21 citations · 71 across the 17 of their papers we have counts for
17 papers
LLQL: Logistic Likelihood Q-Learning for Reinforcement Learning
Outongyi Lv, Bingxin Zhou
Modern reinforcement learning (RL) can be categorized into online and offline variants. As a pivotal aspect of both online and offline RL, current research on the Bellman equation…
Multi-level Protein Representation Learning for Blind Mutational Effect Prediction
Yang Tan, Bingxin Zhou, Yuanhong Jiang +2
Directed evolution plays an indispensable role in protein engineering that revises existing protein sequences to attain new or enhanced functions. Accurately predicting the effects…
Graph Denoising Diffusion for Inverse Protein Folding
Kai Yi, Bingxin Zhou, Yiqing Shen +2
Inverse protein folding is challenging due to its inherent one-to-many mapping characteristic, where numerous possible amino acid sequences can fold into a single, identical protei…
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks
Bingxin Zhou, Outongyi Lv, Kai Yi +4
Directed evolution as a widely-used engineering strategy faces obstacles in finding desired mutants from the massive size of candidate modifications. While deep learning methods le…
Graph Representation Learning for Interactive Biomolecule Systems
Xinye Xiong, Bingxin Zhou, Yu Guang Wang
Advances in deep learning models have revolutionized the study of biomolecule systems and their mechanisms. Graph representation learning, in particular, is important for accuratel…
Framelet Message Passing
Xinliang Liu, Bingxin Zhou, Chutian Zhang +1
Graph neural networks (GNNs) have achieved champion in wide applications. Neural message passing is a typical key module for feature propagation by aggregating neighboring features…