tawu [26].
\nThe Chingshui geothermal field is located in the valley of Chingshui stream, southwest of Ilan Plain, northeast Taiwan (Figure 2). The rock hosting the geothermal field is the Miocene Lushan Formation, consisting dominantly of argillite/slate with intercalated thin meta-sandstones [27, 28]. Several hot springs with minor hydrothermal alterations are the important thermal manifestations, which they crop out along the riverbed and rock cliffs in this area (Figure 3A). Temperatures and TDS of them range from 34°C to over 95°C and from 896 to 1500 ppm, respectively. Hydrothermal minerals include calcite, aragonite, dolomite, strontianite, amorphous silica, burbankite, kaolinite, sulfur, jarosite, tschermigite, and gypsum.
\nThe Chingshui geothermal field is located in the valley of Chingshui River, which is in the southwestern Ilan Plain.
(A) Distribution of surface temperature on rocks along the Chingshui River. (B) Thermal (left) and optical (right) images of hot springs along the riverbed. (C) Thermal (left) and optical (right) images of hot springs and fumaroles along the rock outcrops.
These field investigations and subsurface geological reports indicate that the Lushan Formation in this area is composed predominantly of dark-gray and black slates with thinly layered meta-sandstones, which can be divided into three members. They are the Jentse member, the Chingshuihu member, and the Kulu member. The upper Jentse member consisting of mainly light meta-sandstone intercalated in dark-gray slates, while the lower Chingshuihu and Kulu members consist mostly of slates [27, 28]. Two regional folds according to the vergence from minor folds in the thin-bedded sandstone have been identified in the Chingshui geothermal field [29]. The synclinorium axis is located within the Jentse member across the upstream region of the Chingshuichi River. The anticlinorium axis is located across the downstream region of Chingshuichi in the Chingshuihu member. Both fold axes were offset by the Chingshuichi Fault.
\nSeveral faults, including the Xiaonanao and Chingshuichi faults and a few small unnamed ones, cut through rock bodies in Chingshui area (Figure 3A). The Xiaonanao Fault is a thrust fault with wide damage zones that are rich in quartz veins on the outcrops along the Chilukeng River (Figure 4). The fault stretches to the east where an upside-down sequence of strata appears at the Shimen River [28]. The Chungshuishi Fault is a south–north strike-slip one inferred from geophysical data [30, 31]. The geothermal field, therefore, cropped out follows the surface trace of the suspected Chingshuichi Fault along the Chingshuichi Valley [27, 30, 32] (Figure 3A). Those normal faults, therefore, in the area were formed during the Pengli orogeny in the late Pliocene [33]. However, some outcrops of other faults cannot be found on the surface after heavy weathering and erosion in the area.
\nThe Xiaonanao Fault is a thrust fault with wide damage zones that are rich in quartz veins with euhedral pyramidal crystals on the outcrops along the Chilukeng River.
There are many parallel normal faults with strike-slip component gouges (inferred by slickenside direction) approximately 200 m long along the confluence of the Chingshui River and the Chilukeng River [34] (the star mark at Figures 3A and 5A). The fault gouge strikes ranging from N55°E to N75°E and dips from 30°N to 80°N. The mineral assemblage for these white veins is predominantly composed of calcite with minor quartz (Figure 5B).
\n(A) A normal fault (white line), located at the confluence of Chingshui River and Chilukeng River (star mark at Figure 3A), is approximately 2 m wide with a steep dip and contains blocks of quartz veins. (B) The vein in the damage zone, cut by steep normal fault, is predominantly composed of calcite with minor quartz.
Several geophysical exploration methods have been applied to the Chingshui geothermal field. They were geomagnetic, gravity, and resistivity surveys including transient electromagnetic (TEM), magnetotelluric (MT), and microseismicity.
\nMagnetic surveys record the spatial variation in the Earth’s magnetic field, which the magnetic properties of rocks are measured. Generally, magnetic susceptibility is summarized as the various amounts of minor accessory minerals in all rocks, which contain iron-rich phases such as magnetite, pyrrhotite, and hematite. Magnetite is the most important magnetic mineral, because it is not only very common but also has relatively high magnetic susceptibility. A geomagnetic survey measures the changes in the amounts of magnetic minerals as well as associated rock types. A magnetic map, thus, helps locate mineral deposits by identifying specific rock types and geological features [35].
\nA total of 425 stations for geomagnetic survey in the Ilan Plain were performed in 1978 by Yu and Tsai [31]. They found an obvious WE high magnetic anomaly which is located between Ilan and Luodong, and they interpreted that this anomaly could be the magma intrusion as dikes underneath the Ilan area. Meanwhile, a low-magnetic zone between Chingshui and Hanhsi was observed by Tong et al. [30], which reprocessed the old data and elucidated that it might be associated with the destruction of magnetite in the host rocks by hydrothermal alteration. The fluid for hydrothermal alteration in the Chingshui area was probably related to the magmatic source also supported by oxygen and carbon isotopes [34].
\nThe purpose of gravity survey is to detect mass materials underneath the places, which are not uniformly the same everywhere. They vary with the distributions of the dense materials below. A gravity survey is an indirect method to get the density property of subsurface materials. The higher the gravity values, the denser the rock, such as igneous body beneath.
