Control Risk for Potential Misuse of Artificial Intelligence in Science

1 code implementation • 11 Dec 2023 • Jiyan He, Weitao Feng, Yaosen Min, Jingwei Yi, Kunsheng Tang, Shuai Li, Jie Zhang, Kejiang Chen, Wenbo Zhou, Xing Xie, Weiming Zhang, Nenghai Yu, Shuxin Zheng

In this study, we aim to raise awareness of the dangers of AI misuse in science, and call for responsible AI development and use in this domain.

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

no code implementations • 8 Jun 2023 • Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu

In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems.

From Static to Dynamic Structures: Improving Binding Affinity Prediction with a Graph-Based Deep Learning Model

1 code implementation • 19 Aug 2022 • Yaosen Min, Ye Wei, Peizhuo Wang, Xiaoting Wang, Han Li, Nian Wu, Stefan Bauer, Shuxin Zheng, Yu Shi, Yingheng Wang, Ji Wu, Dan Zhao, Jianyang Zeng

Accurate prediction of the protein-ligand binding affinities is an essential challenge in the structure-based drug design.

Drug Discovery

Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction

1 code implementation • 22 Oct 2020 • Yingheng Wang, Yaosen Min, Xin Chen, Ji Wu

Drug-drug interaction(DDI) prediction is an important task in the medical health machine learning community.

Contrastive Learning Representation Learning