title: Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.
abstract: Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.
authors.affiliation: Department of Chemistry, Center for Structural Biology, Institute of Chemical Biology, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN, 37235, USA.
authors.forename: Jeffrey
authors.identifier: http://orcid.org/0000-0003-1244-1447
authors.initials: J
authors.lastname: Mendenhall
authors.affiliation: Department of Chemistry, Center for Structural Biology, Institute of Chemical Biology, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN, 37235, USA. jens@meilerlab.org.
authors.forename: Jens
authors.identifier: http://orcid.org/0000-0001-8945-193X
authors.initials: J
authors.lastname: Meiler
mesh_terms: D000465:Algorithms; D055808:Drug Discovery; D008024:Ligands; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship
chemical_list: D008024:Ligands
keywords: Artificial Neural Network (ANN); BioChemicalLibrary (BCL); Dropout; Ligand-Based Computer-Aided Drug Discovery (LB-CADD); Machine learning (ML); Quantitative Structure Activity Relationship (QSAR)
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