ReviewComputational and structural biotechnology journal2024
Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.
Review in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 39 papers.
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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.
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Who cites it
39 citing papers in PubMed.
- TINA: a geometry-aware post-hoc scoring framework for protein-protein interaction.Acta pharmacologica Sinica · 2026Article
- Antiplasmodial Compounds fromInternational journal of molecular sciences · 2026Article
- Cytotoxic Activity and In Silico Study of Secondary Metabolites Derived fromMarine drugs · 2026Article
- UHPLC/HRMS-Based Metabolomic Profiling, Antioxidant, and Antibacterial Activity of Dendrobium crumenatum from the Different Regions of Indonesian Conservation Areas.Applied biochemistry and biotechnology · 2026Article
- Structural basis of ligand selectivity in FAD/NAD(P)H-dependent dehydrogenases: insights from trypanothione reductase and type II NADH dehydrogenase.Protein science : a publication of the Protein Society · 2026Article
- Assessing the ligand native-like pose using a quantum mechanical-derived hydropathic score for protein-ligand complementarity.npj drug discovery · 2026Article
- Molecular Dynamics Workflows to Compute Large-Scale Sets of Absolute Binding Free Energies Aiding Drug Candidate and Binding Pose Selection.Journal of chemical theory and computation · 2026Article
- Dynamic stability-driven machine learning improves binding pose identification on human serum albumin.Journal of computer-aided molecular design · 2026Article
- In silico and in vitro investigations of novel Siglec-1 inhibitors in a microglial cell model.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- PepScorer::RMSD: An Improved Machine Learning Scoring Function for Protein-Peptide Docking.International journal of molecular sciences · 2026Article
- Stability-Driven Pose Prediction and Ligand Design via Contact Persistence Analysis from Molecular Dynamics Simulations.Computational and structural biotechnology journal · 2026Article
- Phytochemical and Geographical Comparison ofScientifica · 2026Article
- Extremely Randomized Trees to Determine Binding Affinity.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Decision Tree for Prediction of Binding Affinity.Methods in molecular biology (Clifton, N.J.) · 2026Article
- AlphaFold for Docking Screens.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Neural Networks with Molegro Data Modeller.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Targeting CDK9 with Molegro Virtual Docker.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Differential Evolution for Docking Simulations.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Molegro Data Modeller to Estimate CDK6 Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Tree-Based Methods to Predict Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular docking is a widely used technique in drug discovery to predict the binding mode of a given ligand to its target. However, the identification of the near-native binding pose in docking experiments still represents a challenging task as the scoring functions currently employed by docking programs are parametrized to predict the binding affinity, and, therefore, they often fail to correctly identify the ligand native binding conformation. Selecting the correct binding mode is crucial to obtaining meaningful results and to conveniently optimizing new hit compounds. Deep learning (DL) algorithms have been an area of a growing interest in this sense for their capability to extract the relevant information directly from the protein-ligand structure. Our review aims to present the recent advances regarding the development of DL-based pose selection approaches, discussing limitations and possible future directions. Moreover, a comparison between the performances of some classical scoring functions and DL-based methods concerning their ability to select the correct binding mode is reported. In this regard, two novel DL-based pose selectors developed by us are presented.
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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 Socratic graph.