Description
Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book.The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates.- Presents chemometrics, cheminformatics and machine learning methods under a single reference- Showcases the different structure-based, ligand-based and machine learning tools currently used in drug design- Highlights special topics of computational drug design and available tools and databases
Table of Contents
Section I: Introduction1. Quantitative structure-activity relationships (QSARs) in medicinal chemistry2. Computer-aided Drug Design – An overview3. Structure-based virtual screening in Drug Discovery4. The impact of Artificial Intelligence methods on drug designSection 2. Methods and Case studies5. Graph Machine Learning in Drug Discovery6. Support Vector Machine in Drug Design7. Understanding protein-ligand interactions using state-of-the-art computer simulation methods8. Structure-based methods in drug design9. Structure-based virtual screening10. Deep learning in drug design11. Computational methods in the analysis of viral-host interactions12. Chemical space and Molecular Descriptors for QSAR studies13. Machine learning methods in drug design14. Deep learning methodologies in drug design15. Molecular dynamics in predicting stability of drug receptor interactionsSection 3. Special topics16. Towards models for bioaccumulation suitable for the pharmaceutical domain17. Machine Learning as a Modeling Approach for the Account of Nonlinear Information in MIA-QSAR Applications: A Case Study with SVM Applied to Antimalarial (Aza)aurones18. Deep Learning using molecular image of chemical structure19. Recent Advances in Deep Learning Enabled Approaches for Identification of Molecules of Therapeutics Relevance20. Computational toxicology of pharmaceuticals21. Ecotoxicological QSAR modelling of pharmaceuticals22. Computational modelling of drugs for neglected diseases23. Modelling ADMET properties based on Biomimetic Chromatographic Data24. A systematic chemoinformatic analysis of chemical space, scaffolds and antimicrobial activity of LpxC inhibitorsSection 4. Tools and databases25. Tools and Software for Computer Aided Drug Design and Discovery26. Machine learning resources for drug design27. Building Bioinformatics Web Applications with Streamlit28. Free tools and databases in ligand and structure-based drug design
