7 papers · 1 filter
Fused Bayesian Flow Networks for Dual-Target Molecular Design
Jingyuan Zhou, Shikui Tu, Lei Xu
Dual-target drug design aims to generate 3D molecules that can simultaneously interact with two target proteins, offering a promising route for discovering polypharmacological comp…
Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
Guang Lin, Shikui Tu, Lei Xu
Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD).…
Factored Causal Representation Learning for Robust Reward Modeling in RLHF
Yupei Yang, Lin Yang, Wanxi Deng +5
A reliable reward model is essential for aligning large language models with human preferences through reinforcement learning from human feedback. However, standard reward models a…
Full-Atom Peptide Design via Riemannian-Euclidean Bayesian Flow Networks
Hao Qian, Shikui Tu, Lei Xu
Diffusion and flow matching models have recently emerged as promising approaches for peptide binder design. Despite their progress, these models still face two major challenges. Fi…
Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number
Jingyuan Zhou, Hao Qian, Shikui Tu +1
Structure-based drug design (SBDD), aiming to generate 3D molecules with high binding affinity toward target proteins, is a vital approach in novel drug discovery. Although recent…
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations
Yupei Yang, Biwei Huang, Fan Feng +3
General intelligence requires quick adaption across tasks. While existing reinforcement learning (RL) methods have made progress in generalization, they typically assume only distr…