Evidence map›Paper›PMID 40025560›Full record

ArticleJournal of cheminformatics2025

GNINA 1.3: the next increment in molecular docking with deep learning.

Andrew T McNutt, Yanjing Li, Rocco Meli, Rishal Aggarwal, David Ryan Koes

Abstract read
In one paragraph

Article in Journal of cheminformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.

0numbers the graph read from it
0cells of the map it votes in
82citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

82 citing papers in PubMed.

  1. Review
  2. Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026
    Review
  3. Article
  4. Article
  5. Robots and Minimal, Physics-Informed Features: A Hybrid Framework for Enzyme Catalysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. EasyDock 1.3: An Automated Pipeline for Molecular Docking.Journal of chemical information and modeling · 2026
    Article
  17. Article
  18. Article
  19. Article
  20. Article

22 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Andrew T McNuttDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
Yanjing LiComputational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Rocco MeliDepartment of Biochemistry, University of Oxford, Oxford, OX1 3QU, UK.
Rishal AggarwalDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
David Ryan KoesDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA. dkoes@pitt.edu.

Funding

New Methods and Tools for Computational Drug Discovery: Diversity SupplementR35GM140753 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KOES, DAVID RYAN · 2021 to 2025
$2.2M
Biotechnology and Biological Sciences Research Council BB/S50760X/1Evotec (UK) Interdisciplinary Biosciences DTP BB/MO11224/1NIGMS NIH HHS R35 GM140753NIH HHS R35GM140753
6 · The paper itself

Abstract

Computer-aided drug design has the potential to significantly reduce the astronomical costs of drug development, and molecular docking plays a prominent role in this process. Molecular docking is an in silico technique that predicts the bound 3D conformations of two molecules, a necessary step for other structure-based methods. Here, we describe version 1.3 of the open-source molecular docking software GNINA. This release updates the underlying deep learning framework to PyTorch, resulting in more computationally efficient docking and paving the way for seamless integration of other deep learning methods into the docking pipeline. We retrained our CNN scoring functions on the updated CrossDocked2020 v1.3 dataset and introduce knowledge-distilled CNN scoring functions to facilitate high-throughput virtual screening with GNINA. Furthermore, we add functionality for covalent docking, where an atom of the ligand is covalently bound to an atom of the receptor. This update expands the scope of docking with GNINA and further positions GNINA as a user-friendly, open-source molecular docking framework. GNINA is available at https://github.com/gnina/gnina .Scientific contributions: GNINA 1.3 is an open source a molecular docking tool with enhanced support for covalent docking and updated deep learning models for more effective docking and screening.

Indexed as

Deep learningMolecular dockingStructure-based drug design

Identifiers

PMID40025560
PMCPMC11874439

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.