Artificial intelligence in drug discovery / edited by Nathan Brown, Benevolent AI, UK
Introduction; The History of Artificial Intelligence and Chemistry; Chemical Topic Modelling – An Unsupervised Approach Originating from Text-mining to Organize Chemical Data; Deep Learning and Chemical Data; Concepts and Applications of Conformal Prediction in Computational Drug Discovery; Non-applicability Domain. The Benefits of Defining “I don’t know” in Artificial Intelligence; Predicting Protein-Ligand Binding-Affinities; Virtual Screening with Convolutional Neural Networks; Machine Learning in the Area of Molecular Dynamics Simulations; Compound Design Using Generative Neural Networks; Junction Tree Variational Autoencoder for Molecular Graph Generation; AI via Matched Molecular Pair Analysis; Molecular de novo Design Through Deep Generative Models; Active Learning for Drug Discovery and Automated Data Curation; Data-driven Prediction of Organic Reaction Outcomes; ChemOS: an Orchestration Software to Democratize Autonomous Discovery; Summary and Outlook.
| Medienart: |
E-Book |
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| Erscheinungsjahr: |
[2021] © 2021 |
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| Erschienen: |
Cambridge: Royal Society of Chemistry ; 2021 © 2021 |
| Reihe: |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Brown, Nathan [HerausgeberIn] |
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| Links: |
doi.org [lizenzpflichtig] |
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| ISBN: |
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| BKL: | |
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| Themen: |
Artificial intelligence |
| Umfang: |
1 Online-Ressource (xvii, 405 Seiten) ; Illustrationen |
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| doi: |
10.1039/9781788016841 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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