Intern, The Hong Kong University of Science and Technology (Guangzhou)
3 papers at NeurIPS 2025
We propose ModuLM, a flexible framework for LLM-based molecular relational learning, supporting multimodal inputs and dynamic model construction.
S²VM self-supervised learning from large unlabeled drug pairs, achieving SOTA DDI prediction and superior generalization/interpretability on novel/few-shot drugs.