\nA gravity survey with a total of 636 stations in the Chingshui geothermal field was completed by the ITRI in 1976 [36]. Although the variation in gravity is not significant due to the fact that the rock formation in survey area is predominantly composed of slates, the gravity on both sides of the Chingshui stream is apparently different [30], which may be related to fault cut through along the river. Euler deconvolution is a useful tool to interpret the gravity data rapidly and delineate structural contacts and depth estimation quickly [37, 38]. Based on the results of Euler deconvolution and known adjacent geological structures, several faults have been identified. They are the Xiaonanao, Chingshuihsi, and Kulu faults, which correspond with known faults introduced by Tseng [28] and Lin and Yang [33]. Meanwhile, three unknown faults, namely, the A-fault, B-fault, and C-fault, have also been recognized, which might be associated with the Niutou Fault with a SW–NE trend. The Chingshuihsi Fault was offset by the Xiaonanao Fault and the C-fault in the south and north of Chingshui, respectively, and the known geothermal field in this region is bounded by the C-fault and the Xiaonanao Fault [30, 33].
\nSurface resistivity survey measures the electrical potentials in the ground around a current-carrying electrode depending on the electrical resistivity and distribution of the surrounding sediments and rocks. It applies an electrical current between two electrodes implanted in the ground to measure the difference of potential between two additional electrodes that do not carry current. The distribution of potential can be related theoretically to ground resistivity and their distributions for rock bodies, which are distributed in a horizontally stratified ground and the homogeneous masses separated by vertical planes [39]. Mineral grains in sediments and rocks are essentially nonconductive; except in some ores or metallic minerals, the resistivity of sediments and rocks is governed primarily by the amount of pore water, its resistivity, and the orientations of the pores. The differences of lithology have different resistivity, so the surveys can be used to detect bodies of anomalous materials or in estimating the depths of bedrocks [39]. Generally, since the resistivity of rocks is controlled primarily by the pore water conditions in a rock, the values cannot be directly interpreted in terms of lithology. However, zones of distinctive resistivity are associated with specific rock units on the basis of local field or drill hole information, and the surveys were used profitably to extend field investigations into areas with very limited or nonexistent data. Meanwhile, the resistivity surveys may be used as a reconnaissance method, to detect anomalies that can be further investigated by complementary geophysical methods and/or drill holes [39].
\nThe ITRI conducted surface resistivity surveys, including the transient electromagnetic (TEM, the collinear and orthogonal dipole–dipole measurements) and magnetotelluric (MT) in the Chingshui area since the 1970s [30, 32, 40, 41]. They deployed both the transmitter and receiver dipole lengths at different distances, that is, 300–1300 m, separately for TEM. Four groups of apparent resistivity data sets were collected with four individual transmitter locations and were reprocessed and inverted with modern 2D and 3D inversion codes later [42, 43]. The inverted bipole-bipole results show the regional geological structures of the Chingshui area and reveal three vertical conductive structures, H, I, and J. They may correspond to the vertical Chingshuichi Fault, the stratigraphic boundary between the Chingshuihu member and Jentse member, and coincide to the Dachi Fault [28] and the Xiaonanauo Fault, respectively [43]. Hot springs crops out along the Chingshuichi Fault, suggesting that it might be the conduit to provide geothermal fluids.
\nThe magnetotelluric (MT) survey is a method to use frequently and successfully for exploring geothermal reservoirs [44, 45, 46]. Many reports claim that geothermal fluid circulates along the fractures within the meta-sandstones or slates in the Chingshui geothermal field [27, 28]. To detect the geological structures and reservoir underneath the Chingshui area, 33 broadband magnetotelluric data points were acquired by the ITRI in June 2006 [30]. Those data were processed and inversed for 1D and 2D images [30]. Moreover, the MT time series data were measured over 72 h at all stations to improve the data quality in 2014 and were processed using statistically robust algorithms from Jones et al. [47] for the MT 3D inversion [48].
\nThe MT results could be achieved based on the understanding of the resistivity of various types of rocks in the survey area (Figure 6). A significant low-resistivity zone can be identified, which could be related to a clay-rich cap rock in the geothermal structure. The cap rock is about 1 km in width and is found at depths ranging between 200 and 1000 m [30]. The resistivity of the cap rock is about 14 Ωm [30], which is not as low as many other volcanic geothermal fields that have a low-resistivity cap layer of less than 5 Ωm. It is characterized by the Chingshui geothermal field having slate host rocks that are expected to be less reactive than the volcanic rocks. The cap rocks could be illite-rich slates, which got approved by recent drilling results [8].
\nTwo-reservoir model with MT images and many microseismicity in the Chingshui geothermal field.
The reservoir of Chingshui geothermal field is a typical fault-controlled fractures, which may be created by the several faults distributed in this area [30]. MT images show that the fractures associated with the geothermal reservoir are distributed from near surface to a depth of 1500 m toward the south in fault zones, which is similar to the identified production zone from the core drilling records. The size of the Chingshui geothermal reservoir is estimated to be about 9.54 × 107 m3 and contains about 10 million cubic meters of geothermal fluids, based on the 3D model with a gross porosity of 0.1 and 100% saturation for the fracture zones [42]. Meanwhile, two geothermal reservoirs have been proposed underneath the Chingshui geothermal field according to MT images (Figure 6) [8, 34, 48]. One is shallower at a depth of less than 3000 m, while the other is deeper at depths ranging from 4000 to 8000 m. Moreover, abundant microseismicity occurred at the top of the deep reservoir [49]. This result leads us to infer that the deep reservoir may be a high-temperature hydrothermal system with frequent hydraulic fracturing occurring that induces microseismicity.
