Zobrazeno 1 - 10
of 3 500
pro vyhledávání: '"Wu, LiJun"'
Autor:
He, Liang, Jin, Peiran, Min, Yaosen, Xie, Shufang, Wu, Lijun, Qin, Tao, Liang, Xiaozhuan, Gao, Kaiyuan, Jiang, Yuliang, Liu, Tie-Yan
Proteins, essential to biological systems, perform functions intricately linked to their three-dimensional structures. Understanding the relationship between protein structures and their amino acid sequences remains a core challenge in protein modeli
Externí odkaz:
http://arxiv.org/abs/2410.24022
Autor:
Bai, Tianyi, Yang, Ling, Wong, Zhen Hao, Peng, Jiahui, Zhuang, Xinlin, Zhang, Chi, Wu, Lijun, Qiu, Jiantao, Zhang, Wentao, Yuan, Binhang, He, Conghui
Efficient data selection is crucial to accelerate the pretraining of large language models (LLMs). While various methods have been proposed to enhance data efficiency, limited research has addressed the inherent conflicts between these approaches to
Externí odkaz:
http://arxiv.org/abs/2410.08102
Drug-Target binding Affinity (DTA) prediction is essential for drug discovery. Despite the application of deep learning methods to DTA prediction, the achieved accuracy remain suboptimal. In this work, inspired by the recent success of retrieval meth
Externí odkaz:
http://arxiv.org/abs/2407.15202
The integration of molecule and language has garnered increasing attention in molecular science. Recent advancements in Language Models (LMs) have demonstrated potential for the comprehensive modeling of molecule and language. However, existing works
Externí odkaz:
http://arxiv.org/abs/2406.05797
Autor:
Wang, Zun, Liu, Chang, Zou, Nianlong, Zhang, He, Wei, Xinran, Huang, Lin, Wu, Lijun, Shao, Bin
In this study, we introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep Equilibrium Models (DEQs) for predicting Density Functional Theory (DFT) Hamiltonians.
Externí odkaz:
http://arxiv.org/abs/2406.03794
In this paper, we develop SE3Set, an SE(3) equivariant hypergraph neural network architecture tailored for advanced molecular representation learning. Hypergraphs are not merely an extension of traditional graphs; they are pivotal for modeling high-o
Externí odkaz:
http://arxiv.org/abs/2405.16511
Molecular docking is a pivotal process in drug discovery. While traditional techniques rely on extensive sampling and simulation governed by physical principles, these methods are often slow and costly. The advent of deep learning-based approaches ha
Externí odkaz:
http://arxiv.org/abs/2403.20261
The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology. This approach leverages the rich, multifaceted descriptions o
Externí odkaz:
http://arxiv.org/abs/2403.01528
Autor:
Pei, Qizhi, Wu, Lijun, Gao, Kaiyuan, Liang, Xiaozhuan, Fang, Yin, Zhu, Jinhua, Xie, Shufang, Qin, Tao, Yan, Rui
Recent research trends in computational biology have increasingly focused on integrating text and bio-entity modeling, especially in the context of molecules and proteins. However, previous efforts like BioT5 faced challenges in generalizing across d
Externí odkaz:
http://arxiv.org/abs/2402.17810
A fundamental challenge in multi-agent reinforcement learning (MARL) is to learn the joint policy in an extremely large search space, which grows exponentially with the number of agents. Moreover, fully decentralized policy factorization significantl
Externí odkaz:
http://arxiv.org/abs/2401.12574