Automated Molecular Concept Generation and Labeling with Large Language Models

Explainability can match or exceed performance: AutoMolCo's explainable concept-based models outperformed black-box Graph Neural Networks and LLM approaches on molecular benchmarks, demonstrating that interpretability doesn't require sacrificing accuracy—a key concern for responsible AI deployment in scientific research.

Automation reduces human bias in AI systems: By automatically generating and labeling molecular concepts without requiring predefined human knowledge, AutoMolCo addresses a critical responsible AI challenge: reducing the subjective human choices that can embed bias into AI systems used for scientific discovery.

Explainability enables meaningful human oversight: The framework's transparent concept-based approach allows researchers to easily intervene and understand model decisions, supporting the responsible AI principle that humans should be able to audit and correct AI systems before they're used in high-stakes molecular research.

LLMs can democratize scientific AI: Using Large Language Models to automate concept generation removes barriers to creating interpretable AI tools in molecular science, potentially making advanced AI more accessible to researchers without specialized machine learning expertise—important for equitable AI development.

Explainable AI supports scientific validation: By producing human-interpretable molecular concepts rather than black-box predictions, AutoMolCo enables scientists to validate whether the AI's reasoning aligns with established chemistry principles, a crucial safeguard for responsible use of AI in drug discovery and materials science.