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. Author manuscript; available in PMC: 2020 May 1.
Published in final edited form as: Nat Mater. 2019 Apr 18;18(5):435–441. doi: 10.1038/s41563-019-0338-z

Figure 1. Implementing end-to-end (E2E) machine learning models at all stages of drug discovery and development illustrating some of the key areas that could be modeled.

Figure 1.

A drug discovery and development dashboard for E2E machine learning provides the go-no-go decisions based on inputs of machine learning algorithms (SVM – support vector machine; DL – deep learning; BNB – Naïve Bayesian; KNN – K-nearest neighbors; RF – random forest; ADA-AdaBoost) or a consensus.