3 citations · 3 across the 5 of their papers we have counts for
5 papers
Retrieval-Augmented Foundation Models for Matched Molecular Pair Transformations to Recapitulate Medicinal Chemistry Intuition
Bo Pan, Peter Zhiping Zhang, Hao-Wei Pang +4
Matched molecular pairs (MMPs) capture the local chemical edits that medicinal chemists routinely use to design analogs, but existing ML approaches either operate at the whole-mole…
PepEVOLVE: Position-Aware Dynamic Peptide Optimization via Group-Relative Advantage
Trieu Nguyen, Hao-Wei Pang, Shasha Feng
Macrocyclic peptides are an emerging modality that combines biologics-like affinity with small-molecule-like developability, but their vast combinatorial space and multi-parameter…
PepThink-R1: LLM for Interpretable Cyclic Peptide Optimization with CoT SFT and Reinforcement Learning
Ruheng Wang, Hang Zhang, Trieu Nguyen +5
Designing therapeutic peptides with tailored properties is hindered by the vastness of sequence space, limited experimental data, and poor interpretability of current generative mo…
Kongzi: A Historical Large Language Model with Fact Enhancement
Jiashu Yang, Ningning Wang, Yian Zhao +6
Large Language Models (LLMs) can exhibit factual inaccuracies during long-chain reasoning or under misleading prompts, limiting their reliability in historical analysis. We propose…
Large Language Model guided Deep Reinforcement Learning for Decision Making in Autonomous Driving
Hao Pang, Zhenpo Wang, Guoqiang Li
Deep reinforcement learning (DRL) shows promising potential for autonomous driving decision-making. However, DRL demands extensive computational resources to achieve a qualified po…