\nGeothermal exploration provides abundant information for the location, nature, and origin of the geothermal waters in a geothermal system. Geochemical studies of geothermal fluids involve three main steps: (1) sample collection, (2) chemical analysis, and (3) data interpretation [50]. The results give the parameters that are sensitive to subsurface temperatures, salinity of the fluids, and gaseous chemical compositions in interesting areas. Meanwhile, the constituents of fluid chemistry are used to trace the origin and flow of geothermal waters, especially the stable isotopes, such as 2H and 18O, along with B and Cl being most important. Chemical constituents of rocks, for example, SiO2, Na, K, Ca, Mg, and CO2, are useful for estimating subsurface temperatures and potential production problems such as scaling and corrosion [50].
\nSeveral hot springs cropped out on the surface, and 21 exploring and production wells have been drilled in the Chingshui geothermal field (Figure 3A). The temperatures and pH of springs and wells range from 48 to 99°C and 6.4 to 9.7 and from 180 to 230°C and 6.3 to 8.9, respectively [7]. The major gases of geothermal steam being about 20% in the Chingshui area are CO2, H2S, and others. Carbon dioxide is generally the major gas component often comprising more than 97% of all non-condensable gases, and its concentration increases with reservoir temperature. Hydrogen sulfide concentration is about 1% and commonly decreases as steam ascends to the surface due to reaction with wall rock, dissociation to sulfur, or oxidation to SO4 [7].
\nThe chemical and isotopic compositions of hot spring and wells are shown in Figures 7 and 8. Chemical compositions of cations and anions in the Chingshui geothermal fluids are pretty variable. Bicarbonate ([HCO3−] = 500–3200 ppm) is the major anion in most geothermal waters, being with lower chloride ([Cl−] = 6.5–23.4 ppm) and sulfate ([SO42−] = 29–72 ppm), which are the bicarbonate fluid type based on Piper diagram (Figure 7) [51]. Sodium ([Na+] = 35–1235 ppm) is the major cation in most geothermal fluids, being with lower calcium and magnesium (both few ppm). Based on the Na-K-Ca geothermometer, the temperatures of thermal fluids in reservoirs range from 137 to 205°C. Silica (SiO2) compositions of thermal fluids range from 83 to 413 ppm, which are correlated to the temperature from 127 to 214°C, respectively, by silica geothermometry [7]. However, the SiO2 geothermometer is a more reasonable and suitable method than Na-K-Ca one for assessing reservoir fluid temperatures in the slate region in Taiwan in terms of experimental fluid–rock interactions on laboratory [52, 53].
\nThe water compositions of Chingshui area are plotted on the Piper diagram showing the typical Na-bicarbonate fluid type.
Plots of H- and O- isotopic compositions of thermal water on the local meteoric water line (MWL) show the close relationship with the meteoric water.
Hydrogen and oxygen isotopic compositions for meteoric water along LangyongSi River (the mainstream of Chingshui River) were −70~ + 10‰ and −11 to 0‰, which may be due to rapid topographic change and strong monsoon effect in winter [54] (Figure 8). For the hot springs and thermal water, the δD and δ18O values are −67~−32‰ and −9.2 to −4.4‰ and −57~−24‰ and −6.7 to −4.0‰, respectively. The wide ranges of isotopic compositions in hot springs may be partly attributed to wider geographic distributions of them and mixing of thermal water with meteoric water [54]. Plots of H− and O− isotopic compositions of thermal water on the local meteoric water line (MWL) show the close relationship with the meteoric water (Figure 8). Isotopic changes of geothermal water due to fluid–rock interaction were small with a maximum δ18O shift of about 3‰ from the MWL. This small shift may reflect the slow fluid–rock interaction in terms of low permeability of the slate host rocks [54].
\nThe hot fluids in Chingshui geothermal field are characteristic of high concentration of HCO3−. When the geothermal reservoir is breached by tectonic activities or drilling for geothermal exploitation, CO2 is oversaturated and can be released quickly by depressurization causing the bicarbonate solution to oversaturate rapidly with pH increase and to precipitate carbonate minerals from thermal water immediately. The isotopic data from fracture-filling carbonate minerals have been found to be particularly useful to constrain the geochemical characteristics of fluid reservoirs and possible post-depositional and syntectonic fluid processes [55, 56, 57, 58, 59].
\nTwo populations of 18O values were recognized, −5.8 ± 0.8‰ VSMOW from scaling in the wells and −1.0 ± 1.6‰ to 10.0 ± 1.3‰ VSMOW from the calcite veins of outcrops (Figure 9), which are indicative of meteoric and magmatic fluid sources, respectively [34]. Meanwhile, two hydrothermal reservoirs at different depths have been identified by magnetotelluric (MT) imaging with microseismicity underneath this area [48, 49] (Figure 6). Two-reservoir model has been proposed: One is the shallow reservoir with fluids from meteoric water to provide the thermal water for scaling depositions inside the production wells, while the deep one supplies magmatic fluids mixing with deep marble decarbonization to precipitate the calcite veins near fault zones [34]. Helium isotope data from Cheng [60] also provided strong evidence of magmatic fluids from the deeper reservoir in the Chingshui geothermal field. The ratios of 3He/4He were 3.8–4.0 RA and 0.8 RA for the samples. These lines of evidence indicated the existence of a mantle-derived component in the Chingshui area, which may be derived from magmatic degassing; however, the lower helium isotope ratio of the other sample also implied a mixing between such a deeper, magmatic-related reservoir and a shallower, crustal-related one [34, 60].
