ArticleNature communications2025
Identification of nanomolar adenosine A
Article in Nature communications, 2025. 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.
- A critical appraisal of structure-based virtual screening, binding free energy methods, and AI-driven molecular design across the evolving computational landscape of drug discovery.Journal of computer-aided molecular design · 2026Review
- Multi-objective optimization in the context of generative chemistry.Nature communications · 2026Review
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Automated Parallel Synthesis Accelerates Virtual Screening Hit Discovery.Journal of the American Chemical Society · 2026Article
- Applying Deep-Learning-DrivenJournal of medicinal chemistry · 2026Article
- 3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table.Journal of chemical information and modeling · 2026Article
- Novel molecular design via a scaffold-aware transformer with multi-scale attention mechanisms.Journal of cheminformatics · 2026Article
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Generative AI in structure-based drug discovery.BMC biology · 2026Review
- REINFORCE-ING Chemical Language Models for Drug Discovery.Journal of chemical information and modeling · 2025Article
- MDPath: Unraveling Allosteric Communication Paths of Drug Targets through Molecular Dynamics Simulations.Journal of chemical information and modeling · 2025Article
- Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.npj drug discovery · 2025Review
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 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
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative chemical language models (CLMs) have demonstrated success in learning language-based molecular representations for de novo drug design. Here, we integrate structure-based drug design (SBDD) principles with CLMs to go from protein structure to novel small-molecule ligands, without a priori knowledge of ligand chemistry. Using Augmented Hill-Climb, we successfully optimise multiple objectives within a practical timeframe, including protein-ligand complementarity. Resulting de novo molecules contain known or promising adenosine A
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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.