We introduce GenieAgent, a drug discovery agent that integrates a wide range of molecule design models and bridges the user intentions to concrete actions by navigating the large skill ecosystem. We also propose an evaluation framework simulating drug discovery conversations, based on real-world experiments. A large-scale assessment, validated by expert annotations, demonstrates that GenieAgent reliably meets the majority of molecular engineers' needs with high scientific accuracy and robustness.
Apr 15, 2025
SpatialAgent integrates large language models with dynamic tool execution and adaptive reasoning. SpatialAgent spans the entire research pipeline, from experimental design to multimodal data analysis and hypothesis generation.
Apr 3, 2025
We propose STAR, a structure-to-text data generation method for complicated structure prediction tasks that first generates complicated event structures (Y) and then generates input passages (X), all with Large Language Models. We further reduce errors and improve data quality through self-reflection error identification and self-refinement with iterative revision. We show that the data generated by STAR significantly improves the performance of low-resource event extraction and relation extraction tasks, even surpassing the effectiveness of human-curated data.
Feb 22, 2024