\nPlots of carbon and oxygen isotope values of calcite veins and scaling from outcrops, IC-21, and wells in the Chingshui geothermal field.
The well IC-21 commenced drilling at May 2010. Upper 600 m was drilled into the hole and did not take any cores, just a cutting per 10 m. Whole coring raised 200 m in length between 600 and 800 m in depth and got over 95% core recovery. Lithologically, the 200 m cores are predominantly composed of dark-gray to black slates occasionally intercalated with argillites or meta-sandstones. There are many deformation structures, fractured systems, and veins in the cores (Figure 10). It is characteristic that many scaling minerals are irregularly filled up in the fractures, veins, and open cracks.
\nPhotos of veins in the core of well IC-21.
Three types of calcite crystal morphologies have been identified in the veins of the cores: bladed, rhombic, and massive crystals (Figure 11). Bladed calcites are generated via degassing under boiling conditions with a precipitation temperature of ~165°C and calculated δ18O value of −6.8‰ to −10.2‰ VSMOW for the thermal water [61]. Rhombic calcites grow in low-concentration Ca2+ and CO32− [62, 63, 64] meteoric fluids and precipitate at approximately ~180°C [61]. Finally, massive calcites coprecipitated with quartz in the mixing zone of meteoric water and magmatic or metamorphic fluids with calculated δ18O value of up to 1.5 ± 0.7‰ VSMOW. Furthermore, the scaling and hot fluids at a nearby pilot geothermal power plant confirm a meteoric origin. It indicates the current orientations of the main conduits for geothermal fluids are oriented at N10°E with a dip of 70°E [61].
\nPhotographs of the calcite morphologies observed in the veins of IC-21 cores: massive (yellow arrow), rhombic (red arrow), and bladed calcites (blue arrow) in fractures. The diameter of 1 dollar coin is 2 cm for scales.
The Chingshui geothermal field, a moderate-temperature and water-dominated hydrothermal system, was the site of the first geothermal power plant in Taiwan. This article introduces the exploration results of a geothermal reservoir located in the slate formation of Taiwan using geological, geophysical, and geochemical methods. The hot springs, gas fumaroles with sulfur precipitation, hydrothermal alteration, and high surface temperature on rock body are the thermal manifestations in the Chingshui geothermal field. All of these manifestations are predominantly occurring in very narrow belt about 20–30 m wide along the Chingshui River, which are controlled by the fractures of faults. Therefore, surface geological works provide abundant information on rock formations and fracture patterns to infer where and how deep the geothermal reservoir and probably current conduct circulation system are. Furthermore, to construct a model for understanding the subsurface rock bodies and geological structures.
\nGeophysical surveys including the geomagnetic, gravity, resistivity measurements (transient electromagnetic [TEM] and magnetotelluric [MT]), and microseismicity have been applied to detect the subsurface geological structures. A model with two geothermal reservoirs has been proposed underneath the Chingshui geothermal field. One is shallower at a depth of less than 3000 m, while the other is deeper at depths ranging from 4000 to 8000 m. Moreover, abundant microseismicity distributed at the top of the deep reservoir to infer a high-temperature hydrothermal system with frequent hydraulic fracturing occurred that induces microseismicity. However, the resolutions of geophysical surveys are not so high to precisely draw the whole picture of reservoirs and to pinpoint where the fault fractures as conduits for thermal fluid circulation due to the narrow of fault zones in the slate formation.
\nChemical constituents of the Chingshui geothermal water are rich in bicarbonate and sodium in anion and cation, suggesting that it is the HCO3−-Na+ fluid type based on Piper diagram. Based on the Na-K-Ca and silica geothermometers, the temperatures of thermal fluids in reservoirs range from 137 to 205°C and from 127 to 214°C, respectively. The H− and O− isotopic compositions of thermal water are close relationship with the meteoric water that indicate that the isotopic changes of geothermal water due to fluid–rock interaction were small. This small shift may reflect the slow fluid–rock interaction in terms of low permeability of the slate host rocks.
\nCarbon and oxygen isotopic analyses indicate that the samples from outcrops and scaling in geothermal wells possess the highest and lowest values, respectively. These results infer that the former could be derived from fluids originating from the shallower reservoir, while the latter may be from the deeper reservoir to precipitate calcite veins near the faults. The calculated oxygen isotopes of fluids combining with the ratios of 3He/4He suggest that the fluid in the deep may be from magmatic source underneath the Chingshui geothermal field, while the thermal water in shallower reservoir is a mixing between deep magmatic fluids with meteoric one. The original fluids of bladed calcites confirm a meteoric origin, which have the similar oxygen isotopic value with the thermal fluids of Chingshui. It indicates the current orientations of the main conduits for geothermal fluids are oriented at N10°E with a dip of 70°E.
\nThis work was supported by the Ministry of Science and Technology (MOST), Taiwan, under the grants of NSC 102-3113-P-002-031, MOST 103-3113-M-002-001, MOST 104-3113-M-002-001, and MOST 105-3113-M-002-001.
