Work

Automated Molecular Concept Generation and Labeling with Large Language Models

Year: 2024

Type: preprint

Abstract: Artificial intelligence (AI) is transforming scientific research, with explainable AI methods like concept-based models (CMs) showing promise for new discoveries. However, in molecular science, CMs are less common than black-box models like Graph Neural Networks (GNNs), due to their need for predefined concepts and manual labeling. This paper introduces the Automated Molecular Concept (AutoMolCo) framework, which leverages Large Language Models (LLMs) to automatically generate and label predictive molecular concepts. Through iterative concept refinement, AutoMolCo enables simple linear models to outperform GNNs and LLM in-context learning on several benchmarks. The framework operates without human knowledge input, overcoming limitations of existing CMs while maintaining explainability and allowing easy intervention. Experiments on MoleculeNet and High-Throughput Experimentation (HTE) datasets demonstrate that AutoMolCo-induced explainable CMs are beneficial for molecular science research.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2406.09612

Authors: Zhang, Zimin, Wu, Qianli, Xia, Botao, Sun, Fang, Hu, Ziniu, Sun, Yizhou, Zhang, Shichang

Institutions: University of Illinois Urbana-Champaign, University of California, Los Angeles, California Institute of Technology, Harvard University

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4399758705

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