Cloud-based AI-driven autonomous lab

Abstract

Creating and designing new molecules is one of chemistry's most significant outcomes. The application of domain knowledge gained over decades of laboratory experience has been critical in the synthesis of numerous new molecular structures. Nonetheless, most synthetic success stories are preceded by lengthy hours of unproductive repetitive experiments. Automation systems, which were developed less than two decades ago to assist chemists with repetitive laboratory operations, have helped to reduce this problem. While these technologies have demonstrated exceptional efficacy in a select fields, such as high-throughput chemistry, automating general-purpose jobs remains an extremely complex issue even today. It requires chemical operators to develop unique software for various operations, each of which codifies a distinct sort of chemistry. Natural language processing models have emerged as one of the most effective, scalable approaches for capturing human knowledge and modelling chemical processes in organic chemistry. Its use in machine learning tasks demonstrated high quality and ease of use in problems such as predicting chemical reactions [1-2], retrosynthetic routes [3], digitizing chemical literature [4], predicting detailed experimental procedures [5], designing new fingerprints [6] and yield predictions [7]. In this talk, I'll talk about the impact of language models in chemistry by highlighting the critical role of NLP architectures in implementing the first cloud-based AI-driven autonomous laboratory [8]. The remote laboratory is made accessible to chemists through the cloud [6] and is equipped with automation technologies. The AI assists remote chemists with several tasks: designing retrosynthetic trees and suggesting the correct sequence of operational actions (reaction conditions and procedures), or ingesting literature on synthetic procedures to convert them into an executable program. Following supervision by synthetic chemists, the AI self-programs the automation layer and makes decisions on the synthesis execution using feedback loops from analytical chemistry instruments. I will present the platform architecture and its performance across various classes of synthetic tasks.