Abstract
In recent decades, the advancement of computational algorithms and the availability of big data have enabled artificial intelligence (AI) to dramatically improve predictive performance in nearly all research areas. Specifically, machine learning (ML) techniques, a major branch of AI, have been widely used in many tasks of drug discovery and development, including predicting treatment effects, identifying target genes and functional pathways, as well as selecting potential biomarkers. However, in practice, blindly applying ML methods may lead to common pitfalls, including overfitting and lack of generalizability. Therefore, how to improve the robustness and prediction accuracy of ML methods has become a crucial problem for researchers. In this review, we summarize the application of ML models to drug discovery by introducing the top-performing methods developed from large-scale drug-related data challenges in recent years.
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This work is supported by NSF#1452656 and NIH R35-GM133346.
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Wang, Z., Li, H., Carpenter, C. et al. Challenge-Enabled Machine Learning to Drug-Response Prediction. AAPS J 22, 106 (2020). https://doi.org/10.1208/s12248-020-00494-5
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DOI: https://doi.org/10.1208/s12248-020-00494-5