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

ABSTRACT:

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. 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

SUBMITTER: Venkatraman V 

PROVIDER: S-EPMC8479898 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature