ArticleJournal of computer-aided molecular design2016
Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.
Article in Journal of computer-aided molecular design, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Rapid and energy-efficient ultra-large library screening for drug discovery on a SpiNNaker2 neuromorphic chip.Communications chemistry · 2026Article
- Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors.Journal of chemical information and modeling · 2025Article
- Interpretable Deep-Learning pJournal of chemical information and modeling · 2025Article
- Computer-aided Drug Discovery Approaches in the Identification of Anticancer Drugs from Natural Products: A Review.Current computer-aided drug design · 2025Review
- Comprehensive review of the repositioning of non-oncologic drugs for cancer immunotherapy.Medical oncology (Northwood, London, England) · 2024Review
- Deep neural network learning biological condition information refines gene-expression-based cell subtypes.Briefings in bioinformatics · 2023Article
- Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance.Bioengineering (Basel, Switzerland) · 2022Review
- Towards generalizable predictions for G protein-coupled receptor variant expression.Biophysical journal · 2022Article
- Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery.Frontiers in pharmacology · 2022Article
- Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.Molecular diversity · 2021Review
- General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps.Journal of chemical information and modeling · 2021Article
- A review on machine learning approaches and trends in drug discovery.Computational and structural biotechnology journal · 2021Review
- Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors.Sensors (Basel, Switzerland) · 2020Article
- Discovering Anti-Cancer DrugsFrontiers in pharmacology · 2020Review
- BCL::Mol2D-a robust atom environment descriptor for QSAR modeling and lead optimization.Journal of computer-aided molecular design · 2019Article
- Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance.Circulation. Cardiovascular genetics · 2017Article
- Quantitative Structure-Activity Relationship Modeling of Kinase Selectivity Profiles.Molecules (Basel, Switzerland) · 2017Article
- Ligand-based virtual screen for the discovery of novel M5 inhibitor chemotypes.Bioorganic & medicinal chemistry letters · 2016Article
- Computational methods in drug discovery.Beilstein journal of organic chemistry · 2016Review
Corrections and comments
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Authors and funding
2 authors.
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.