ArticleBeilstein journal of organic chemistry2026
Using generative AI to transform peptide hits into small molecule leads.
Article in Beilstein journal of organic chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a wealth of structural data on peptides that bind potential drug targets, serving as rich inspiration for designing small molecule inhibitors which can recapitulate key binding interactions while improving pharmacokinetic properties. With the rapid advancement of artificial intelligence capabilities including powerful generative models, there is significant potential for new AI-based tools to expedite the structure-based transformation of peptide hits into small molecule leads. In this Perspective, we highlight how AI-enabled prediction and design tools can potentially span the entire workflow from peptide to small molecule: target protein structure prediction, de novo peptide binder generation, diffusion models for generating novel small molecule scaffolds, and deep-learning predictors of binding affinity to rapidly triage candidates.
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.