\nIn biology, dissimilar molecules dock and interact to enable the perpetuation of the primordial logistics of living organisms. Molecular docking methodologies can be used to identify the interaction between a small ligand and a target molecule and to determine whether they could behave in combination as the binding site of two or more constituent molecules with a given structure. The comparison of docking molecules for proteins, other drug-like molecules, or even fragments from the original molecule enables a pool of prominent candidates to be calculated with listed values. Interestingly, a wide spectrum of molecular binding interactions can be explored with this technique, including lipid-protein, lipid-lipid, enzyme-substrate, drug-enzyme, drug-nucleic acid, protein-nucleic acid, nucleic acid-nucleic acid, protein-drug, and protein-protein potential affinities, with key functions in every molecular biological or biochemical stage, as well as structural coupling [1, 2].
The analysis of the binding scores between the constituent molecules in molecular recognition is essential to explain the constitutive processes and subsequently suggest a possible therapy in the context of a particular disease. The molecular docking in silico approach seeks the optimization of this process, not only in terms of techniques but also in relation to time and economic resources. For instance, there is no microscope with a sufficient power of resolution to capture an image at the dynamic (real-time) molecular level, and accordingly, theoretical and computational approaches can be used to predict the best binding and most probable trajectories. Faster techniques and reduced resources are related to efficiency, in contrast to in vitro approaches, in which the examination of every synthesized and purified protein can have higher time and material costs. On average, traditional in vitro research can take about a decade to complete and can cost around 800 million USD; in silico method importantly diminishes these costs [3]. As such, due to the difficulties in determining the structures of complexes, in silico approaches, including molecular docking, are suitable for predicting binding modes by investigating thousands of ligand positions using the lowest energy score analyzed.
Since 1975, the development of high-throughput protein purification X-ray crystallography and nuclear magnetic resonance spectroscopy has continued to advance, predominantly contributing to a better understanding of the structural details of macromolecules and complexes with ligands [4]. Molecular docking, as with many other in silico tools, has become more common and easier to apply to the field of drug discovery; however, it is not entirely dependent on molecular structure databases. It is not impossible to work with molecules that are absent from the databases, as they can be modeled by using one or multiple similar structures to build a novel chimeric output that can mimic the original molecule. In the docking process, the parameters can be further adjusted to test the function of the drug molecule versus a particular target molecule.
After the molecular docking has been performed, the software executes a systematic search on the algorithm, in which the ligand conformation is recurrently approached until the minimum energy conformation is identified. The final result will have a negative value of ΔG (U total in kcal/mol), in which a number of electrostatic and van der Waals energy variables will have been synthesized. These energies are related through the interaction between two molecules. This association allows a final scoring function to classify the candidate positions through the driving forces of the specific interactions to be obtained. The structural shape and electrostatic forces of both the ligand and the target molecule at specific binding-site surfaces are key aspects in biological complementarity systems. In the drug discovery field, several key aspects must be considered when predicting whether the molecule will bind with the receptor target, such as the structural shape and electrostatic interactions of the protein-ligand, ligand-ligand, or protein-protein. In this sense, several physicochemical parameters, including the van der Waals forces, Coulombic interactions, and the formation of hydrogen bonds, play relevant roles. The combination of all these values and potential binding is predicted by a docking score. Essentially, for drug design, it is possible to use a rigid system in which a rotational and translational space in six dimensions is explored to fit the ligand into a specific binding structure site [5].
The constantly growing number of biological targets for the design of rational structure-based ligands in public databases has gained interest in the research community. In the drug discovery field, the essential processes in computational docking are the design of the ligand and the search for targets of the existing candidate ligands. The latter are used to predict a reliable binding affinity, in which the best possible physicochemical prediction of how the target and ligand will interact is made. A strategy to enhance the selection of drug candidate ligands is based on the scores obtained from in silico approaches. These scores not only significantly reduce the amount of inefficient compounds synthesized but also decrease the amount of unnecessary biological tests by taking into account valuable information about crucial binding elements in a given ligand-receptor conglomerate. Molecular docking approaches are used to calculate the scores of ligand-binding types and linking affinities. The estimation of reliable ligand-binding associations and modes is a difficult challenge. During the last few decades, the scientific community has gradually shown an increasing interest in molecular docking methods, illustrated by the increase in references and the number of publications in the field [6]. Nevertheless, there is currently no standard consensus regarding the criteria that should be used to classify a docking mode as correct or incorrect. Most docking methods are based on the use of general scoring functions to predict molecular suitability for a wide range of applications. In order to accomplish what is needed, a reliable scoring function, reasonable protein flexibility, and a treatment for ligand conformational changes are required.
In the context of molecular biology, the interactions between molecules are key to understanding the mechanisms that underlie a particular biomedical event. The latest achievements have been the improvement of computational methods essential to the process of drug discovery, modeling in the prelaminar stage, and the actual analysis of putative binding interactions. It is possible to conduct exploratory work by examining the best score function values or by using a large set of multivariate experimental data. In both cases, it is possible to analyze how changes in ligands or macromolecules can have an effect on their interactions by validating the associated biological processes, with the aim of gaining a better understanding of the interplay between the biomolecular functions of the bioactive candidates through the characterization of the kinetics and binding score values imperative to their molecular recognition. In order to better understand the historical and conceptual implications of the development of this interesting and well-established technique, past and present achievements must be considered, as well as the current limitations with the potential to change the course of the technological methods developed in the future. In comparison to “wet lab” experimental procedures such as, e.g., microarray technology or even sequencing, virtual screening is inexpensive and efficient. However, several considerations need to be taken into account [7]. Overall, computational methods have been a recurrent option due to the focus approximation of the analysis.
