title: Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands.
abstract: The Average Information Content Maximization algorithm (AIC-MAX) based on mutual information maximization was recently introduced to select the most discriminatory features. Here, this methodology was applied to select the most significant bits from the Klekota-Roth fingerprint for serotonin receptors ligands as well as to select the most important features for distinguishing ligands with activity for one receptor versus another. The interpretation of selected bits and machine-learning experiments performed using the reduced interpretations outperformed the raw fingerprints and indicated the most important structural features of the analyzed ligands in terms of activity and selectivity. Moreover, the AIC-MAX methodology applied here for serotonin receptor ligands can also be applied to other target classes.
authors.affiliation: Institute of Pharmacology, Polish Academy of Sciences, Smetna street 12, 31-343, Kraków, Poland. warszyc@if-pan.krakow.pl.
authors.forename: Dawid
authors.initials: D
authors.lastname: Warszycki
authors.affiliation: Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Lojasiewicza Street, 30-348, Kraków, Poland.
authors.forename: Marek
authors.initials: M
authors.lastname: Śmieja
authors.affiliation: Institute of Pharmacology, Polish Academy of Sciences, Smetna street 12, 31-343, Kraków, Poland.
authors.forename: Rafał
authors.initials: R
authors.lastname: Kafel
mesh_terms: D055808:Drug Discovery / Q000379:methods*; D048088:Informatics / Q000379:methods*; D008024:Ligands; D000069550:Machine Learning; D011985:Receptors, Serotonin / Q000378:metabolism*; D013329:Structure-Activity Relationship
chemical_list: D008024:Ligands; D011985:Receptors, Serotonin
keywords: Fingerprint reduction; Fingerprints; Machine learning; Selectivity studies; Serotonin receptors; Virtual screening