ReviewJournal of chemical information and modeling2022
Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel-Ligand Docking.
Review in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- 3D Geometric Equivariant Graph Neural Networks with Contrastive Learning for Protein-Ligand Binding Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Robustness of Protein-Ligand Binding Affinity Prediction Models to Docked and Predicted Structures.Journal of chemical information and modeling · 2026Article
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Dynamic stability-driven machine learning improves binding pose identification on human serum albumin.Journal of computer-aided molecular design · 2026Article
- Exploring the antibacterial mechanism and application of lavender essential oil based on network pharmacology and molecular docking.Scientific reports · 2026Article
- An efficient ranking deep neural network algorithm for the prediction of Ca2+ binding sites of the protein.PloS one · 2026Article
- RankMHC: Learning to Rank Class-I Peptide-MHC Structural Models.Journal of chemical information and modeling · 2024Article
- Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.Computational and structural biotechnology journal · 2024Review
- Building shape-focused pharmacophore models for effective docking screening.Journal of cheminformatics · 2024Article
- PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications.Scientific data · 2024Article
- Protein-ligand binding affinity prediction using multi-instance learning with docking structures.Frontiers in pharmacology · 2024Article
- Integrated Molecular Modeling and Machine Learning for Drug Design.Journal of chemical theory and computation · 2023Review
- High-throughput screening strategies for space-based radiation countermeasure discovery.Life sciences in space research · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early stage drug discovery. With the recent increase in available small-molecule databases, virtual high-throughput screening using physics-based molecular docking has emerged as an essential tool in assisting fast and cost-efficient lead discovery and optimization. However, the best scored docking poses are often suboptimal, resulting in incorrect screening and chemical property calculation. We address the pose classification problem by leveraging data-driven machine learning approaches to identify correct docking poses from AutoDock Vina and Glide screens. To enable effective classification of docking poses, we present two convolutional neural network approaches: a three-dimensional convolutional neural network (3D-CNN) and an attention-based point cloud network (PCN) trained on the PDBbind
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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.