As one of the most commonly used approaches since the 1980s, the experimental data obtained through molecular docking techniques have grown at an increasing rate since the approach was first established. Programs configured through different algorithms for molecular docking analysis have been developed on an almost yearly basis, significantly improving pharmaceutical research [6]. The first algorithms were designed for protein-protein interactions. Along with the scoring function, which is used to determine the best binding poses, algorithms designed to calculate the best geometrically complementary shapes as rigid bodies are necessary to identify the most favorable orientations and conformational bindings with the potential to confer a putative drug candidate.
The gradual achievement of more powerful and complex algorithms with the addition of further parameters has paralleled computational technological advances over the last few decades. In order to achieve optimum flexibility, in silico methods use different tools with different approaches. Docking software depends on the algorithms employed, which comprise three different kinds: systematic, stochastic, or deterministic.
In the beginning, calculation algorithms that consider docking complexes to be rigid structures were used. In rigid docking, the objective is to match the ligand to the protein receptor, with the main aim being the generation of as many poses as possible in order to achieve the optimum of all poses. Through this process, all possibilities are considered heuristically to identify a group of complementary matches that present the most favorable van der Waals forces between the ligand and the macromolecule receptor. Intermolecular interaction calculations avoid any flexibility but nevertheless have a level of freedom dependent on a 3x3 matrix plus the vector rotation. This means that three rotational and three translational degrees of freedom cover all possible moves in three-dimensional space within the active site. However, no binding is permitted, as the macromolecular structures are simplistically represented as solid structures located under a center of mass and longitude [8].
The earliest work was performed using structural shape contacts, in which the fitting of outlines enables the best possible complementary configuration between two proteins to be identified [9]. A little later, a shape matching strategy algorithm was used by Kuntz and collaborators in UCSF8 to continue searching for possible configurations using the geometric distance between the ligand atoms and the macromolecule or receptor spheres (Figure 1).
Top left, binding site; top right, ligand. Down below conjugate with geometrical fitness functional group related proposed by the earliest docking algorithm model.
In this method, the ideal intersection or match between the ligand and receptor is viewed as a “negative image” that represents the active site. The image is produced by covering the receptor surface region and overlapping spheres with a solvent, in which a part of the overlapping spheres comprises the actual binding site. This constitutes the fundamentals of the DOCK search algorithm [10]. A few years later, Kuntz also developed a more advanced approach by conferring flexibility to the ligand; however, this variant is still categorized as “flexible docking.” Subsequently, the investigation of HIV-1 protease using this approach was notable for leading to the technique’s exponential use in drug discovery [11].
Following the pioneering work from Kuntz, a different approach was taken a decade later in order to develop an improved new geometric recognition method, which was developed through an algorithm called Fourier transformation [12]. For the first time, the molecules could be described by a digital model, allowing their interior and exterior parts to be distinguished. This novel method allows faster calculation by determining the surface of contact, overlap, and approximation using the six degrees of freedom. In this method, molecules are considered rigid bodies, and the changes in structure have the degrees of freedom. This technique makes it possible to process atomic coordinates, and Zdock represents an example of this approach. Nevertheless, rigid-body algorithms are very erratic and ineffective in terms of any structural and conformational change arising due to the interface between the ligand and the receptor. In this context, new alternatives to enable torsions and angle movement became a matter of interest. In the same period, a new semiflexible docking innovation was achieved using the HADDOCK protocol [13], which involves rigid-body docking complemented by semiflexible optimization in order to describe possible torsion angles in the main backbone and side chains. Unlike the previous Fourier transformation method [12], which uses a grid, this method adopts a Cartesian approach with particular coordinates, in which one of the two molecules is flexible and the solvent can be selected. One of the two molecules therefore needs to be small in order to be computationally possible in terms of the number of conformational variations. Other methods also attempt to describe flexible bodies undergoing rotational conformational, rotational, and translational changes, mimicking the nature of biological molecules. In this category, both the ligand and the receptor that are modeled by simulating protocols are flexible. However, the flexibility needs to be lowered to make computational configuration possible. In the end, flexible docking approaches offer a more precise technique capable of imitating in vivo behavior of the possible structural conformations.
In flexible docking, there are two different logarithmic approaches, deterministic incremental construction and stochastic. Systematic incremental construction algorithms are most commonly used, which gradually develop binding predictions on the basis of all possible ligand-binding poses covering all specified areas, e.g., DOCK [14], Glide [15], LUDI [16] FlexX [17], Hammerhead [18], and Surflex [19], in which on-the-fly incremental ligand construction is implemented. In this method, the number of analyses grows in line with increases in the degrees of freedom as part of anchor-and-grow methods. In a different example, in eHiTS, the ligand is fragmented, and each piece is tested for rigid docking, commonly based on library screening for the best conformations to religate the fragments and test their flexibility.
A different approach randomizes probabilistic or stochastic algorithms to selectively reject or accept configurations through the criteria spectrum, in which computational efforts are optimized, e.g., AutoDock [20], DARWIN [21], Monte Carlo [22], and GOLD [23]. By the middle of the 1990s, this technique was the point of origin of a diverse set of methods that are most commonly present in the genetic algorithm, named after Darwin’s theory of evolution, in which the ligand is interpreted as a chromosome and its fragments are considered genes [24]. Every gene exhibits conformational behavior due to its torsional/translational nature. During computational analyses, the information is transmitted and altered through stochastic crossover and mutational events evolving through specific parameters. The changes improve the conformational binding pose from the ligand and the receptor, e.g., Lamarckian (AutoDock). In the case of the Monte Carlo stochastic variant that produces randomized translational conformations, the most thermodynamically stable potential bindings are explored by focusing on the local minimum energy using a decision criteria parameter that is based on a temperature reaction, called Metropolis. The flexibility also alternates with rigid rotation, displaying several parameters at once. A more recent development is the deterministic method, which has been used for Newton equation simulations and also employs Monte Carlo methods that can measure trajectories, using Amber, Charm, and GROMACS; however, this scope forms the focus of the present work, and wide reviews have been provided by other researchers [25, 26, 27].
