Patent Data for Artificial Intelligence based Drug Discovery
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Standigm Inc., founded by researchers from Samsung Advanced Institute of Technology, specializes in AI-driven drug discovery by generating and optimizing therapeutic compounds. The company emphasizes its innovative platforms for novel target identification and chemical space exploration, while leveraging advanced machine learning techniques and patent strategies. Key resources and technologies discussed include proprietary AI models, chemical databases, and OCR enhancements for patent analysis.
Patent Data for Artificial Intelligence based Drug Discovery
- 1. AI Drug Discovery in Patent Space Hanjo Kim Principal Scientist at Standigm Inc. hanjo.kim@standigm.com business@standigm.com apply@standigm.com www.standigm.com
- 2. Disclaimer • Statements of fact and opinions expressed in this presentation and on the following slides are solely those of the presenter and not necessarily those of Standigm Inc.
- 3. Standigm Inc. 2015 Founded by three researchers at Samsung Advanced Institute of Technology Jinhan Kim, PhD Artificial Intelligence (The University of Edinburgh) Sang Ok Song, PhD Chemical Engineering (Seoul National University) So Jeong Yun, PhD Systems Biology (POSTECH) $23M Funding raised SK Holdings, Mirae Asset Capital, Mirae Asset Venture Investment, DSC Investment, Wonik Investment, Atinum Investment, LB Investment, Kakao Ventures Seoul Korea (33) Ann Arbor Michigan (2) Standigm= drug discovery company that generates and optimizes therapeutic lead compounds by using advanced artificial intelligence toward license-out Cambridge UK (1) AI, 16 Biology, 6 Chemistry, 8 Systems Biology, 4 Advisor, 3 PhD 20/37* * Except Operation 5, Patent attorney 1
- 4. The AI solution Disease Hit Lead Preclinical Clinical Drug Drug repositioning The Standigm AI solution is industrializing drug discovery Discovery at Scale Target * developing BEST TM ASK TM Insight TM FIRST * Standigm ASKTM is freely available at https://icluenask.standigm.com
- 5. Standigm BEST Platform Standigm BESTStandigm ASK Knowledge based biology platform for novel targets, pathways, and MoA discovery Standigm FIRST Hit generation platform for novel and/or undruggable targets Generative Models Graph-based VAE Scaffold-based conditional enumerator Novel Molecular Representation Scoring Functions Simulations AI rescoring models Machine learning models Compound Database Known Molecules Seed Molecules Novel Virtual Structures Commercial Library Privileged Standigm Library Target Database Public data (gene, protein, function) BEST Feasibility Public Library Strategy setup Hit Generation Hit-2-Lead Predictive Models ADME/Tox predictors Novelty (patentability) Synthetic accessibility Filters/Ranking models External CROs Organic synthesis, In vitro/in vivo Assays Novel/Commercial Hits Lead Series
- 6. Graph-based VAE Chemical space Encoder Decoder Latent space Chemical space E DZ Learning chemical space Training DB ~4M Y Property/Target information Contextualizing: - substructures - topology - shape - etc property 1 property 2 property 3 Z : latent space predictor q(y|z) seed molecules decoder p(x|z) X : original chemical space encoder q(z|x) Analogue structure generation functionally similar but novel scaffolds/molecules Lead optimization novel molecules w/ better desired properties decoder p(x|z) Smart library expansion IP generation & expansion
- 7. Patent Space Target A Compounds in latent space Competitor 1 Competitor 2 Competitor 3 Interesting Area potentweak
- 8. Chemical Space Navigation • Chemical Space ~ Map • Known scaffolds ~ POIs • Information-rich space (ChEMBL, PubChem Bioassays, etc.) • Novel scaffold ~ New POI • El Dorado • Patent • Markush structure: How to protect as wide as possible area • Exemplified compounds: boundary stones
- 9. Using ChemCurator • Project types • Google Patents (most cases) • PDF files (do not use pdf files!) • Text files (when google ocr is not good)
- 12. OCR (and chemical OCR) • Lessons • Google patents is reliable in most cases • It even provides the compound table though very primitive • Professional OCR software can give better results • Convert pdf file to plain text with chemical names • Complex tables • Image (not OCRed) tables (next 3 slides) • Chemical OCR engine helps a lot • Text-image comparison • Chemical OCR engines • CLiDE (recommended, proprietary) • Osra (open-source, recommended on Linux machine) • Imago (I have no experience) • Unsupported engines (like ChemGrapher, https://pubs.acs.org/doi/10.1021/acs.jcim.0c00459)
- 13. Chemical structures in patens
- 14. Chemical structures in patents
- 15. Chemical structures in patents
- 17. Markush Structures • Very expressive • Same set of compounds can be written to very different forms • Not well-validated • ChemCurator helps • Extracting example compounds • Matching them to the Markush structure • Require manual correction • Sentence to chemical groups • Ambiguous/incomplete R-group definitions
- 18. AI can help • Reduction of frequent text OCR error • NLP technique can correct frequent OCR errors • The availability of large training set is important • Extraction of relevant data • Biological activities • Analytical data • Chemical OCR can be improved • AI can do image recognition very well • Different drawing styles can be managed
- 19. Acknowledgement • Standigm Inc. • Sanghyung JIN, Minkyu HA, Soyeon Kim, Sangok SONG • T&J Tech. (Korean distributor) • Jung-A HAN