Background: Accurate prediction of absorption, distribution, metabolism and excretion (ADME) properties can facilitate the identification of promising drug candidates. Methodology Results: The authors present the Janssen generic Target Product Profile (gTPP) model, which predicts 18 early ADME properties, employs a graph convolutional neural network algorithm and was trained on between 1000-10,000 internal data points per predicted parameter. gTPP demonstrated stronger predictive power than pretrained commercial ADME models and automatic model builders. Through a novel logging method, the authors report gTPP usage for more than 200 Janssen drug discovery scientists. Conclusion: The investigators successfully enabled the rapid and systematic implementation of predictive ML tools across a drug discovery pipeline in all therapeutic areas. This experience provides useful guidance for other large-scale AI/ML deployment efforts.
CITATION STYLE
Kumar, K., Chupakhin, V., Vos, A., Morrison, D., Rassokhin, D., Dellwo, M. J., … Desjarlais, R. L. (2021). Development and implementation of an enterprise-wide predictive model for early absorption, distribution, metabolism and excretion properties. Future Medicinal Chemistry, 13(19), 1639–1654. https://doi.org/10.4155/fmc-2021-0138
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