Accelerating Organic Synthesis with Chemical Language Models - Philippe Schwaller

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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
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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