Machine Learning in Drug Discovery and Development Part 1: A Primer
| Autor: | Almut G. Winterstein, Gregory Hather, Juan Francisco Morales, Jagdeep T. Podichetty, Jackson Burton, Sarah Kim, Joshua D. Brown, Samuel Kim, Alan Talevi, Daniela J. Conrado, Jensen Kael White, Stephan Schmidt, Peter Bloomingdale |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Drug development Machine learning computer.software_genre Machine Learning Drug Development Artificial Intelligence Predictive Value of Tests Drug Discovery Tutorial Humans Pharmacology (medical) Model development Drug Approval business.industry Drug discovery lcsh:RM1-950 Pillar Química History 20th Century Models Theoretical Pharmacometrics lcsh:Therapeutics. Pharmacology Modeling and Simulation Key (cryptography) Artificial intelligence business computer Algorithms |
| Zdroj: | SEDICI (UNLP) Universidad Nacional de La Plata instacron:UNLP CPT: Pharmacometrics & Systems Pharmacology, Vol 9, Iss 3, Pp 129-142 (2020) CPT: Pharmacometrics & Systems Pharmacology |
| Popis: | Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase. Laboratorio de Investigación y Desarrollo de Bioactivos |
| Databáze: | OpenAIRE |
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