title: PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties.
abstract: Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.
authors.affiliation: †INSERM, UMRS-973, MTi, Paris, France.|‡University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France.|§University of Helsinki, Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Helsinki, Finland.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Borrel
authors.affiliation: †INSERM, UMRS-973, MTi, Paris, France.|‡University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France.
authors.forename: Leslie
authors.initials: L
authors.lastname: Regad
authors.affiliation: §University of Helsinki, Division of Pharmaceutical Chemistry, Faculty of Pharmacy, Helsinki, Finland.
authors.forename: Henri
authors.initials: H
authors.lastname: Xhaard
authors.affiliation: †INSERM, UMRS-973, MTi, Paris, France.|‡University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France.
authors.forename: Michel
authors.initials: M
authors.lastname: Petitjean
authors.affiliation: †INSERM, UMRS-973, MTi, Paris, France.|‡University Paris Diderot, Sorbonne Paris Cité, UMRS-973, MTi, Paris, France.
authors.forename: Anne-Claude
authors.initials: AC
authors.lastname: Camproux
mesh_terms: D019295:Computational Biology / Q000379:methods*; D055808:Drug Discovery; D008024:Ligands; D008958:Models, Molecular; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D000069553:Supervised Machine Learning; D035501:Uncertainty
chemical_list: D008024:Ligands; D004364:Pharmaceutical Preparations; D011506:Proteins