Computational Drug Discovery: A Primer

By N. Sukumar, Harishchander Anandaram and Pratiti Bhadra
(Ion Cure Press, 2023)

This book presents a concise yet thorough introduction to the process of computational drug discovery, including how machine learning techniques are increasingly used in the design of new drugs. It provides a balanced coverage of chemical space, biological space and computational modeling aspects.

Foreword
Over the last two decades, the field of drug discovery has undergone remarkable transformations due to the increasing availability of chemical and biological data, as well as the powerful computational tools to analyze and model this data. In "Computational Drug Discovery: A Primer", Professor Sukumar and his co-authors provide a comprehensive account of key techniques and methodologies necessary to drive innovation in the area. This book covers the topics of cheminformatics and machine learning, as applied to the design and discovery of drugs.
This book presents a concise yet thorough introduction to the process of computational drug discovery, including how machine learning techniques are increasingly used in the design of new drugs. The book begins by delving into the basic principles of molecular modeling techniques and highlighting the importance of extracting different types of domain information from molecular structures. One of the book's major strengths is its excellent focus on chemical space networks and biological networks, including metabolic networks, gene regulatory networks, and signal transduction networks.
After providing enough background information, the book then shifts its focus to predictive modeling and how to map structural information to activities and properties. It provides exhaustive coverage of different cheminformatics approaches. The book also provides an overview of various conventional data mining and statistical techniques, including various linear and nonlinear learning algorithms and techniques, along with optimization techniques such as Genetic algorithms. Most importantly the book provides an overview of artificial neural networks and deep learning, with lucid details about different DNN architectures useful in drug discovery. The authors further provide invaluable tips for making robust and reliable predictions in drug discovery. Among these tips is the importance of choosing a model that strikes a balance between predictive ability and interpretability. While deep neural network architectures are useful for shortlisting drug-like molecules, the authors emphasize the need to develop physics-informed 3D-based models in order to meet the urgent need for more accurate predictions.
To sum up, this book provides a balanced coverage of chemical space, biological space and computational modeling aspects. With his extensive experience in teaching and research in this area, Professor Sukumar along with his co-authors have commendably presented complex concepts in a readily understandable manner. Overall, this book is a must have resource for students, academicians, teachers, researchers and practitioners interested in the cutting-edge field of computational drug discovery.
Jayaraman K. Valadi
Distinguished Professor
FLAME University, Pune, India

Preface
This book is the outcome of undergraduate and postgraduate course I (NS) developed and taught at Shiv Nadar University, Dadri. It is aimed at an audience with some knowledge of chemistry and mathematics, but unfamiliar with cheminformatics, machine learning or its applications in drug discovery. I am thankful to the Institute of Mathematical Sciences, Chennai, for hosting me in September-November 2022, during which time most of the actual writing on the book was completed. I am deeply indebted to all my former colleagues at the Rensselaer Polytechnic Institute in Troy, NY, especially Professors Curt Breneman, Kristin Bennett, and Mark Embrechts, to Drs. Michael P. Krein and Saurav Das, and to Professor Valadi Jayaraman, for many valuable insights that have gone into this book. Thanks are also to Professor Areejit Samal and Drs. Vinith Rejathalal, Sanjanashree Palanivel and Navaneeth Haridasan, for helpful discussions. I also owe thanks to the many students, especially Drs. Ganesh Prabhu, Pinaki Saha, Vivek Ananth and Sagar Bhayye, and to Manuja Kothiyal, Rudra Agarwal, Ritwik Bhattacharya, Ananya Biswas, Raman Dutt, Gunjan Gupta, Sanjana Krishnamani, Vivek Krishnan, Sanjana Maheshwari, Aniket Mishra, Garvisha Mittal, and Madhav Samanta, who challenged me with numerous interesting questions over the years. All the authors thank Prof. K. P. Soman, Amrita Vishwa Vidyapeetham, Coimbatore, for his encouragement.
This book does not assume any prior knowledge of medicinal chemistry, computational chemistry, or drug design. It should be accessible equally to students of chemistry, physics, biology, bioinformatics, as well as computer science and data science. The subject matter encompasses the fields of cheminformatics as well as machine learning, applied to the design and discovery of small molecule drugs. It is hoped that this book will provide a brief introduction to readers who want to get an idea about the process of computational drug discovery, and how machine learning techniques are being increasingly used in the design of new drugs. Resources to the original literature and in-depth reviews are provided at the end for readers interested in delving deeper into the subject matter.

Contents 1. Drug Discovery in the Information-rich age
1.1. Why Computational Drug Discovery? The Drug Discovery pipeline
1.2. ADMET Screening
1.3. Lipinski’s Rules of 5
1.4. Chemical Space
1.5. Drug Delivery across the Blood Brain Barrier
1.6. Structure-Based and Ligand-Based Drug Design
1.7. Pattern recognition and Machine Learning
2. Representation of Chemical Structure and Similarity
2.1. Topological Indices
2.2. Substructural Descriptors and 2D fingerprints
2.3. 3D descriptors
2.4. Local Molecular Surface Property Descriptors
2.5. Shape descriptors
2.6. Chiral descriptors
2.7. Molecular Similarity Measures
3. Chemical and Biological Networks
3.1. Chemical Space Networks
3.2. Biological Networks in Biomarker Discovery
3.3. Metabolic Networks
3.4. Gene Regulatory Networks
3.5. Protein-Protein Interaction Networks
3.6. Signal Transduction Networks
3.7. Analysis of Biological Network-based Biomarker Discovery
3.8. Artificial Intelligence and Biological Networks-based Biomarker Discovery
3.9. Summary, Challenges and Future Prospects
4. Mapping Structure to Activity: Predictive Modeling
4.1. Linear Free Energy Relationships
4.2. Pharmacophores and Molecular Interaction Fields
4.3. Model Domain of Applicability
4.4. Activity Cliffs
4.5. Performance Measures in Classification and Regression
4.6. Model Validation
4.7. Structure Based Methods - Docking and Scoring
4.8. Molecular Dynamics Simulation in Computational Drug Discovery
5. Data Mining and Statistical Methods
5.1. Linear and Non-Linear Models
5.2. Data preprocessing and unbalanced datasets
5.3. Principal Component Analysis and Partial Least-Squares Regression
5.4. Feature selection
5.5. Evolutionary computing and Genetic Algorithms
5.6. K-Means and k-NN
5.7. Classification trees and Random forests
5.8. Support Vector Machines classification and regression
6. Artificial Neural Networks and Deep Learning
6.1. Self-Organizing Maps
6.2. Multi-Layer Perceptrons
6.3. Deep Neural Networks and Auto-Encoders
6.4. Convolutional Neural Networks
6.5. Generative Adversarial Networks
6.6. Reinforcement Learning
6.7. Transfer Learning
6.8. Recurrent Neural Networks and Transformers
7. Best Practices in Predictive Cheminformatics