The drug discovery informatics market had an estimated value of 713.4 million USD in 2016 [28]. The presence of in silico tools that can allow the computation of data flowing from diverse methodology pathways in parsimony with medical chemistry can be synergistic in terms of upgrading the market and are well-known in the scientific literature. In this manner, molecular docking has been consolidated as a useful technique among sequence analysis platforms, molecular modeling, and clinical training management. The use of molecular docking in each of these fields is enhancing drug discovery in the pharmaceutical and biotechnology sector. As it comprises several stages and workflows, the discovery of new drugs relies on in silico tools and molecular docking in particular to simplify the overall process.
A crucial factor is the steadily rising number of structures stored in the Protein Data Bank (PDB). The PDB is the most robust, currently storing over 151,000 structures and counting. The 3D structure information bank includes a large set of proteins, lipids, carbohydrates, and nucleic acids, in both single structures and complexes [29]. On the other hand, nearly a hundred different forms of molecular docking software are available, which offer analogous implementations with various implementation options. There has been rapid progress in developing faster architecture based on graphics processing unit clusters, more adequate algorithms for optimized computational analysis, and the tracking of ligand-receptor binding expressed in scoring functions.
Although there is a need to maintain computational equipment, the associated expenses are certainly lower than the costs of “wet lab” experiments, and molecular docking is therefore an affordable technique. One of the most challenging tasks in bioinformatics sciences is undoubtedly the development of new and effective drugs, which is currently an almost mandatory step before wet lab experiments. In structure-based drug modeling, obtaining the most accurate and efficient model of ligand-receptor binding is a crucial step and is a suitable starting point for further evaluation to test new compounds or drug candidates, but also and no less importantly, to discard the improbable candidates. Molecular-ligand docking is a significant tool in pharmacology at present and an important area of drug discovery that has comprised a central node of important achievements over the current century. As an interdisciplinary process of multiple joint efforts mainly from the pharmaceutical sector, biotechnological companies, and academic researchers, as well as many other fields, the process is highly complex and requires the most accurate and precise tools and methodologies. This has been enhanced by an increasing number of protein coordinates and the high number of available software programs that are constantly evolving with more sophisticated levels and a wider field of applications, in combination with more numerous candidates. In order to discover new drugs, as well as improve the existing ones, it is necessary to understand the targets as well as the nature of the possible drug candidates. In silico bioinformatics approaches have attracted increased interest due to the results of post-genomic era sequencing. Due to the limited set of protein-coding genes, the complexity is much higher due to posttranscriptional modifications, prosthetic groups, multimeric complexes, and other various phenomena, clearly demonstrating the need to better understand their nature to fulfill biomedical objectives. Interestingly this year’s (2019) publications account for the first time a pause in the upper trend of docking publication number (Figure 2). This may be symptomatic on how the future holds already crucial challenges.
Chart bar displaying paper publications per year (1982–2020) (NCBI, accessed on January 12, 2019).
The drug discovery informatics market is estimated to grow from 1.5 billion in 2016 to 2.84 billion by 2022 and may continue expanding. Accordingly, there is currently a rising demand for the discovery and implementation of novel informatics solutions. The major factors driving the expansion of the global market include the transition from pure research to clinical treatment. More skilled professionals, interdisciplinary backgrounds, and the high pricing of informatics software may have a crucial impact on the growing market. At present, a number of well-established applications have been made available for free or as paid software or services. However, many challenges remain to be addressed to enable the full potential of this powerful technique to be realized.
Nevertheless, in the case of pharmacology, the synergistic aspect is an important chemical phenomenon in which two different biomolecules with different origins can have an exponential effect in combination that is greater than their separate effects. If it is determined that a particular structure is more favorable [30] in terms of the docking score and it may be correlated with synergism, this can be secondary, due to the fact that a molecular docking procedure has not been developed to examine it in a particular scoring function. A linear/quadratic formula could be developed to measure synergy by discriminating between synergistic, additive, or antagonistic effects, which can be expressed both qualitatively and quantitatively. In this sense, further work is needed to investigate how the chemosensitivity between a macromolecule and ligand could be detected once more than one ligand is included. Although unmanageable amounts of data make this process difficult, it is possible to analyze the small targets that are the most restricted to the binding site being examined, especially in drug-protein analysis. System biology models that depend on a drug synergy test need to be developed in a more comprehensive manner, perhaps by including qualitative features in combination with the quantitative. In this sense, a novel input could be developed in computational docking analysis to enable, e.g., the measurement of molecular signaling that has been established to be part of several components, ligands, or targets. These systematic synergy modeling methods could support drug synergy research with the aim of improving the accuracy of experimental results.
