ArticleJournal of chemical information and modeling2019
Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models.
Article in Journal of chemical information and modeling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- A Hybrid Experimental and in silico Platform for ITPK1 Chemical Probe Discovery.SLAS discovery : advancing life sciences R & D · 2026Article
- Integrating AI in Medicinal Chemistry for Accelerated Drug Discovery: A Comprehensive SAR (CSAR) Optimization Strategy and Discovery of Potent ALDH3A1 Inhibitors.Journal of medicinal chemistry · 2026Article
- A Multitask Active Learning Framework with Probabilistic Modeling for Multi-Species Acute Toxicity Prediction.Molecules (Basel, Switzerland) · 2026Article
- Multi-Task Cascade Forest Framework for Predicting Acute Toxicity across Species.Research (Washington, D.C.) · 2026Article
- KRASAVA-An Expert System for Virtual Screening of KRAS G12D Inhibitors.International journal of molecular sciences · 2025Article
- Integrated Approach of Machine Learning and High-Throughput Screening to Identify Chemical Probe Candidates Targeting Aldehyde Dehydrogenases.ACS pharmacology & translational science · 2025Article
- HDAC3_VS_assistant: cheminformatics-driven discovery of histone deacetylase 3 inhibitors.Molecular diversity · 2025Article
- ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment.Nature communications · 2025Article
- Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data.Chemical research in toxicology · 2025Article
- AI-driven discovery of synergistic drug combinations against pancreatic cancer.Nature communications · 2025Article
- Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.Journal of chemical information and modeling · 2025Article
- Anticancer activity features of imidazole-based ionic liquids and lysosomotropic detergents: in silico and in vitro studies.Molecular diversity · 2024Article
- The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential.Beilstein journal of nanotechnology · 2024Article
- Development and application of consensusFrontiers in pharmacology · 2024Article
- Artificial Intelligence in ADME Property Prediction.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Pharos 2023: an integrated resource for the understudied human proteome.Nucleic acids research · 2023Article
- Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents.ACS pharmacology & translational science · 2022Article
- Allosteric binders of ACE2 are promising anti-SARS-CoV-2 agents.bioRxiv : the preprint server for biology · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem. Here, we explore how to use this comprehensive mapping of chemical biology space to support the development of large-scale quantitative structure-activity relationship (QSAR) models. We propose a new deep learning consensus architecture (DLCA) that combines consensus and multitask deep learning approaches together to generate large-scale QSAR models. This method improves knowledge transfer across different target/assays while also integrating contributions from models based on different descriptors. The proposed approach was validated and compared with proteochemometrics, multitask deep learning, and Random Forest methods paired with various descriptors types. DLCA models demonstrated improved prediction accuracy for both regression and classification tasks. The best models together with their modeling sets are provided through publicly available web services at https://predictor.ncats.io .
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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.