Modern QSAR approaches have wide practical applications in drug discovery for designing potentially bioactive molecules. If such models are based on the use of 2D descriptors, important information contained in the spatial structures of molecules is lost. The major problem in constructing models using 3D descriptors is the choice of a putative bioactive conformation, which affects the predictive performance. The multi-instance (MI) learning approach considering multiple conformations in model training could be a reasonable solution to the above problem. In this study, we implemented several multi-instance algorithms, both conventional and based on deep learning, and investigated their performance. We compared the performance of MI-QSAR models with those based on the classical single-instance QSAR (SI-QSAR) approach in which each molecule is encoded by either 2D descriptors computed for the corresponding molecular graph or 3D descriptors issued for a single lowest energy conformation. The calculations were carried out on 175 data sets extracted from the ChEMBL23 database. It is demonstrated that (i) MI-QSAR outperforms SI-QSAR in numerous cases and (ii) MI algorithms can automatically identify plausible bioactive conformations.

Department of Materials Science and Engineering Technion Israel Institute of Technology 3200003 Haifa Israel

Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacký University and University Hospital in Olomouc Hnevotinska 5 77900 Olomouc Czech Republic

Laboratory of Chemoinformatics and Molecular Modeling A M Butlerov Institute of Chemistry Kazan Federal University Kremlyovskaya 29 420111 Kazan Russia

Laboratory of Chemoinformatics Institute Le Bel University of Strasbourg B Pascal 4 67081 Strasbourg France

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