  Large language models (LLMs) have shown remarkable potential in various
domains, but they often lack the ability to access and reason over
domain-specific knowledge and tools. In this paper, we introduced CACTUS
(Chemistry Agent Connecting Tool-Usage to Science), an LLM-based agent that
integrates cheminformatics tools to enable advanced reasoning and
problem-solving in chemistry and molecular discovery. We evaluate the
performance of CACTUS using a diverse set of open-source LLMs, including
Gemma-7b, Falcon-7b, MPT-7b, Llama2-7b, and Mistral-7b, on a benchmark of
thousands of chemistry questions. Our results demonstrate that CACTUS
significantly outperforms baseline LLMs, with the Gemma-7b and Mistral-7b
models achieving the highest accuracy regardless of the prompting strategy
used. Moreover, we explore the impact of domain-specific prompting and hardware
configurations on model performance, highlighting the importance of prompt
engineering and the potential for deploying smaller models on consumer-grade
hardware without significant loss in accuracy. By combining the cognitive
capabilities of open-source LLMs with domain-specific tools, CACTUS can assist
researchers in tasks such as molecular property prediction, similarity
searching, and drug-likeness assessment. Furthermore, CACTUS represents a
significant milestone in the field of cheminformatics, offering an adaptable
tool for researchers engaged in chemistry and molecular discovery. By
integrating the strengths of open-source LLMs with domain-specific tools,
CACTUS has the potential to accelerate scientific advancement and unlock new
frontiers in the exploration of novel, effective, and safe therapeutic
candidates, catalysts, and materials. Moreover, CACTUS's ability to integrate
with automated experimentation platforms and make data-driven decisions in real
time opens up new possibilities for autonomous discovery.
