“ML in Pharmacognosy & Biotech Discovery”
First Book in this Category as Per New PCI Syllabus NEP 2020

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

Open to: Faculty Members • Research Scholars • PG Students • PharmD Final Year Students

Book Editor Positions

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Chapter Author Position

Chapter 1

AI in Personalized Nutrition

Explains personalized nutrition, the role of AI, Examples of AI-driven diet recommendation engines and apps, Natural Language Processing (NLP), Machine learning models, knowledge graphs for food-nutrient-disease relationships

Chapter 2

AI for Nutraceuticals

Explains AI in Nutraceutical Discovery and Personalization, Virtual screening of natural product libraries, Developing Quantitative Structure-Activity Relationship (QSAR) models, Predicting molecular targets, In silico toxicology predictions

Chapter 3

AI in Smart Agriculture and Conservation

Explains AI in agriculture for optimizing plant growth, Greenhouse automation, GIS, Remote Sensing & Prediction Models

Chapter 4

Predicting Plant Secondary Metabolites

Explains Prediction of Plant secondary metabolites using genomes, Biosynthetic Gene Clusters (BGCs), AI tools for structure elucidation, Generative Adversarial Networks (GANs), Predicting bioactivity and chemical properties

Chapter 5

AI in Crude Drug Identification

Explains AI in Classification of Crude Drugs, computer vision and Convolutional Neural Networks (CNNs), Applying ML to chemical fingerprint data, Using AI to analyze spectroscopic data, Developing AI models for adulteration and substitution detection

Chapter 6

AI in Natural Product Drug Discovery

Explains Building and screening large virtual libraries, target deconvolution, De novo design of new drug candidates, AI-powered databases and mobile apps, Predicting potential herb-drug interactions, AI in optimizing polyherbal formulations

Chapter 7

AI for Regulatory and Biosynthetic Analysis

Explains Machine learning to track updates in EU, ICH, and WHO herbal guidelines, Using NLP to scan, interpret, and summarize, Trend Analysis, AI-powered tools for building and managing compliance, Metabolic flux analysis and AI-driven predictions

Chapter 8

Foundations of AI in Molecular Biology

Explains Supervised, unsupervised, and deep learning, genomics, proteomics, metabolomics, DNA, RNA, and proteins, Analyzing high-throughput sequencing data (e.g., RNA-Seq, ChIP-Seq)

Chapter 9

Advanced Molecular Analysis with ML

Explains The protein folding problem, AlphaFold and RoseTTAFold, ML-based identification of non-coding RNAs, Computational prediction of ncRNA targets

Chapter 10

Microbial Identification Techniques

Explains sequence alignment and homology, Basic Local Alignment Search Tool (BLAST), Interpreting BLAST results, ML models for classifying microbial sequences, antiSMASH and deep learning models, Genome mining for novel antibiotics

Chapter 11

Phylogenetic Analysis

Explains Sequence acquisition and Multiple Sequence Alignment, Methods for tree building, Distance-Matrix like Neighbor-Joining, and Character-Based like Maximum Likelihood, Interpreting and validating phylogenetic trees

Chapter 12

AI in Cellular and Enzyme Engineering

Explains AI in cellular bioimage analysis, CNNs for image segmentation, automating high-content screening and cellular diagnostics, enzyme immobilization, Using machine learning to predict optimal conditions, AI-guided directed evolution and enzyme engineering

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