ArticleBriefings in bioinformatics2024
Protein language models are performant in structure-free virtual screening.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- TRPM8 virtual screening: the essential role of target-specific, inactive-enriched machine-learning scoring functions.Digital discovery · 2026Article
- Multimodal deep learning with a joint uncertainty quantification scheme for drug-target interaction prediction.Molecular diversity · 2026Article
- Atomic-level protein-ligand recognition with PBCNet2.0 for probe discovery.Nature chemical biology · 2026Article
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- BindPred: a framework for predicting protein-protein binding affinity from language model embeddings.Bioinformatics (Oxford, England) · 2026Article
- Article
- Structure-free drug-target affinity prediction using protein and molecule language models.Journal of cheminformatics · 2026Article
- Multimodal out-of-distribution individual uncertainty quantification enhances binding affinity prediction for polypharmacology.Nature machine intelligence · 2025Article
- Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction.Journal of chemical information and modeling · 2025Article
- Hierarchical affinity landscape navigation through learning a shared pocket-ligand space.Patterns (New York, N.Y.) · 2025Article
- Simpatico: accurate and ultra-fast virtual drug screening with atomic embeddings.bioRxiv : the preprint server for biology · 2025Article
- The role of bioinformatics algorithms in modern biopharmaceutical design: Progress, challenges, and future perspectives.BioImpacts : BI · 2025Article
- STAG-LLM: Predicting TCR-pHLA binding with protein language models and computationally generated 3D structures.Computational and structural biotechnology journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Hitherto virtual screening (VS) has been typically performed using a structure-based drug design paradigm. Such methods typically require the use of molecular docking on high-resolution three-dimensional structures of a target protein-a computationally-intensive and time-consuming exercise. This work demonstrates that by employing protein language models and molecular graphs as inputs to a novel graph-to-transformer cross-attention mechanism, a screening power comparable to state-of-the-art structure-based models can be achieved. The implications thereof include highly expedited VS due to the greatly reduced compute required to run this model, and the ability to perform early stages of computer-aided drug design in the complete absence of 3D protein structures.
Indexed as
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.