Chapters authored
Computer-Aided Biosensor Design
Amperometric biosensors are widely used in point-of-care medical devices that help patients control blood glucose and cholesterol levels in an effective and convenient way. On the other hand, computer-aided technologies for biosensor design remain an actively developing field. In this chapter, we present a computational model for biosensor design that uses a reaction-diffusion equation. We have successfully applied this model to simulate cholesterol analysis based on a multienzyme system. Furthermore, we show that this computer-aided approach can be used to optimize biosensor performance. This model can be applied to industry-grade biosensor development and can be easily extended to model multiple types of biosensors for a wide array of clinical applications.
Part of the book: Computer-aided Technologies
Quantitative Methods in System-Based Drug Discovery
Modern pharmaceutical industries have faced significant challenges to deliver safe and effective medicines because of significant toxicity and severe side effects of discovered drugs. On the other hand, recent developments and advances in system-based pharmacology aim to address these challenges. In this chapter, we provide an overview of quantitative methods for system-based drug discovery. System-based drug discovery integrates chemical, molecular, and systematic information and applies this knowledge to the designing of small molecules with controlled toxicity and minimized side effects. First, we discuss current approaches for drug discovery and outline their advantages and disadvantages. Next, we introduce basic concepts of systems pharmacology with an emphasis on ligand-based drug discovery and target identification. This is followed by a discussion on structure-based drug design and statistical tools for pharmaceutical research. Finally, we provide an overview of future directions in systems pharmacology that will guide further developments.
Part of the book: Complex Systems, Sustainability and Innovation
Artificial Intelligence-Based Drug Design and Discovery
The drug discovery process from hit-to-lead has been a challenging task that requires simultaneously optimizing numerous factors from maximizing compound activity, efficacy to minimizing toxicity and adverse reactions. Recently, the advance of artificial intelligence technique enables drugs to be efficiently purposed in silico prior to chemical synthesis and experimental evaluation. In this chapter, we present fundamental concepts of artificial intelligence and their application in drug design and discovery. The emphasis will be on machine learning and deep learning, which demonstrated extensive utility in many branches of computer-aided drug discovery including de novo drug design, QSAR (Quantitative Structure–Activity Relationship) analysis, drug repurposing and chemical space visualization. We will demonstrate how artificial intelligence techniques can be leveraged for developing chemoinformatics pipelines and presented with real-world case studies and practical applications in drug design and discovery. Finally, we will discuss limitations and future direction to guide this rapidly evolving field.
Part of the book: Cheminformatics and its Applications