Are Language Models Better than Physical Based Models for Chemistry (and More)?

Natural language processing models in chemistry have emerged as one of the most effective approaches for capturing human knowledge and modelling creativity in organic chemistry. Its application in machine learning tasks demonstrated high quality and ease of use in problems such as predicting chemical reactions, retrosynthetic routes, digitizing chemical literature, predicting detailed experimental procedures, designing new fingerprints and yield predictions. In this presentation, I will discuss the impact of language models in chemistry by describing first the implementation of the first cloud-based AI-driven autonomous laboratory.
Finally, I will focus on the most recent applications of language models to address the problem of characterizing unknown enzymes, the development of human-in-the-loop schemes for retrosynthetic strategies or promoting sustainability and green chemistry strategies with ad-hoc AI models.