Researchers Introduce ChemCrow For Augmenting Large-Language Models With Chemistry Tools

Natural language processing automation brought forth by Language Language Models (LLMs) during the past few years has had far-reaching effects across many industries. It has now been applied to various NLP applications with impressive few-shot and zero-shot results. Recently, advancements have been made based on the Transformer architecture, originally developed for neural machine translation. 

Even yet, it’s important to remember that LLMs have their limits and have trouble learning things like elementary arithmetic and chemical calculations. The fundamental structure of the models, which is centered on predicting upcoming words, is responsible for these drawbacks. One way to overcome these restrictions is to supplement extensive language models with additional third-party software.

Expert-designed artificial intelligence (AI) systems that tackle specific problems have impacted the field of chemistry, specifically in reaction prediction, retrosynthesis planning, molecular property prediction, materials design, and, most recently, Bayesian Optimization. It has been demonstrated that code-generating LLMs do have some comprehension of chemistry12 due to the nature of their training. The high experimental and sometimes artisanal nature of chemistry and the restricted scope and applicability of computational tools, even within their specified regions. Tools like RXN for Chemistry and AIZynthFinder are examples of closed settings where integration is common, made possible by corporate mandates prioritizing integration and internal use. 

Researchers at the Laboratory of Artificial Chemical Intelligence (LIAC), National Centre of Competence in Research (NCCR) Catalysis, and the University of Rochester present ChemCrow, an LLM-powered chemistry engine that draws inspiration from similar successful applications in other fields. It is meant to simplify the reasoning process for many typical chemical jobs in areas like drug and materials design and synthesis. By providing an LLM (GPT-4 in our trials) with task- and format-specific prompts, ChemCrow can leverage the power of a wide range of chemistry-specific expert-designed tools. The LLM is given a list of tools, a brief explanation of their purpose, and information regarding the data input and output.

The model is instructed to use the Thought, Action, Action Input, and Observation pattern. This makes it necessary to think about the task’s present state and how it relates to the end objective and then plan how to proceed. Concurrent with this preprint, 46 details a similar strategy for equipping an LLM with chemistry-specific capabilities that would otherwise be beyond its purview. The LLM then asks for an action and the input for this Action (with the keyword “Action based on the reasoning it has just completed in the Thought step. After a short break, the text generator resumes its search for an appropriate function to apply to the data it has been given. The result is sent back to the LLM with the phrase “Observation” prepended, and the LLM repeats the previous step, “Thought.” 

Thus, the LLM evolves from a self-assured, albeit sometimes erroneous, information source into a thinking engine that observes and reflects on its observations and takes appropriate Action based on what it learns. The researchers deployed thirteen different tools to aid in research and discovery. The team acknowledges that the given toolset is not comprehensive. It is easily extensible to new uses by simply supplying the tool and describing its intended purpose in natural language. ChemCrow helps professional chemists and those without specialized training in the field by providing a user-friendly interface to reliable chemical information. 

This paper evaluates ChemCrow’s features across 12 different use scenarios, such as synthesizing a target molecule, safety controls, and finding compounds with similar modes of Action. The LLM-based evaluation found that GPT-4 and ChemCrow are nearly equally effective in completeness and quality of thought. In contrast, the human evaluations found that ChemCrow significantly outperformed GPT-4 by nearly 4.4/10 points and 2.75/10 in successful task completion.


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