ReviewFrontiers in pharmacology2018
Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.
Review in Frontiers in pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers.
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Who cites it
111 citing papers in PubMed.
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- Assessing the ligand native-like pose using a quantum mechanical-derived hydropathic score for protein-ligand complementarity.npj drug discovery · 2026Article
- DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity.BMC bioinformatics · 2026Article
- Machine learning-assisted screening of natural product database for the identification of novel chalcone-based derivative as a potent DHODH inhibitor in cancer therapy.Discover oncology · 2026Article
- Computer-aided discovery of CDK16 inhibitors: a docking-augmented machine learning regression modelling approach.Journal of computer-aided molecular design · 2026Article
- Open-Source Molecular Docking and AI-Augmented Structure-Based Drug Design: Current Workflows, Challenges, and Opportunities.International journal of molecular sciences · 2026Review
- AI-driven drug-target interaction prediction: current progress, challenges, and future roadmap for precision medicine.Journal of computer-aided molecular design · 2026Review
- Computational Repurposing of Nilotinib and Radotinib as SIRT2 Inhibitors for Neurodegenerative Diseases.Journal of molecular neuroscience : MN · 2026Article
- Effects of steam processing conditions on the aroma and "dryness-like" effect ofFood chemistry: X · 2026Article
- Accurate and Generalizable Protein-Ligand Binding Affinity Prediction With Geometric Deep Learning.IEEE open journal of engineering in medicine and biology · 2026Article
- In silico discovery of umami peptides: From sequence-based intelligent models to structure-function mechanistic insight.Current research in food science · 2026Review
- Review
- Swimming exercise improves vascular function and mitigates cardiac apoptotic response via Bcl-2 and Caspase-3 pathway in streptozotocin-induced diabetic rats: in vivo and in silico molecular docking study.European journal of medical research · 2025Article
- Article
- Design, synthesis, and antiproliferative activity of new 5-ethylsulfonyl-indazole-3-carbohydrazides as dual EGFR/VEGFR-2 kinases inhibitors.Journal of enzyme inhibition and medicinal chemistry · 2025Article
- Article
- Artificial intelligence in drug development for delirium and Alzheimer's disease.Acta pharmaceutica Sinica. B · 2025Review
- RiSKs in Computational Modeling of Isoform-Selective RSK Inhibitors.Journal of chemical information and modeling · 2025Review
- A Structure-Based Computational Pipeline for Broad-Spectrum Antiviral Discovery.bioRxiv : the preprint server for biology · 2025Article
51 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.
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