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arXiv:2412.14642공식 코드 · Papers with Code 등록 2024-12-19

TOMG-Bench: Evaluating LLMs on Text-based Open Molecule Generation

In this paper, we propose Text-based Open Molecule Generation Benchmark (TOMG-Bench), the first benchmark to evaluate the open-domain molecule generation capability of LLMs. TOMG-Bench encompasses a dataset of three major tasks: molecule editing (MolEdit), molecule optimization (MolOpt), and customized molecule generation (MolCustom). Each major task further contains three subtasks, while each subtask comprises 5,000 test samples. Given the inherent complexity of open molecule generation evaluation, we also developed an automated evaluation system that helps measure both the quality and the accuracy of the generated molecules. Our comprehensive benchmarking of 25 LLMs reveals the current limitations as well as potential areas for improvement in text-guided molecule discovery. Furthermore, we propose OpenMolIns, a specialized instruction tuning dataset established for solving challenges raised by TOMG-Bench. Fine-tuned on OpenMolIns, Llama3.1-8B could outperform all the open-source general LLMs, even surpassing GPT-3.5-turbo by 46.5\% on TOMG-Bench. Our codes and datasets are available through https://github.com/phenixace/TOMG-Bench.

arXiv 원문 상세

arXiv:2306.08018공식 코드 · Papers with Code 등록 2023-06-13

Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models

Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their proficiency within specialized domains such as biomolecular studies remains limited. To address this challenge, we introduce Mol-Instructions, a comprehensive instruction dataset designed for the biomolecular domain. Mol-Instructions encompasses three key components: molecule-oriented instructions, protein-oriented instructions, and biomolecular text instructions. Each component aims to improve the understanding and prediction capabilities of LLMs concerning biomolecular features and behaviors. Through extensive instruction tuning experiments on LLMs, we demonstrate the effectiveness of Mol-Instructions in enhancing large models' performance in the intricate realm of biomolecular studies, thus fostering progress in the biomolecular research community. Mol-Instructions is publicly available for ongoing research and will undergo regular updates to enhance its applicability.

arXiv 원문 상세