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CPT Pharmacometrics Syst Pharmacol ; 9(3): 129-142, 2020 03.
Article in English | MEDLINE | ID: mdl-31905263

ABSTRACT

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.


Subject(s)
Artificial Intelligence/standards , Drug Development/methods , Drug Discovery/methods , Machine Learning/statistics & numerical data , Algorithms , Artificial Intelligence/history , Artificial Intelligence/statistics & numerical data , Drug Approval/legislation & jurisprudence , History, 20th Century , Humans , Models, Theoretical , Predictive Value of Tests
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