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ADMET modeling approaches in drug discovery

2019; Elsevier BV; Volume: 24; Issue: 5 Linguagem: Inglês

10.1016/j.drudis.2019.03.015

1878-5832

Leonardo L. G. Ferreira, Adriano D. Andricopulo,

Analytical Chemistry and Chromatography

In silico prediction of ADMET is an important component of pharmaceutical R&D. Last year, the FDA approved 59 new molecular entities, with small molecules comprising 64% of the therapies approved in 2018. Estimation of pharmacokinetic properties in the early phases of drug discovery has been central to guiding hit-to-lead and lead-optimization efforts. Given the outstanding complexity of the current R&D model, drug discovery players have intensely pursued molecular modeling strategies to identify patterns in ADMET data and convert them into knowledge. The field has advanced alongside the progress of chemoinformatics, which has evolved from traditional chemometrics to advanced machine learning methods.