Artificial Intelligence and Computational Approaches in Drug Discovery and Development
Description
This contributed volume presents a comprehensive overview of how artificial intelligence (AI), machine learning (ML), and traditional computational methods are being integrated and applied across various stages of pharmaceutical research and drug discovery. It covers a wide range of topics, including generative AI for novel compound design, deep learning in molecular modeling, ADMET prediction, and data curation strategies essential for effective AI applications. The book also discusses disease-specific case studies, such as AI-driven approaches for Alzheimer's disease, diabetes, cancer, and bacterial infections, as well as applications in drug repositioning, cosmetic ingredient design, and the analysis of natural compounds using density functional theory (DFT). By combining advanced computational strategies with real-world pharmaceutical challenges, the book offers valuable insights into current capabilities and future directions in the field. This work is a great resource for researchers, practitioners, and graduate students in pharmaceutical sciences, computational chemistry, bioinformatics, and related disciplines.
More details
Person
Samir CHTITA is a professor in the Department of Chemistry at the Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Morocco. His research specializes in computational drug discovery, including QSAR/QSPR modeling, molecular docking, molecular dynamics, DFT calculations, and AI-based virtual screening. He has authored more than 200 peer-reviewed publications in high-impact journals in the fields of cheminformatics and molecular modeling. His recent work focuses on integrating artificial intelligence and machine learning to predict pharmacokinetic properties and bioactivities of drug-like molecules. Prof. CHTITA ranks among the top 2% most cited scientists globally (Stanford University classification).
Content
Implementing a process-oriented "Fab Lab" model for AI in pharmaceutical sciences: Challenges and future directions.- Integration of AI and traditional computational methods in drug discovery.- Generative AI models for novel compound design.- Big data and data management for AI applications in pharmaceutical sciences.- Deep learning in molecular modeling: Advances and applications.- Drug repositioning strategies for viral diseases using AI.- DFT-Based study of natural compounds targeting Alzheimer's disease.- New drug design targeting Alzheimer's disease using AI approaches.- QSAR-Based virtual screening for Alzheimer's therapeutics.- Molecular docking and dynamics of antidiabetic agents.- In-silico exploration of antidiabetic compounds from medicinal plants.- Computational approaches in cancer drug discovery.- Molecular modeling for cosmetic ingredient design.- AI and ML models against bacterial infections.