Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development 1st Edition

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development (1st Edition) by Kunal Roy is an essential guide for researchers, medicinal chemists, data scientists, and graduate students involved in modern drug discovery. This authoritative text bridges cheminformatics, quantitative structure–activity relationship (QSAR) modeling, and contemporary machine learning to deliver practical strategies for accelerating novel drug development.

Beginning with clear explanations of molecular descriptors, data curation, and feature selection, the book builds to advanced topics such as predictive modeling, ensemble methods, and validation techniques. Rich with real-world examples and case studies, it demonstrates how to translate computational insights into actionable leads—improving hit-to-lead prioritization and reducing late-stage attrition. Readers will gain hands-on understanding of algorithm selection, model interpretability, and performance metrics tailored for pharmacology and ADMET prediction.

Designed for a global audience—from academic labs to multinational pharma teams across North America, Europe, and Asia—this volume emphasizes reproducible workflows and industry-relevant best practices. Whether you are implementing QSAR models, integrating machine learning into medicinal chemistry, or guiding regulatory-compliant predictive toxicology, Kunal Roy’s concise explanations and pragmatic approach make complex concepts accessible.

If you want to stay at the forefront of computational drug discovery and equip your team with the tools to design safer, more effective molecules, this book is a must-have reference. Order your copy today and transform how you approach in silico drug design with proven cheminformatics and machine learning methodologies.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.