5 papers
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…
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…
Kongzi: A Historical Large Language Model with Fact Enhancement
Jiashu Yang, Ningning Wang, Yian Zhao +5
The capabilities of the latest large language models (LLMs) have been extended from pure natural language understanding to complex reasoning tasks. However, current reasoning model…
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…