Artificial Intelligence for Chemical Sciences: Concepts, Models, and Applications

by Shrikaant Kulkarni (Editor), Shashikant Bhandari (Editor), Dushyant Varshney (Editor), P. William (Editor)

Overview

Chemists are increasingly employing artificial intelligence (AI) for diversified applications. This new volume explores the use of AI and its various computer-aided applications for the design of new drugs and chemical products, for toxicity prediction and biodegradation, and for fault diagnosis in chemical processing plants. The volume explores knowledge and reasoning-based approaches of the field of chemintelligence to make predictions about the right molecules with given structures and properties as precursors or starting materials, reaction pathways, reaction conditions, improvement in reaction efficiency and selectivity, toxicity, metabolism, biodegradation, and more.

Product Details

ISBN-13:	9781774918326
Publisher:	Apple Academic Press
Publication date:	05/09/2025
Pages:	414
Product dimensions:	6.12(w) x 9.19(h) x (d)

About the Author

Shrikaant Kulkarni, Ph.D., is an Adjunct Professor of Business at the Victorian Institute of Technology, Melbourne, Australia, as well as an Adjunct Professor at the Centre of Research Outcome and Impact at Chitkara University, Punjab, India. Dr. Kulkarni has been a senior academician and researcher for over four decades. He has delivered invited lectures and conducted sessions at national and international conferences and faculty development programs. He has published many research papers and has authored many book chapters. He has edited 25 books, with more in process at present, and also authored four textbooks on engineering chemistry.

Shashikant V. Bhandari, PhD, is a Professor at the All India Shri Shivaji Memorial Society’s College of Pharmacy, Pune, India. With over 24 years of experience in research and academics, Dr. Bhandari made a significant impact on the field of pharmaceutical chemistry through his groundbreaking research on anti-inflammatories, antioxidants, anticonvulsants, anticancer and anti-HIV drugs, and antiviral and antitubercular agents. He has made many presentations and published over 30 research papers in national and international journals. He has been awarded grants for research projects and has received numerous accolades for his work.

Dushyant B. Varshney, PhD, is the Chief Technology Officer at Arcturus Therapeutics, USA. Dr. Varshney has over 25 years of experience at global bio-pharma organizations. Earlier, he was Global Head of Manufacturing, Science & Technology at Gilead-Kite; Vice President and Global Head of Technical Services, Operations and Supply at Jubilant; and Head of Manufacturing, Science and Technology at Pfizer, where he led the American, European, and Asia Pacific teams. He has made many conference presentations, published over 40 articles and book chapters, and edited one book. He was recognized by Insights Success magazine as a Technophile CTO of the Year 2022.

P. William, PhD, is Dean of Research and Development and Assistant Professor of Information Technology at the Sanjivani College of Engineering, Savitribai Phule Pune University, India. He has published many papers in Scopus-indexed journals and IEEE conferences. His field of research includes natural language processing, artificial intelligence, deep learning, machine learning, soft computing, cybersecurity, and cloud computing. He has been associated with numerous multinational companies, including IBM, TATA Consultancy Services Limited, etc., and various educational groups.

Table of Contents

PART I: AI IN CHEMICAL SCIENCES FOR DESIGNING SYNTHETIC PATHWAYS, TOOLS AND TECHNIQUES 1. Applications and Case Studies of AI in Chemical Sciences 2. Computer-Aided Drug Synthesis and Design 3. Computational Tools and Techniques in Planning Organic Synthesis 4. Patenting Artificial Intelligence-Based Technologies in Chemical and Pharmaceutical Sciences PART II: APPLICATION OF COMPUTATIONAL TOOLS, AI, ML FOR PREDICTING TOXICITY AND BIODEGRADATION 5. Toxicity Predication in Chemistry Based on Machine Learning: A Review 6. Machine Learning Algorithms for Prediction of Chemical Toxicity 7. Artificial Intelligence-Based Prediction of Drug Metabolism 8. Exploration of Computational Approaches in Toxicity Prediction 9. Toxicity Forecasts: Navigating Data-Driven AI/ML Models: From Theory to Practice 10. AI-Based Models for Prediction of Biodegradation 11. Computer-Based Technologies for Prediction of Biodegradation PART III: APPLICATION OF EXPERT SYSTEMS AND AI IN FAULT DIAGNOSIS AND STRUCTURE REPRESENTATION 12. Exploring the Range of Knowledge-Based Prediction Applications in Chemistry 13. Fault Diagnosis in Chemical Process Plants Using Artificial Intelligence 14. Structure Representation Techniques and Applications in Cheminformatics