ArticleNature communications2024
De novo generation of multi-target compounds using deep generative chemistry.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
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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.
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Who cites it
40 citing papers in PubMed.
- Multi-objective optimization in the context of generative chemistry.Nature communications · 2026Review
- ActivityDiff: a diffusion model with positive and negative activity guidance for de novo drug design.Bioinformatics (Oxford, England) · 2026Article
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
- Generative pretraining for drug molecule design with bidirectional structure-property optimization.Communications chemistry · 2026Article
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- MT-ConBiFormer-GPT: multi-target molecular generation for low-data drug discovery via a contrastive BiFormer-GPT architecture and curriculum learning with cross-domain generalization.Briefings in bioinformatics · 2026Article
- Combining cutting edge computational and experimental methods for targeting KRAS mutations in non-small cell lung cancer.Expert opinion on drug discovery · 2026Review
- A multi-target drug design method based on target feature fusion.BMC bioinformatics · 2026Article
- A Reinforcement Learning-Guided Genetic Algorithm Integrating Medicinal Chemistry-Inspired Molecular Transformations.Journal of chemical information and modeling · 2026Article
- Generative Artificial Intelligence Transitions Pharmaceutical Development from Empirical Screening to Predictive Molecular Design and Clinical Trial Optimization.Pharmaceuticals (Basel, Switzerland) · 2026Review
- LaMGen: LLM-based 3D molecular generation for multi-target drug design.Nature communications · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- MolOrgGPT:Journal of chemical information and modeling · 2026Article
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Synthetic lethality in cancer drug discovery: challenges and opportunities.Nature reviews. Drug discovery · 2026Review
- The applications of single-cell multiomics in drug screening.Pharmaceutical science advances · 2025Review
- Deep Generative AI for Multi-Target Therapeutic Design: Toward Self-Improving Drug Discovery Framework.International journal of molecular sciences · 2025Review
- Dynamic Hydrogels: Adaptive Biomaterials for Engineering Tumor Microenvironment and Cancer Treatment.International journal of molecular sciences · 2025Review
- Nucleic acid therapeutics for liver diseases: A decade of technological convergence and clinical challenges.iLIVER · 2025Review
- Design, Synthesis and Biological Evaluation of Chromeno[3,4-ACS chemical neuroscience · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Polypharmacology drugs-compounds that inhibit multiple proteins-have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis. In binding data for >100,000 compounds, POLYGON correctly recognizes polypharmacology interactions with 82.5% accuracy. We subsequently generate de-novo compounds targeting ten pairs of proteins with documented co-dependency. Docking analysis indicates that top structures bind their two targets with low free energies and similar 3D orientations to canonical single-protein inhibitors. We synthesize 32 compounds targeting MEK1 and mTOR, with most yielding >50% reduction in each protein activity and in cell viability when dosed at 1-10 μM. These results support the potential of generative modeling for polypharmacology.
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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.