Preface

Computer-aided drug design (CADD) techniques are used in almost every stage of the drug discovery continuum, given the need to shorten discovery timelines, reduce costs, and improve the odds of clinical success. CADD integrates modeling, simulation, informatics, and artificial intelligence (AI) to design molecules with desired properties. Briefly, the application of CADD methodologies in drug discovery dates back to the 1960s, tracing its origin to the development of quantitative structure-activity relationship (QSAR) approaches. Between the 1970s and 1980s, computer graphics programs to visualize macromolecules began to take off together with advancements in computational power. This coincided with the emergence of more sophisticated techniques, including mapping energetically favorable binding sites on proteins, molecular docking, pharmacophore modeling, and modeling the dynamics of biomolecules. Since then, CADD has evolved as a powerful technique opening new possibilities, leading to increased adoption within the pharmaceutical industry and contributing to the discovery of several approved drugs.

Recent developments in CADD have been propelled by advancements in computing, breakthroughs in related fields such as structural biology, and the emergence of new therapeutic modalities. Notably, the advent of highly parallelizable GPUs and cloud computing have significantly increased computing power, while quantum computing holds promise to simulate complex systems at an unprecedented scale and speed. Advances in AI technologies, particularly generative AI for molecule design, are reducing cycle times during lead optimization. Meanwhile, the resolution revolution in cryo-electron microscopy (cryo-EM) and AI-powered structure biology are shedding light on the three-dimensional structure of many therapeutically relevant drug targets, thereby expanding our ability to carry out structure-based drug design against these targets. Other exciting breakthroughs that offer new opportunities include the explosion in the size of "make-on-demand" chemical libraries that enable ultra-large-scale virtual screening for hit identification, the big data phenomena in medicinal chemistry with the advent of bioactivity databases like ChEMBL and GOSTAR that provide access to millions of SAR data points useful for building predictive models and for knowledge-based compound, the emergence of new therapeutic modalities like targeted protein degradation like PROTACs and molecular glues, and viable approaches for targeting various reactive amino acid side chains beyond cysteine for developing covalent inhibitors. These developments are also now enabling drug discovery scientists to tackle high-value drug targets previously considered undruggable.

The changing paradigm in drug discovery, complemented by technological advancements, has significantly expanded the toolbox available for computational chemists to enable drug discovery in recent years. Against this backdrop, we felt a need for a book that offers up-to-date information on the most important developments in the field of CADD. This book, titled "Computational Drug Discovery," is meant to be a valuable resource for readers seeking a comprehensive account of the latest developments in CADD methods and technologies that are transforming small-molecule drug discovery. The intended target audience for this book is medicinal chemists, computational chemists, and drug discovery professionals from industry and academia.

The book is organized into eight thematic sections, each dedicated to a cutting-edge computational method, or a technology utilized in computational drug discovery. In total, it comprises 26 chapters authored by renowned experts from academia, pharma, and major drug discovery software providers, offering a broad overview of the latest advances in computational drug discovery.

Part I explores the role of molecular dynamics simulation and related approaches in drug discovery. It encompasses various topics such as the utilization of physics-based methods for binding free energy estimation, the theory and application of enhanced sampling methods like Gaussian Accelerated MD to facilitate efficient sampling of the conformational space, understanding binding and unbinding kinetics of compound binding through molecular dynamics simulation, the application of computational approaches like WaterMap and 3D-RISM framework to understand the location and thermodynamic properties of solvents that solvate the binding pocket which offers rich physical insights compound design, and the use of mixed solvent?MD?simulations for mapping binding hotspots on protein surfaces based on the SILCS technology.

Part II focuses on the role of quantum mechanical approaches in drug discovery, covering topics such as the use of hybrid QM/MM method for modeling reaction mechanisms and covalent inhibitor design, refinement of X-ray and cryo-EM structures integrating QM and QM/MM approaches for accurate assignment of tautomer, protomers, and amide flip rotamers for downstream structure-based design, and quantifying protein-ligand interaction energies using QM methods at a reduced computational cost like the fragment molecular orbital (FMO) framework

Part III focuses on the application of AI in preclinical drug discovery, highlighting its growing importance across different stages of the drug discovery process. Given the recent advancements in AI and related technologies, we have chapters that outline advancements in deep learning for protein structure prediction, in particular the significant breakthrough achieved by AlphaFold2, the use of deep learning architectures such as Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and physics-inspired neural networks for predicting protein-ligand binding affinity, the emergence of generative modeling techniques for de novo design of synthetically tractable drug-like molecules that satisfy a defined set of constraints. In order to offer readers guidance on effectively applying machine learning (ML) models and ensuring their validity and usefulness, this section includes a chapter that discusses different approaches for evaluating the reliability and domain applicability of ML models.

Part IV of this book focuses on how the concept of chemical space and the big data phenomenon are driving drug discovery. It includes chapters describing innovative approaches in reaction-based enumerations that enable the generation of virtual libraries containing tangible compounds, followed by computational solutions for visualizing and navigating this vast chemical space. Additionally, this section also highlights the use of SAR knowledge bases like GOSATR for extracting valuable insights and generating robust design ideas based on medicinal chemistry precedence. Wrapping up the section is a chapter highlighting how the wealth of knowledge gained by mining the data in CSD is proving valuable in various stages of drug discovery.

The ever-expanding size of compound libraries and the advent of make-on-demand compound libraries have elevated virtual screening to a whole new level.

Part V focuses on ultra-large-scale virtual screening using approaches that scale virtual screening methods to match the size of these massively large compound libraries. Although virtual screening using docking is a well-established approach for hit finding in drug discovery, the ability of docking programs to generate the correct binding mode and accurately estimate binding affinity is still a challenge. Hence, we have a chapter that reviews collaborative efforts within the scientific community for evaluating and comparing the performance of docking methods, establishing standardized metrics for assessing the efficiency of virtual screening techniques through rigorous competitive evaluations.

Early profiling of absorption, distribution, metabolism, excretion, and toxicity (ADMET) endpoints in early drug discovery is essential for designing and selecting compounds with superior ADMET properties. Consequently, major pharmaceutical companies have developed and implemented predictive models within their organizations for predicting multiple endpoints to enhance compound design. Part VI of the book chapter offers an overview of in silico ADMET methods and their practical applications in facilitating compound design within an industrial context. Part VII explores the role of computational techniques in accelerating the design of cutting-edge therapeutic modalities. This section provides a comprehensive focus on two key areas: the design of molecular glues and the design of covalent inhibitors.

In addition to the aforementioned methods and approaches that revolutionize the drug discovery process, computing technologies are further accelerating drug discovery with enhanced speed and accuracy.

Part VIII is dedicated to exploring how cloud computing and quantum computing significantly expand the range of drug discovery opportunities. Particularly, there is great hope and excitement surrounding the potential applications of quantum computing in drug discovery. "The Quantum...