🤖 AI Summary
This work addresses the challenge of transforming general-purpose large language models (LLMs) into attribute-controllable molecular generators. We propose a lightweight adaptation paradigm that converts open-source Llama models into chemical language models (CLMs) via supervised fine-tuning (SFT) and direct preference optimization (DPO), enabling direct SMILES string generation conditioned on multidimensional physicochemical properties (e.g., logP, aqueous solubility). To our knowledge, this is the first empirical demonstration that an adapted general LLM achieves performance on multi-objective molecular generation tasks comparable to or exceeding that of domain-specific chemically pretrained models. The approach enables a paradigm shift from “chemical knowledge question-answering” to “property-directed molecular design,” significantly enhancing controllability, interpretability, and interactive exploration of chemical space.
📝 Abstract
Here we show that a Large Language Model (LLM) can serve as a foundation model for a Chemical Language Model (CLM) which performs at or above the level of CLMs trained solely on chemical SMILES string data. Using supervised fine-tuning (SFT) and direct preference optimization (DPO) on the open-source Llama LLM, we demonstrate that we can train an LLM to respond to prompts such as generating molecules with properties of interest to drug development. This overall framework allows an LLM to not just be a chatbot client for chemistry and materials tasks, but can be adapted to speak more directly as a CLM which can generate molecules with user-specified properties.