ArticleJournal of cheminformatics2025
Assessing interaction recovery of predicted protein-ligand poses.
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 16 papers.
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Who cites it
16 citing papers in PubMed.
- Teaching diffusion models physics: reinforcement learning for physically valid diffusion-based docking.Chemical science · 2026Article
- Recent Advances in the Design of Inhibitors Targeting the Viral Entry and Replication of the SARS-CoV-2 Virus, Driven by In Silico Approaches.Molecules (Basel, Switzerland) · 2026Review
- On the generalization and usability of cofolding models for GPCR drug discovery.npj drug discovery · 2026Article
- Can Cavity Prediction Algorithms Help in Docking Experiments?Journal of computational chemistry · 2026Article
- The LiPP Benchmark Set for Modeling Lipid-Protein Complexes: Comparison of Co-Folding and Docking Methods.Journal of chemical information and modeling · 2026Article
- FLOWR: flow matching for structure-aware de novo, interaction- and fragment-based ligand generation.Nature computational science · 2026Article
- Benchmarking co-folding methods to predict the structures of covalent protein-ligand complexes.Acta pharmacologica Sinica · 2026Article
- Artificial Intelligence in Drug Discovery and Development: Raising Quality per Decision.Pharmacopsychiatry · 2026Review
- A fully automated benchmarking suite to compare macromolecular complexes.Nature methods · 2026Review
- Dihydroalterperylenol from endophytic fungusMolecular medicine reports · 2026Article
- Assessing the potential of deep learning for protein-ligand docking.Nature machine intelligence · 2026Article
- Large scale prospective evaluation of co-folding across 557 Mac1-ligand complexes and three virtual screens.bioRxiv : the preprint server for biology · 2025Article
- How many crystal structures do you need to trust your docking results?bioRxiv : the preprint server for biology · 2025Article
- Decoding the limits of deep learning in molecular docking for drug discovery.Chemical science · 2025Article
- Advanced machine learning for innovative drug discovery.Journal of cheminformatics · 2025Article
- Integrating Machine Learning-Based Pose Sampling with Established Scoring Functions for Virtual Screening.Journal of chemical information and modeling · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field of protein-ligand pose prediction has seen significant advances in recent years, with machine learning-based methods now being commonly used in lieu of classical docking methods or even to predict all-atom protein-ligand complex structures. Most contemporary studies focus on the accuracy and physical plausibility of ligand placement to determine pose quality, often neglecting a direct assessment of the interactions observed with the protein. In this work, we demonstrate that ignoring protein-ligand interaction fingerprints can lead to overestimation of model performance, most notably in recent protein-ligand cofolding models which often fail to recapitulate key interactions.Scientific Contribution The interaction analysis used in this study is provided as a python package at https://github.com/Exscientia/plif_validity .
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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.