ArticleMolecules (Basel, Switzerland)2013
Benchmarking ligand-based virtual High-Throughput Screening with the PubChem database.
Article in Molecules (Basel, Switzerland), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- Ultra-large library screening with an evolutionary algorithm in Rosetta (REvoLd).Communications chemistry · 2025Article
- 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
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- 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
- Identification of Novel HBV/HDV Entry Inhibitors by Pharmacophore- and QSAR-Guided Virtual Screening.Viruses · 2021Article
- Hepatitis D Virus Entry Inhibitors Based on Repurposing Intestinal Bile Acid Reabsorption Inhibitors.Viruses · 2021Article
- General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps.Journal of chemical information and modeling · 2021Article
- BCL::Conf: Improved Open-Source Knowledge-Based Conformation Sampling Using the Crystallography Open Database.Journal of chemical information and modeling · 2021Article
- Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement.International journal of molecular sciences · 2020Review
- Cinnamic Derivatives as Antitubercular Agents: Characterization by Quantitative Structure-Activity Relationship Studies.Molecules (Basel, Switzerland) · 2020Article
- Identification of Novel Allosteric Modulators of Metabotropic Glutamate Receptor Subtype 5 Acting at Site Distinct from 2-Methyl-6-(phenylethynyl)-pyridine Binding.ACS chemical neuroscience · 2019Article
- BCL::Mol2D-a robust atom environment descriptor for QSAR modeling and lead optimization.Journal of computer-aided molecular design · 2019Article
- BCL::MolAlign: Three-Dimensional Small Molecule Alignment for Pharmacophore Mapping.Journal of chemical information and modeling · 2019Article
- QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery.Frontiers in pharmacology · 2018Article
- Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.Molecules (Basel, Switzerland) · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
With the rapidly increasing availability of High-Throughput Screening (HTS) data in the public domain, such as the PubChem database, methods for ligand-based computer-aided drug discovery (LB-CADD) have the potential to accelerate and reduce the cost of probe development and drug discovery efforts in academia. We assemble nine data sets from realistic HTS campaigns representing major families of drug target proteins for benchmarking LB-CADD methods. Each data set is public domain through PubChem and carefully collated through confirmation screens validating active compounds. These data sets provide the foundation for benchmarking a new cheminformatics framework BCL::ChemInfo, which is freely available for non-commercial use. Quantitative structure activity relationship (QSAR) models are built using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), and Kohonen networks (KNs). Problem-specific descriptor optimization protocols are assessed including Sequential Feature Forward Selection (SFFS) and various information content measures. Measures of predictive power and confidence are evaluated through cross-validation, and a consensus prediction scheme is tested that combines orthogonal machine learning algorithms into a single predictor. Enrichments ranging from 15 to 101 for a TPR cutoff of 25% are observed.
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Registered trials
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.