ADMET modeling approaches in drug discovery.

By Leonardo L. G. Ferreira, Adriano Defini Andricopulo. Drug Discovery Today, 2019

681 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/ee2c4de0da38716e1a8c745acb8235a26f079249

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In Computational Theory and Mathematics

1. Xianhuan Shen
2. Langhui Li
3. M. Tan
4. Zunnan Huang
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6. Ganying Chen
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In Molecular Biology

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2. Langhui Li
3. M. Tan
4. Zunnan Huang
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7. Yuquan Zhou
8. Xianyang Liang

Citations in the corpus

• Computational Approaches in Preclinical Studies on Drug Discovery and Development (2020, background): “In silico ADMET prediction has progressed with the continuous development of cheminformatics and has entered the era of big data (Ferreira and Andricopulo, 2019).”