--- title: "POLYGON: Generative reinforcement learning model generates compounds that target multiple proteins" description: Explore the groundbreaking POLYGON model, a generative AI and reinforcement learning platform that designs novel compounds targeting multiple proteins simultaneously. Uncover its potential in drug discovery, its success in multi-targeted therapy, and its promising applications in addressing complex diseases through polypharmacology. Learn how POLYGON navigates the challenges of drug design with a remarkable 82% accuracy rate in identifying dual-target compoundsExplore the groundbreaking POLYGON model, a generative AI and reinforcement learning platform that designs novel compounds targeting multiple proteins simultaneously. Uncover its potential in drug discovery, its success in multi-targeted therapy, and its promising applications in addressing complex diseases through polypharmacology. Learn how POLYGON navigates the challenges of drug design with a remarkable 82% accuracy rate in identifying dual-target compounds image: https://info.genophore.com/hubfs/polygon.svg --- [Skip to content](https://info.genophore.com/posts/polygon-generative-reinforcement-learning-model-generates-compounds-that-target-multiple-proteins#main-content) [![Genophore](https://info.genophore.com/hubfs/Imported%20sitepage%20images/203149784d2ad8223028.svg "Genophore")](https://www.genophore.com) - Products - [Molecular Biology Suite](https://genophore.com/products/molecular_biology_suite/) - [Protein Sciences and Antibody Discovery](https://genophore.com/products/protein_sciences_and_antibody_discovery/) - [Structural Enablement](https://genophore.com/products/structural_enablement/) - [Connecting Biological and Chemical Knowledge](https://genophore.com/products/connecting_biological_and_chemical_knowledge/) - [Electronic Lab Notebook](https://genophore.com/products/electronic_lab_notebook/) - Technologies - [Data and Sequence Visualization](https://genophore.com/technologies/data_and_sequence_visualization/) - [Protein and DNA Parts Builder](https://genophore.com/technologies/protein_and_dna_parts_builder/) - [Workflows Builder](https://genophore.com/technologies/workflows_builder/) - [Customizable Data Structures](https://genophore.com/technologies/customizable_data_structures/) - Solutions - [Biopharmaceuticals & Biotech R&D](https://genophore.com/technologies/biotech_research_and_development/) - [Life Sciences](https://genophore.com/technologies/life_sciences/) - [Pricing](https://genophore.com/pricing/) - [Tools](https://info.genophore.com/tools) - [Blog](https://info.genophore.com/blog) - [Contact Us](https://genophore.com/contact/) - [Try Now](https://www.genophore.com/app) - [Chemistry](https://info.genophore.com/posts/tag/chemistry) - [Generative Design](https://info.genophore.com/posts/tag/generative-design) # POLYGON: Generative reinforcement learning model generates compounds that target multiple proteins May 12, 2024 ![Genophore](https://info.genophore.com/hubfs/Genophore_Profile_Logo_2024.png) [Genophore](https://info.genophore.com/posts/author/genophore) Munson et al. recently reported POLYGON, a model for de novo generation of compounds that inhibit multiple proteins. POLYGON employs generative AI and reinforcement learning to design novel structures by sampling from an embedded chemical space. The rewarded, desired qualities are predicted inhibitory potential against each of two protein targets as well as drug-likeness and synthetizability. Traditionally, drug discovery has focused on inhibitors targeting single proteins. While effective, this approach may not be ideal for all diseases. Some conditions might benefit more from targeting multiple proteins that contribute partially to the illness. This has led to a growing interest in developing multi-targeted therapies. One approach is to use combination therapy (combining multiple drugs), and another is the so-called polypharmacology, which involves the use of a single drug that modulates multiple targets simultaneously. Although polypharmacology is an interesting strategy with many potential advantages over combination therapy, the challenge of designing such multi-target drugs has been a major limiting factor. In a benchmark comprising 109,811 compounds (1850 targets) from the BindingDB data set, POLYGON was able to classify compounds with activity on two targets at an accuracy of ~82%.  