ArticleJournal of chemical information and modeling2021
General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps.
Article in Journal of chemical information and modeling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Structure-guided compound prioritization strategy for virtual screening identifies putative binders for the nuclear receptor LRH-1.bioRxiv : the preprint server for biology · 2026Article
- Comparative Data Analysis of Virtual Screening Methodologies for Predicting Urease Inhibitory Activity.ACS omega · 2025Article
- CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13.Journal of chemical information and modeling · 2025Article
- Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches.npj drug discovery · 2025Review
- Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products.Natural product reports · 2024Review
- The role of artificial intelligence in drug screening, drug design, and clinical trials.Frontiers in pharmacology · 2024Review
- Recent Deep Learning Applications to Structure-Based Drug Design.Methods in molecular biology (Clifton, N.J.) · 2024Review
- An overview of recent advances and challenges in predicting compound-protein interaction (CPI).Medical review (2021) · 2023Review
- PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions.Protein science : a publication of the Protein Society · 2023Article
- Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development.Pharmaceuticals (Basel, Switzerland) · 2023Review
- A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios.Journal of computer-aided molecular design · 2022Article
- Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.Molecular diversity · 2022Review
- Predicting the functional impact of KCNQ1 variants with artificial neural networks.PLoS computational biology · 2022Article
- Structural Comparative Modeling of Multi-Domain F508del CFTR.Biomolecules · 2022Article
- Towards Structure-Guided Development of Pain Therapeutics Targeting Voltage-Gated Sodium Channels.Frontiers in pharmacology · 2022Review
- 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
- Article
- Structure-based molecular modeling in SAR analysis and lead optimization.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The BioChemical Library (BCL) is an academic open-source cheminformatics toolkit comprising ligand-based virtual high-throughput screening (vHTS) tools such as quantitative structure-activity/property relationship (QSAR/QSPR) modeling, small molecule flexible alignment, small molecule conformer generation, and more. Here, we expand the capabilities of the BCL to include structure-based virtual screening. We introduce two new score functions, BCL-AffinityNet and BCL-DockANNScore, based on novel distance-dependent signed protein-ligand atomic property correlations. Both metrics are conventional feed-forward dropout neural networks trained on the new descriptors. We demonstrate that BCL-AffinityNet is one of the top performing score functions on the comparative assessment of score functions 2016 affinity prediction and affinity ranking tasks. We also demonstrate that BCL-AffinityNet performs well on the CSAR-NRC HiQ I and II test sets. Furthermore, we demonstrate that BCL-DockANNScore is competitive with multiple state-of-the-art methods on the docking power and screening power tasks. Finally, we show how our models can be decomposed into human-interpretable pharmacophore maps to aid in hit/lead optimization. Altogether, our results expand the utility of the BCL for structure-based scoring to aid small molecule discovery and design. BCL-AffinityNet, BCL-DockANNScore, and the pharmacophore mapping application, as well as the remainder of the BCL cheminformatics toolkit, are freely available with an academic license at the BCL Commons site hosted on http://meilerlab.org/.
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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.