FP-ADMET: a compendium of fingerprint-based ADMET prediction models

Motivation: The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties. Summary:In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D/3D molecular descriptors. Availability: The models are made available as part of open access software that can be downloaded from https://gitlab.com/vishsoft/fpadmet.

Automated summary

This article examines the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. For most properties, fingerprint-based random forest models show comparable or better performance than traditional 2D/3D molecular descriptors.