Zobrazeno 1 - 10
of 455
pro vyhledávání: '"Lin, Haitao"'
Autor:
Lin, Haitao, Zhao, Guojiang, Zhang, Odin, Huang, Yufei, Wu, Lirong, Liu, Zicheng, Li, Siyuan, Tan, Cheng, Gao, Zhifeng, Li, Stan Z.
Structure-based drug design (SBDD) aims to generate potential drugs that can bind to a target protein and is greatly expedited by the aid of AI techniques in generative models. However, a lack of systematic understanding persists due to the diverse s
Externí odkaz:
http://arxiv.org/abs/2406.10840
Autor:
Wu, Lirong, Tian, Yijun, Lin, Haitao, Huang, Yufei, Li, Siyuan, Chawla, Nitesh V, Li, Stan Z.
Protein-protein bindings play a key role in a variety of fundamental biological processes, and thus predicting the effects of amino acid mutations on protein-protein binding is crucial. To tackle the scarcity of annotated mutation data, pre-training
Externí odkaz:
http://arxiv.org/abs/2405.10348
Autor:
Zhang, Odin, Lin, Haitao, Zhang, Hui, Zhao, Huifeng, Huang, Yufei, Huang, Yuansheng, Jiang, Dejun, Hsieh, Chang-yu, Pan, Peichen, Hou, Tingjun
The idea of using deep-learning-based molecular generation to accelerate discovery of drug candidates has attracted extraordinary attention, and many deep generative models have been developed for automated drug design, termed molecular generation. I
Externí odkaz:
http://arxiv.org/abs/2404.19230
Transformer models have been successful in various sequence processing tasks, but the self-attention mechanism's computational cost limits its practicality for long sequences. Although there are existing attention variants that improve computational
Externí odkaz:
http://arxiv.org/abs/2404.11163
Autor:
Zhang, Odin, Huang, Yufei, Cheng, Shichen, Yu, Mengyao, Zhang, Xujun, Lin, Haitao, Zeng, Yundian, Wang, Mingyang, Wu, Zhenxing, Zhao, Huifeng, Zhang, Zaixi, Hua, Chenqing, Kang, Yu, Cui, Sunliang, Pan, Peichen, Hsieh, Chang-Yu, Hou, Tingjun
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets. These methods, while effective in designing tightly bound ligands
Externí odkaz:
http://arxiv.org/abs/2404.00014
Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications. One
Externí odkaz:
http://arxiv.org/abs/2403.03483
Autor:
Lin, Haitao, Zhang, Odin, Zhao, Huifeng, Jiang, Dejun, Wu, Lirong, Liu, Zicheng, Huang, Yufei, Li, Stan Z.
Therapeutic peptides have proven to have great pharmaceutical value and potential in recent decades. However, methods of AI-assisted peptide drug discovery are not fully explored. To fill the gap, we propose a target-aware peptide design method calle
Externí odkaz:
http://arxiv.org/abs/2405.06642
Recent years have witnessed the great success of graph pre-training for graph representation learning. With hundreds of graph pre-training tasks proposed, integrating knowledge acquired from multiple pre-training tasks has become a popular research t
Externí odkaz:
http://arxiv.org/abs/2403.01400
Augmentation is an effective alternative to utilize the small amount of labeled protein data. However, most of the existing work focuses on design-ing new architectures or pre-training tasks, and relatively little work has studied data augmentation f
Externí odkaz:
http://arxiv.org/abs/2403.00875
Autor:
Wu, Lirong, Tian, Yijun, Huang, Yufei, Li, Siyuan, Lin, Haitao, Chawla, Nitesh V, Li, Stan Z.
Protein-Protein Interactions (PPIs) are fundamental in various biological processes and play a key role in life activities. The growing demand and cost of experimental PPI assays require computational methods for efficient PPI prediction. While exist
Externí odkaz:
http://arxiv.org/abs/2402.14391