Further, POLYGON was applied to generate compounds targeting ten pairs of known co-dependent (i.e. synthetic lethal) protein targets including Ser/Thr kinases, Tyr kinases, DNA binding factors, and histone modifiers. The top predicted structures showed low binding energy to their corresponding pairs of targets according to molecular docking analysis. In addition, the top structures showed comparable 3D poses to known single-target inhibitors. The authors synthesized and experimentally tested 32 POLYGON-generated de novo compounds designed for dual inhibition of MEK1 and mTOR. Most of the compounds had IC50s in the 1–10 μM range and lowered the in vitro and cellular (at 1-10 μM) activity of each target by more than 50%. Further, four of the well-validated POLYGON compounds were evaluated for specificity by assessing off-target inhibition of three representative unrelated kinases. All the compounds showed no more than 20% inhibition except for one compound with 38% inhibition towards one off-target kinase. ### Open questions ❓ Can this approach be used for the discovery of PROTACs and molecular glues, e.g. to recruit an E3 ligase to a protein of interest? ❓How well will POLYGON translate to other drug targets and disease areas that have  different protein interactions and functionalities? ### References and Resources Paper: [De novo generation of multi-target compounds using deep generative chemistry](https://www.nature.com/articles/s41467-024-47120-y) Code: [POLYpharmacology Generative Optimization Network (POLYGON) a VAE for de novo polypharmacology.](https://github.com/bpmunson/polygon) Blog Post ## Related Articles [![](https://info.genophore.com/hubfs/machine-learning-design-1.svg)](https://info.genophore.com/posts/machine-learning-designs-potent-dual-peptide-agonists-of-gcgr-and-glp-1r-are-fourth-generation-weight-loss-drugs-around-the-corner) ### [Machine Learning Designs Potent Dual Peptide Agonists of GCGR and GLP-1R: Are Fourth Generation Weight Loss Drugs Around the Corner?](https://info.genophore.com/posts/machine-learning-designs-potent-dual-peptide-agonists-of-gcgr-and-glp-1r-are-fourth-generation-weight-loss-drugs-around-the-corner) June 1, 2024 [ML/AI](https://info.genophore.com/posts/tag/ml-ai) [Drug Discovery](https://info.genophore.com/posts/tag/drug-discovery) [Pharmaceuticals](https://info.genophore.com/posts/tag/pharmaceuticals) [Bioinformatics](https://info.genophore.com/posts/tag/bioinformatics) [![](https://info.genophore.com/hubfs/Thumbnail%20Themes%20(13)-1.svg)](https://info.genophore.com/posts/molx-a-third-generation-3d-biomolecule-visualization-platform) ### [MolX: Towards a 3rd Generation 3D Biomolecule Visualization Platform](https://info.genophore.com/posts/molx-a-third-generation-3d-biomolecule-visualization-platform) June 17, 2024 [Protein Design](https://info.genophore.com/posts/tag/protein-design) [Virtual Screening](https://info.genophore.com/posts/tag/virtual-screening) [Chemistry](https://info.genophore.com/posts/tag/chemistry) [Docking](https://info.genophore.com/posts/tag/docking) [Structure Prediction](https://info.genophore.com/posts/tag/structure-prediction) [News](https://info.genophore.com/posts/tag/news) [Generative Design](https://info.genophore.com/posts/tag/generative-design) [Molecular Dynamics](https://info.genophore.com/posts/tag/molecular-dynamics) [Drug Discovery](https://info.genophore.com/posts/tag/drug-discovery) [Biologics](https://info.genophore.com/posts/tag/biologics) [Bioinformatics](https://info.genophore.com/posts/tag/bioinformatics) [![](https://info.genophore.com/hubfs/Thumbnail%20Themes%20(24).svg)](https://info.genophore.com/posts/carbondesign-inverting-alphafold2-for-the-inverse-folding-problem) ### [CarbonDesign: Inverting AlphaFold2 for the Inverse Folding Problem](https://info.genophore.com/posts/carbondesign-inverting-alphafold2-for-the-inverse-folding-problem) June 1, 2024 [Protein Design](https://info.genophore.com/posts/tag/protein-design) [Structure Prediction](https://info.genophore.com/posts/tag/structure-prediction) [Generative Design](https://info.genophore.com/posts/tag/generative-design) [Bioinformatics](https://info.genophore.com/posts/tag/bioinformatics) ##### Genophore - 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