ArticleNature communications2025
Accelerating discovery of bioactive ligands with pharmacophore-informed generative models.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
What it found
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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
11 citing papers in PubMed.
- Innovative strategies for anti-fibrotic drugs discovery from traditional Chinese medicine.Chinese herbal medicines · 2026Review
- NaviDiv: a web app for monitoring chemical diversity in generative molecular design.Digital discovery · 2026Article
- NumMolFormer: an explicit functional group number-guided framework for structure-based drug design.Bioinformatics (Oxford, England) · 2026Article
- A novel approach for enhancing the potency of kinase inhibitors using topological water networks.Journal of cheminformatics · 2026Article
- Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery.Cell biochemistry and biophysics · 2026Review
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- SHARP: Generating Synthesizable Molecules via Fragment-Based Hierarchical Action-Space Reinforcement Learning for Pareto Optimization.Journal of chemical information and modeling · 2025Article
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- Comparative effect of gibberellic acid and brassinolide for mitigating drought stress in pea (Physiology and molecular biology of plants : an international journal of functional plant biology · 2025Article
- The recent advances in the approach of artificial intelligence (AI) towards drug discovery.Frontiers in chemistry · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
Deep generative models have advanced drug discovery but often generate compounds with limited structural novelty, providing constrained inspiration for medicinal chemists. To address this, we develop TransPharmer, a generative model that integrates ligand-based interpretable pharmacophore fingerprints with a generative pre-training transformer (GPT)-based framework for de novo molecule generation. TransPharmer excels in unconditioned distribution learning, de novo generation, and scaffold elaboration under pharmacophoric constraints. Its unique exploration mode could enhance scaffold hopping, producing structurally distinct but pharmaceutically related compounds. Its efficacy is validated through two case studies involving the dopamine receptor D2 (DRD2) and polo-like kinase 1 (PLK1). Notably, three out of four synthesized PLK1-targeting compounds show submicromolar activities, with the most potent, IIP0943, exhibiting a potency of 5.1 nM. Featuring a new 4-(benzo[b]thiophen-7-yloxy)pyrimidine scaffold, IIP0943 also has high PLK1 selectivity and submicromolar inhibitory activity in HCT116 cell proliferation. TransPharmer offers a promising tool for discovering structurally novel and bioactive ligands.
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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.