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Updated: Jun 13, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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通过结合指纹和基于物理的机器学习模型对MATE1传送器进行抑制活动的预测.

Koichi Handa1,2, Shunta Sasaki3, Satoshi Asano2

  • 1Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.

Journal of chemical information and modeling
|September 10, 2024
PubMed
概括

这项研究开发了改进的计算模型来预测MATE1抑制剂,这对于理解药物相互作用至关重要. 最好的模型实现了0.833的ROC-AUC,有助于药物发现.

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Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
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