Molecule Task

Molecule task research focuses on leveraging machine learning, particularly large language models (LLMs) and graph neural networks (GNNs), to analyze and predict molecular properties and behaviors. Current efforts concentrate on developing robust benchmarks for evaluating LLMs' performance on diverse chemical tasks, integrating multimodal data (text, graphs, images) into models for improved accuracy, and designing novel algorithms like those incorporating classifier guidance or nearest-neighbor searches for enhanced efficiency and predictive power in applications such as drug discovery and materials science. This work promises to accelerate scientific discovery by automating complex molecular analyses and enabling more efficient design of molecules with desired properties.