Fingerprint-based in silico models for the prediction of P-glycoprotein substrates and inhibitors

• Published in: Bioorganic & medicinal chemistry Vol. 20; no. 18; pp. 5388 - 5395
• Main Authors: Poongavanam, Vasanthanathan, Haider, Norbert, Ecker, Gerhard F.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.09.2012; Elsevier Science
• Subjects: ADMET; Algorithms; antagonists & inhibitors; Association rule; ATP Binding Cassette Transporter, Subfamily B, Member 1; ATP Binding Cassette Transporter, Subfamily B, Member 1 - antagonists & inhibitors; ATP Binding Cassette Transporter, Subfamily B, Member 1 - chemistry; ATP Binding Cassette Transporter, Subfamily B, Member 1 - metabolism; chemistry; Computational Biology; Databases, Pharmaceutical; Drug Substitution; drugs; Fingerprint; In silico; Indexing in process; Machine learning methods; metabolism; Models, Molecular; Molecular Structure; multiple drug resistance; P-glycoprotein; pharmacokinetics; prediction; support vector machines; therapeutics; P-glycoprotein; In silico; Fingerprint; Association rule; ADMET; Machine learning methods

P-Glycoprotein (P-gp, ABCB1) plays a significant role in determining the ADMET properties of drugs and drug candidates. Substrates of P-gp are not only subject to multidrug resistance (MDR) in tumor therapy, they are also associated with poor pharmacokinetic profiles. In contrast, inhibitors of P-gp...