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Cheminformatics, QSAR and machine learning applications for novel drug development / edited by Kunal Roy.

By:
Contributor(s): Publisher: London, England : Academic Press , 2023Description: xix,745 pages, 24,cmContent type:
  • نص
Media type:
  • دون وسيط
Carrier type:
  • مجلد
ISBN:
  • 9780443186387
Subject(s): DDC classification:
  • 23 615.19  C517
Contents:
Section I: Introduction -- 1. Quantitative structure-activity relationships (QSARs) in medicinal chemistry -- 2. Computer-aided Drug Design : An overview -- 3. Structure-based virtual screening in Drug Discovery -- 4. The impact of Artificial Intelligence methods on drug design -- Section 2. Methods and Case studies -- 5. Graph Machine Learning in Drug Discovery -- 6. Support Vector Machine in Drug Design -- 7. Understanding protein-ligand interactions using state-of-the-art computer simulation methods -- 8. Structure-based methods in drug design -- 9. Structure-based virtual screening -- 10. Deep learning in drug design -- 11. Computational methods in the analysis of viral-host interactions -- 12. Chemical space and Molecular Descriptors for QSAR studies -- 13. Machine learning methods in drug design -- 14. Deep learning methodologies in drug design -- 15. Molecular dynamics in predicting stability of drug receptor interactions -- Section 3. Special topics -- 16. Towards models for bioaccumulation suitable for the pharmaceutical domain -- 17. 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)aurones -- 18. Deep Learning using molecular image of chemical structure -- 19. Recent Advances in Deep Learning Enabled Approaches for Identification of Molecules of Therapeutics Relevance -- 20. Computational toxicology of pharmaceuticals -- 21. Ecotoxicological QSAR modelling of pharmaceuticals -- 22. Computational modelling of drugs for neglected diseases -- 23. Modelling ADMET properties based on Biomimetic Chromatographic Data -- 24. A systematic chemoinformatic analysis of chemical space, scaffolds and antimicrobial activity of LpxC inhibitors -- Section 4. Tools and databases -- 25. Tools and Software for Computer Aided Drug Design and Discovery -- 26. Machine learning resources for drug design -- 27. Building Bioinformatics Web Applications with Streamlit 28. Free tools and databases in ligand and structure-based drug design.
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كتاب كتاب Central Library المكتبة المركزية 615.19 C517 (Browse shelf(Opens below)) Available قاعة الكتب 47843

ncludes bibliographical references and index.
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Section I: Introduction -- 1. Quantitative structure-activity relationships (QSARs) in medicinal chemistry -- 2. Computer-aided Drug Design : An overview -- 3. Structure-based virtual screening in Drug Discovery -- 4. The impact of Artificial Intelligence methods on drug design -- Section 2. Methods and Case studies -- 5. Graph Machine Learning in Drug Discovery -- 6. Support Vector Machine in Drug Design -- 7. Understanding protein-ligand interactions using state-of-the-art computer simulation methods -- 8. Structure-based methods in drug design -- 9. Structure-based virtual screening -- 10. Deep learning in drug design -- 11. Computational methods in the analysis of viral-host interactions -- 12. Chemical space and Molecular Descriptors for QSAR studies -- 13. Machine learning methods in drug design -- 14. Deep learning methodologies in drug design -- 15. Molecular dynamics in predicting stability of drug receptor interactions -- Section 3. Special topics -- 16. Towards models for bioaccumulation suitable for the pharmaceutical domain -- 17. 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)aurones -- 18. Deep Learning using molecular image of chemical structure -- 19. Recent Advances in Deep Learning Enabled Approaches for Identification of Molecules of Therapeutics Relevance -- 20. Computational toxicology of pharmaceuticals -- 21. Ecotoxicological QSAR modelling of pharmaceuticals -- 22. Computational modelling of drugs for neglected diseases -- 23. Modelling ADMET properties based on Biomimetic Chromatographic Data -- 24. A systematic chemoinformatic analysis of chemical space, scaffolds and antimicrobial activity of LpxC inhibitors -- Section 4. Tools and databases -- 25. Tools and Software for Computer Aided Drug Design and Discovery -- 26. Machine learning resources for drug design -- 27. Building Bioinformatics Web Applications with Streamlit 28. Free tools and databases in ligand and structure-based drug design.