An improvement of the molecular structure databases is necessary for further development. Filters are needed to ensure the structural models they contain are of a better quality, as this will influence the reliability of the results. The PDB was established in 1971 as a pioneer crystal structure database, and today it is the most common source for molecular in silico modeling, harboring more than 150,000 experimentally proven 3D models. However, there is no guarantee that the chosen structures are error-free, including even those with excellent geometrical parameters, and this must be taken into account. High-quality statistics are not an indication that the structure is perfect. Therefore, an improvement of their quality, protocols, and validation would allow the construction of better models that could be valuable in the inevitable task of structure refinement. However, a better model will not be more informative in terms of more detailed biological information, which means that the interpretation of a scientist will be necessary. However, the confirmation of outcomes and the precision of the docking tool in a certain interaction can be tested. Although docking strategies have become more complex, false positives are a recurrent issue with this technique, and as such, refining the structures stored in the PDB will undoubtedly lead to an improvement and better results from pharmacodynamics studies [31].
Those who devote their time to molecular docking are well aware of the large number of docking techniques. In the years to come, docking experiments will need to be more consistent in terms of the outputs generated by different docking methods. Using meta-experimental databases, including a large-scale and diverse variety of targets and ligands, comparisons of scoring functions have shown that accuracy and reportability are far from being reached. A standardized common workflow that follows the same procedures and is associated with the same advantages and issues is therefore necessary. A streamlined validation process to define standard test protocols needs to be agreed for every aspect of the docking method; otherwise there will be a lack of reproducibility in the output process used by each research group and for each given software [32].
The interaction model of the ligand and the active site must achieve the most optimum site of recognition. Docking ensembles using rigid proteins can be slightly inaccurate. Through the ensemble, the protein can fluctuate according to the relative energy, with more time spent in the lowered energy structure. On the other hand, the conformations of ligands fluctuate partially, making the whole ensemble more stable. This can be misleading for dockings that are not flexible, due to the fact that a given conformation may not be the most stable choice in the structure. Up-to-date docking scores have been oriented for machine learning scoring and mainly consist of four building blocks: descriptors, a model, a training set, and a test set. Currently, SFCscore, NNscore, or RFscore represents prominent examples of nonlinear and nontrivial correlations of data in order to avoid obstacles to interpretation [33]. Techniques that provide free access to the scoring function are still a minority and more options are needed, particularly those with open access. The number of poses needs to be exhaustive; however, this has not been well-established. In this sense, we can state that the sensitivity of the original conformation of the ligands remains unanswered. Furthermore, in the case of multidomain proteins, proteins are frequently composed of more than a single effector domain, and this should be taken into consideration.
With regard to a different aspect, how water is placed around the binding site is not a straightforward problem to solve, although recent studies have proposed the use of this parameter as functionally valid in specific contexts [34] within and around the conglomerate binding site. X-ray crystallography is the most extensively used tool for predicting 3D conformational structure; however, the actual output is only partially informative, due to the fact that the density limits are out of resolution and, on occasion, the electron density can be of insufficient quality. Future efforts need to endorse novel alternatives to increase the capacity and parameters that can be used in every aspect of a given analysis, not only in terms of water but also the physiological solutes found in nature and even protonation, in addition to the pH potency spectra.
An understanding of the biological functions and roles of a protein in a particular cell or tissue is highly relevant in determining the role of a protein’s structure, including all of its functional domains. Genome-wide studies have demonstrated that multidomains are present in over 70% of eukaryotic proteins. Nevertheless, protein-folding studies usually consider only single domains and are therefore not focused on the mechanisms in multidomains that can even influence the folding structure [34]. Very crucial obstacles are involved in multidomain docking analyses. In some examples, the understanding of intermolecular movement can be restricted by rigid docking methodologies that lack the ability to consider the effect of multiple domains in a single macromolecule. A given protein is not always present in a static and simplistic single conformational shape but can be present in a collection of scaffolds, stages, and intersections of conformational shapes. As a consequence, the free energy landscape can be profoundly affected, distinctively changing the scoring function’s output. This continues to present a major issue [35].
To improve modeling, the role played by multiple molecules in the context of a certain reaction is an indispensable step that must be considered. At the current stage of technology, this does not fall under the current scope of molecular docking, due to the fact that the processes are far too complex and it is difficult to manage all of the interactions that occur during a molecular binding and reaction. In order to mimic how chemistry works in nature, the inclusion of more than two factors (ligand/macromolecule) where methodologically possible would be a priority to enable the possible interactions in a molecular group to be predicted. Although a few software packages use this approach, in the future, it needs to become more common in other methods to address the binding modes of ligands in assessments with higher stoichiometry using multiple ligand complexes against the molecular target. Additionally, as stated earlier in this work, it would be of great interest to evaluate the synergy of ligand combination conjugates.
Over the last four decades, molecular docking has improved quite remarkably, contributing to the enhancement and improvement of pharmacology in addition to many different areas of applied and molecular biology. After the first complete draft of the Human Genome Project was announced in 2003, the scientific community concluded that there are far fewer protein-coding genes than expected and it has therefore been swift to study how molecules interact by investigating more possible target bindings of a given molecule. The increasing demand for molecular docking has paralleled the revolutionary advancement of its technological background. Nevertheless, several biochemical and physical properties of proteins, particularly at the surface of contact, need to be included in docking algorithms in conjunction with those already present. On the other hand, the question of how to diminish unnecessary calculations and outputs from undesirable rotations and therefore translations is a big challenge to be considered in the near future, especially in virtual screening. The right implementation needs to be standardized, and closer multi- and interdisciplinary teams must overcome this challenge in order to fine-tune this already widely explored technique.
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