DFT and DFT ML Hybrid Approaches for Biological Entities

Biological systems consist of chemical molecules and molecular complexes involved in essential biological and biochemical processes. Density Functional Theory (DFT) has become a fundamental quantum-mechanical tool for investigating the electronic structure, reactivity, and properties of biomolecules, including proteins, nucleic acids, enzymes, and pharmaceuticals. It provides valuable insights into enzymatic mechanisms, molecular recognition, proton transfer, and redox reactions. However, the computational expense of DFT limits its application to large and complex biological systems. To overcome this challenge, hybrid Density Functional Theory–Machine Learning (DFT–ML) approaches with quantum-informed explainable artificial intelligence (XAI) in molecular dynamics (MD) simulations, has emerged for the rapid prediction of molecular properties, identification of reactive sites, and high-throughput screening for drug discovery and drug-target interactions. This book explores the integration of DFT, MD simulations, and hydrogen-bonding analyses in biologically relevant systems, while highlighting the principles, advantages, limitations, and emerging applications of DFT–ML with XAI-MD approaches in biomolecular research and the rational design of metalloenzymes as advanced biocatalysts. Through practical examples and emerging methodologies, it demonstrates how DFT-ML and XAI-MD approaches enable accurate molecular property prediction, biomolecular interaction analysis, metalloenzyme characterization, and rational therapeutic design while addressing challenges in interpretability, reproducibility, and scalability. Key FeaturesExplains the fundamentals and applications of DFT, AIMD, and QTAIM in molecular sciences. Demonstrates DFT and MD applications for biologically relevant compounds and protein–ligand systems. Covers ML, DL, XAI, and interpretable AI for drug discovery and molecular simulations. Explores AI-enhanced biomolecular dynamics, differentiable programming, and high-throughput simulation strategies. Highlights ML approaches for metalloenzyme prediction, active-site identification, and enzyme engineering. Discusses current challenges, limitations, and future directions in AI-integrated quantum chemistry and multiscale modeling. Designed for graduate students, researchers, computational chemists, pharmaceutical scientists, molecular biologists, and professionals in academia and industry, this book serves as an essential resource for understanding and applying next-generation computational methodologies that combine quantum mechanics, molecular modeling, and artificial intelligence to drive predictive molecular design and accelerated scientific innovation.