3 papers
cs.LG2026
GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Models
Oscar Rivera, Ziqing Wang, Matthieu Dagommer +2
Machine learning accelerates molecular property prediction, yet state-of-the-art Large Language Models and Graph Neural Networks operate as black boxes. In drug discovery, where sa…
cs.LG2025
POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimization
Ziqing Wang, Yibo Wen, William Pattie +6
Lead optimization in drug discovery requires efficiently navigating vast chemical space through iterative cycles to enhance molecular properties while preserving structural similar…
cs.LG2025
A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization
Ziqing Wang, Kexin Zhang, Zihan Zhao +4
Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language, symbolic not…