Articlenpj drug discovery2025
Augmented BindingNet dataset for enhanced ligand binding pose predictions using deep learning.
Article in npj drug discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- FLOWR.ROOT - A flow matching-based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction.Nature communications · 2026Article
- More protein-ligand data are needed for AlphaFold-like models to enable drug discovery.Current opinion in structural biology · 2026Review
- ActivityFinder: Toward the Fully Automatic Integration of Structural and Binding Affinity Data.Journal of chemical information and modeling · 2026Article
- Can AI-Predicted Complexes Teach Machine Learning to Compute Drug Binding Affinity?Journal of chemical information and modeling · 2025Article
- Structure-Based Generation of 3D Small-Molecule Drugs: Are We There Yet?Journal of medicinal chemistry · 2025Article
- Article
- Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects.RSC medicinal chemistry · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
High-quality data on protein-ligand complex structures and binding affinities are crucial for structure-based drug design. Existing datasets often lack diversity and quantity, limiting the comprehensive understanding of protein-ligand interactions. Here, we present BindingNet v2, an expanded dataset comprising 689,796 modeled protein-ligand binding complexes across 1794 protein targets. Constructed using an enhanced template-based modeling workflow from BindingNet v1, it incorporates pharmacophore and molecular shape similarities. BindingNet v2's effectiveness in binding pose generation was evaluated, showing an improved generalization ability of Uni-Mol model for novel ligands. The success rate on the PoseBusters dataset increased from 38.55% with the PDBbind dataset alone to 64.25% with augmenting BindingNet v2. Coupled with physics-based refinement, the success rate rose to 74.07%, passing PoseBusters validity checks. These results highlight the value of larger, diverse datasets in enhancing the accuracy and reliability of deep learning models for binding pose prediction.
Identifiers
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.