Development and implementation of an enterprise-wide predictive model for early absorption, distribution, metabolism and excretion properties

13Citations
Citations of this article
42Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

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.

Cite

CITATION STYLE

APA

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

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free