ReviewCurrent opinion in structural biology2026
More protein-ligand data are needed for AlphaFold-like models to enable drug discovery.
Review in Current opinion in structural biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Physical priors improve performance of structure-based binding affinity models.bioRxiv : the preprint server for biology · 2026Article
- Kinase inhibitors can change protonation or tautomeric state upon binding.bioRxiv : the preprint server for biology · 2026Article
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
Structure-based drug design (SBDD) is an evolving paradigm that leverages protein structural information to improve small molecule therapeutic design. Building on more than 50 years of data curation from the Protein Data Bank, the recent emergence of protein structure prediction models (PSPMs) promises to enable new computationally driven approaches for therapeutic discovery. However, it is critical to assess the limitations of these models using blind challenges and to expand existing datasets to better reflect real-world drug design tasks. Here, we discuss recent efforts to benchmark existing PSPMs and identify their limitations. We offer a hierarchical framework for parsing which tasks the current models perform well, and which tasks remain challenging or unexplored. Finally, we emphasize the need for systematic dataset generation to support the development of frontier models and highlight recent efforts to generate experimental and physics-based datasets for challenging tasks in drug discovery.
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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.