ReviewJournal of chemical information and modeling2022
Predicting Protein-Ligand Docking Structure with Graph Neural Network.
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 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Bakh formula ameliorates high-fat diet-induced erectile dysfunction in rats via coordinated restoration of metabolic and tissue homeostasis.Sexual medicine · 2026Article
- Degrading GPX4 via Biomimetic Nanoparticle-Mediated In Situ Synthesis of Deep-Learning-Designed Binder-Degron Chimeras for Prostate Cancer Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Tightly coupled equivariant flow matching for molecular docking with multimodal physical constraints.Molecular diversity · 2026Article
- Predicting Surfactant Oil-Water Interfacial Tension Using Gated Message-Passing Graph Neural Networks.ACS omega · 2026Article
- DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity.BMC bioinformatics · 2026Article
- Natural Products in Epilepsy Treatment: From Traditional Medicine Towards Computational Drug Discovery.Current issues in molecular biology · 2026Review
- NMR-Based Fragment Screening for RNA-Targeted Drug Discovery.Molecules (Basel, Switzerland) · 2026Review
- Prediction of Protein-Ligand Binding Affinities Using Atomic Surface Site Interaction Points.Journal of chemical information and modeling · 2026Article
- Graph neural networks driven acceleration in drug discovery.Acta pharmaceutica Sinica. B · 2025Review
- Building Explainable Graph Neural Network by Sparse Learning for the Drug-Protein Binding Prediction.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- Artificial intelligence-enabled discovery of a RIPK3 inhibitor with neuroprotective effects in an acute glaucoma mouse model.Chinese medical journal · 2025Article
- Drug Discovery and Design through Computational Innovations.Current computer-aided drug design · 2025Article
- Improved Prediction of Ligand-Protein Binding Affinities by Meta-modeling.Journal of chemical information and modeling · 2024Article
- AI-integrated network for RNA complex structure and dynamic prediction.Biophysics reviews · 2024Review
- Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.Computational and structural biotechnology journal · 2024Review
- OpenDock: a pytorch-based open-source framework for protein-ligand docking and modelling.Bioinformatics (Oxford, England) · 2024Article
- Exploring the structural landscape of DNA maintenance proteins.Nature communications · 2024Article
- EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models.Protein science : a publication of the Protein Society · 2024Article
- RhoMax: Computational Prediction of Rhodopsin Absorption Maxima Using Geometric Deep Learning.Journal of chemical information and modeling · 2024Article
- A new paradigm for applying deep learning to protein-ligand interaction prediction.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both low accuracy and high latency. A few recent machine learning-based approaches have been proposed for virtual screening by improving the ability to evaluate protein-ligand binding affinity, but such methods rely heavily on conventional docking software to sample docking poses, which results in excessive execution latencies. Here, we propose and evaluate a novel graph neural network (GNN)-based framework, MedusaGraph, which includes both pose-prediction (sampling) and pose-selection (scoring) models. Unlike the previous machine learning-centric studies, MedusaGraph generates the docking poses directly and achieves from 10 to 100 times speedup compared to state-of-the-art approaches, while having a slightly better docking accuracy.
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What OpenQuestion holds
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.