Accelerating Organic Synthesis with Chemical Language Models - Philippe Schwaller скачать в хорошем качестве

Accelerating Organic Synthesis with Chemical Language Models - Philippe Schwaller

If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live: https://valence-discovery.github.io/M... Title: Accelerating Organic Synthesis with Chemical Language Models Abstract: In organic chemistry, we are currently witnessing a rise in artificial intelligence (AI) approaches, which show great potential for improving molecular designs, facilitating synthesis and accelerating the discovery of novel molecules. Based on an analogy between written language and organic chemistry, we built linguistics-inspired transformer neural network models for chemical reaction prediction, synthesis planning, and the prediction of experimental actions. We extended the models to chemical reaction classification and fingerprints. By finding a mapping from discrete reactions to continuous vectors, we enabled efficient chemical reaction space exploration. Moreover, we specialized similar models for reaction yield predictions. Intrigued by the remarkable performance of chemical language models, we discovered that the models can capture how atoms rearrange during a reaction, without supervision or human labelling, leading to the development of the open-source atom-mapping tool RXNMapper. During my talk, I will provide an overview of the different contributions that are at the base of this digital synthetic chemistry revolution. Speaker: Philippe Schwaller -   / pschwllr   Twitter Prudencio:   / tossouprudencio   Twitter Therence:   / therence_mtl   Twitter Cas:   / cas_wognum   Twitter Valence Discovery:   / valence_ai   ~ Chapters: 00:00 Video begins 00:10 Introduction and background 06:50 Molecular Transformer for seq2seq modeling in chemistry (IBM RXN / RoboRXN) 20:17 Q&A 27:27 Learned reaction fingerprints (RXNFP) 30:36 Q&A 33:54 Predicting reaction yields 37:03 Atom Mapping in Molecular Transformers (RXNMapper) 43:49 Summary & conclusion 46:27 Q&A