ArticleJournal of cheminformatics2021
GNINA 1.0: molecular docking with deep learning.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 386 papers.
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Who cites it
386 citing papers in PubMed.
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- Leveraging high-throughput proteomics and AI-based protein folding to accelerate VAV1 molecular glue discovery.Nature communications · 2026Article
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
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- Drude SILCS-Nucleic: harnessing explicit electronic polarization in targeting RNA and DNA for drug design.Nucleic acids research · 2026Article
- Controllable molecular generation with fine-tuned flow-matching model.Communications chemistry · 2026Article
- Data driven selection of consensus docking pipelines for structure based hit identification.npj drug discovery · 2026Article
- Integrated Phytochemical, Computational and In Vitro Evaluation of the Oocysticidal Activity of Pleurotus ostreatus Extract Against Cryptosporidium parvum.Cell biochemistry and biophysics · 2026Article
326 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. GNINA, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of GNINA under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina .
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