Recent Computational Techniques in De Novo Drug Design
Buch in deiner Nähe kaufen
Beschreibung
“Recent Computational Techniques in De Novo Drug Design” gives a thorough overview of modern computational methods used to discover new chemical compounds. The book looks at how fragment-based design, evolutionary algorithms, free-energy-guided optimization, and deep generative models have helped advance molecular discovery. It also discusses important challenges such as synthetic accessibility and ADME/Tox issues. The book explains how structural bioinformatics, cheminformatics, and machine learning work together to speed up hit generation and lead optimization in both academic and industry settings.
The chapters start by introducing the basics of de novo drug design and explain how it differs from virtual screening and QSAR methods. The book describes the shift from rule-based techniques to those driven by artificial intelligence that use a wide range of molecular data. The content is organized into sections on structure-based and ligand-based methods, MD and QM approaches, deep learning applications, and case studies from different therapeutic areas.
“Recent Computational Techniques in De Novo Drug Design” gives a thorough overview of modern computational methods used to discover new chemical compounds. The book looks at how fragment-based design, evolutionary algorithms, free-energy-guided optimization, and deep generative models have helped advance molecular discovery. It also discusses important challenges such as synthetic accessibility and ADME/Tox issues. The book explains how structural bioinformatics, cheminformatics, and machine learning work together to speed up hit generation and lead optimization in both academic and industry settings.
The chapters start by introducing the basics of de novo drug design and explain how it differs from virtual screening and QSAR methods. The book describes the shift from rule-based techniques to those driven by artificial intelligence that use a wide range of molecular data. The content is organized into sections on structure-based and ligand-based methods, MD and QM approaches, deep learning applications, and case studies from different therapeutic areas.
Unified workflow from target analysis to de novo design, optimization, and preclinical filtering End-to-end case studies using freely available academic and open-source tools Critical benchmarking of AI models against physics-based and classical de novo methods
Autor*in
Arli Aditya Parikesit
Themen in »Recent Computational Techniques in De Novo Drug Design«
Generative AI drug design De novo molecule generation AI protein engineering Synthetic accessibility prediction Retrosynthesis planning AI GANs VAEs drug discovery Quantum computing drug design AI pharma ethics Industry-academia AI collaboration Clinical translation AI candidates Generative chemistry AI applications AI-driven antibody design Multi-objective optimization AI Quantum generative chemistry Open-source drug discovery tools