title: Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors.
abstract: P-glycoprotein (Pgp) inhibition has been considered as an effective strategy towards combating multidrug-resistant cancers. Owing to the substrate promiscuity of Pgp, the classification of its interacting ligands is not an easy task and is an ongoing issue of debate. Chemical structures can be represented by the simplified molecular input line entry system (SMILES) in the form of linear string of symbols. In this study, the SMILES notations of 2254 Pgp inhibitors including 1341 active, and 913 inactive compounds were used for the construction of a SMILE-based classification model using CORrelation And Logic (CORAL) software. The model provided an acceptable predictive performance as observed from statistical parameters consisting of accuracy, sensitivity and specificity that afforded values greater than 70% and MCC value greater than 0.6 for training, calibration and validation sets. In addition, the CORAL method highlighted chemical features that may contribute to increased and decreased Pgp inhibitory activities. This study highlights the potential of CORAL software for rapid screening of prospective compounds from a large chemical space and provides information that could aid in the design and development of potential Pgp inhibitors.
authors.affiliation: a Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand.
authors.forename: V
authors.initials: V
authors.lastname: Prachayasittikul
authors.affiliation: a Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand.|b Department of Community Medical Technology, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand.|c Department of Clinical Chemistry, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand.
authors.forename: A
authors.initials: A
authors.lastname: Worachartcheewan
authors.affiliation: d IRCCS , Istituto di Ricerche Farmacologiche Mario Negri , Milano , Italy.
authors.forename: A P
authors.initials: AP
authors.lastname: Toropova
authors.affiliation: d IRCCS , Istituto di Ricerche Farmacologiche Mario Negri , Milano , Italy.
authors.forename: A A
authors.initials: AA
authors.lastname: Toropov
authors.affiliation: a Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand.
authors.forename: N
authors.initials: N
authors.lastname: Schaduangrat
authors.affiliation: e Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand.
authors.forename: V
authors.initials: V
authors.lastname: Prachayasittikul
authors.affiliation: a Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology , Mahidol University , Bangkok , Thailand.
authors.forename: C
authors.initials: C
authors.lastname: Nantasenamat
mesh_terms: D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1 / Q000037:antagonists & inhibitors*; D004791:Enzyme Inhibitors / Q000737:chemistry / Q000145:classification* / Q000494:pharmacology*; D015233:Models, Statistical; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D012984:Software
chemical_list: D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1; D004791:Enzyme Inhibitors
keywords: CORAL software; P-glycoprotein inhibitors; SMILES; classification model; multidrug resistance; pharmacokinetic