ReviewNature reviews. Chemistry2025
Protein-ligand data at scale to support machine learning.
Review in Nature reviews. Chemistry, 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.
- PLATE-VS: a web server for protein-ligand assay curation and cross-target virtual screening datasets.Nucleic acids research · 2026Article
- Allosteric PROTACs: Expanding the Horizon of Targeted Protein Degradation.Journal of the American Chemical Society · 2026Review
- RP3Net: a deep learning model for predicting recombinant protein production in Escherichia coli.Bioinformatics (Oxford, England) · 2026Article
- Enantioselective protein affinity selection mass spectrometry (E-ASMS).Nature communications · 2025Article
- GatorAffinity: Boosting Protein-Ligand Binding Affinity Prediction with Large-Scale Synthetic Structural Data.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
- Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches.npj drug discovery · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Target 2035 is a global initiative that aims to develop a potent and selective pharmacological modulator, such as a chemical probe, for every human protein by 2035. Here, we describe the Target 2035 roadmap to develop computational methods to improve small-molecule hit discovery, which is a key bottleneck in the discovery of chemical probes. Large, publicly available datasets of high-quality protein-small-molecule binding data will be created using affinity-selection mass spectrometry and DNA-encoded chemical library screening. Positive and negative data will be made openly available, and the machine learning community will be challenged to use these data to build models and predict new, diverse small-molecule binders. Iterative cycles of prediction and testing will lead to improved models and more successful predictions. By 2030, Target 2035 will have identified experimentally verified hits for thousands of human proteins and advanced the development of open-access algorithms capable of predicting hits for proteins for which there are not yet any experimental data.
Indexed as
Identifiers
40702244What 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.