Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models-Reference-Cited by-同舟云学术

Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models

Published:2024-09-10 Issue: Volume: Page:
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Handa Koichi¹²^ORCID,Sasaki Shunta³,Asano Satoshi²,Kageyama Michiharu²,Iijima Takeshi²,Bender Andreas¹⁴^ORCID

Affiliation:

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

2. Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan

3. Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited, Tokyo 191-8512, Japan

4. Institutul STAR-UBB, Universitatea Babes-Bolyai, Str. Mihail Kogălniceanu nr. 1, Cluj-Napoca 400084, Romania

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00921

Reference56 articles.

1. Evaluation and Quantitative Prediction of Renal Transporter-Mediated Drug-Drug Interactions

2. Organic Cation Transporter OCTs (SLC22) and MATEs (SLC47) in the Human Kidney

3. Validation of a Drug Transporter Probe Cocktail Using the Prototypical Inhibitors Rifampin, Probenecid, Verapamil, and Cimetidine

4. Identification of Appropriate Endogenous Biomarker for Risk Assessment of Multidrug and Toxin Extrusion Protein‐Mediated Drug‐Drug Interactions in Healthy Volunteers

5. Drug‐Drug Interactions Involving Renal OCT2/MATE Transporters: Clinical Risk Assessment May Require Endogenous Biomarker‐Informed Approach