Discovery & Synthesis Chemputer: An intelligent universal system for automated chemical synthesis and discovery across different hardware and scales

University Of Glasgow, Scotland

Investigators

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Abstract

Project Summary In this supplement to the collaborative project initiated between the Digital Chemistry Group at the University of Glasgow and The NCATS ASPIRE laboratory we will deepen the integration of the χDL chemical programming language with the Open Reaction Database (ORD) as well as integrating Large Language Model (LLM)-based AI approaches into the generation of χDL procedures directly from retrosynthetic analyses of target compounds. This work will be accomplished during the term of the original grant. Two specific aims are proposed: 1. Develop a χDL to ORD bridge which can be instantiated on a chemputer-based physical synthesis platform. (Coley Lab collaboration); 2. Integrate large language models (LLMs) within the Chemical Description Language (χDL) framework to generate develop and interface χDLs for closed-loop active learning infrastructures (Chopra Lab collaboration). These aims will be developed over the term of the funding in a highly integrated and collaborative working modus operandi. For specific aim one we will develop a set of converters bridging the three stages of the experimental life cycle: planning, execution, and reporting. This is achieved by integrating the planning and reporting stages, which can be fully represented by the structured data schema of the ORD, with the central stage of execution, which is fully expressible in χDL. These converters will include some level of inference, through heuristics or otherwise, to fill in procedural details that might not be explicitly defined in the original plan. They can also validate if a plan can be executed in a particular lab in terms of hardware compatibility. We will realize such converters as open-source software tools and test these tools on a chemputer hardware platform for a set of benchmark reactions. For specific aim two we will develop an extension to our Natural Language Processing (NLP) approach to χDL procedure generation by using generated data sets to train a LLM AI system to be able to produce χDL instruction files directly from retrosynthetic analysis of a desired molecular structure. This will be accomplished by building a custom set of LLM agents designed to utilize the χDL NLP model to interpret and write valid χDL code based on user input. By integrating these with the χDL blueprints which are being developed for benchmark reactions as part of the NCATS ASPIRE collaboration, these χDL instructions can then be generated from automated retrosynthetic analysis of a given molecule, or class of molecules even if the suggested reactions do not yet exist in the chemical literature. We will produce specifications for a further LLM based AI system to interpret the data generated by automatic analysis and to suggest new subsequent experiments based on a pre-defined fitness function optimization (for example yield or purity of products) which can be defined experimentally in the automated system.