ADMET modeling approaches in drug discovery
ter, 27 ago 2019
Publicado por CIBFar
Ferreira, Leonardo L.G 1 ; Andricopulo, Adriano D. 1
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.
1 Laboratory of Medicinal and Computational Chemistry, Center for Research and Innovation in Biodiversity and Drug Discovery, Sao Carlos Institute of Physics, University of Sao Paulo, Av. Joao Dagnone 1100, 13563-120, Sao Carlos, SP, Brazil