ML in Pharmacognosy & Biotech Discovery

Authors

Dr. Anand Kumar Dakuri; Mrs. Udaya Kumari Tula; Mr. Prakash Nathaniel Kumar Sarella; Dr. Durga Ganesh Jami; Dr. Venkateswarlu Garla
DOI: https://doi.org/10.69613/atw65623

Synopsis

This textbook is a useful guide to the new world of data-driven biology, focusing on how artificial intelligence (AI) and machine learning (ML) are revolutionizing pharmacognosy, nutrition, and biotechnology. It extends beyond traditional "wet lab" theory to show you how "in silico" computational methods are being applied today.

Readers will learn how AI is used to design personalized diets and discover novel nutraceuticals, and how ML models can optimize crop growth, help conserve medicinal plants, and even predict the structures of secondary metabolites. The book covers the use of AI in natural product discovery, from identifying crude drugs with image recognition to predicting herb-drug interactions.

A key feature of this text is its focus on molecular biology, showing how AI has fundamentally changed our understanding of protein structures and the roles of non-coding RNAs. Readers will learn the essentials of microbial and cellular informatics, including how to analyze gene sequences, build phylogenetic trees, and use AI to optimize enzyme engineering.

Designed for students and scientists in pharmacy, biology, and biotechnology, this book provides the core technical knowledge needed to apply machine learning to the next wave of biological discovery. It is prepared in accordance with the new PCI syllabus, drafted as per NEP 2020, making it an essential resource for B.Pharmacy VI semester students

Chapters

Cover Page

Downloads

Paperback

Published

25 December 2025
Copyright (c) 2025 ThinkPlus Pharma Publications

Details about this monograph

ISBN-10 (02)

8198765743

ISBN-13 (15)

9788198765741

Physical Dimensions

5in x 8in x 1in

How to Cite

ML in Pharmacognosy & Biotech Discovery. (2025). ThinkPlus Pharma Publications. https://doi.org/10.69613/atw65623