Zobrazeno 1 - 10
of 730
pro vyhledávání: '"Zeng, XiangXiang"'
Autor:
Ma, Tengfei, Lin, Xuan, Li, Tianle, Li, Chaoyi, Chen, Long, Zhou, Peng, Cai, Xibao, Yang, Xinyu, Zeng, Daojian, Cao, Dongsheng, Zeng, Xiangxiang
Large Language Models (LLMs) have recently demonstrated remarkable performance in general tasks across various fields. However, their effectiveness within specific domains such as drug development remains challenges. To solve these challenges, we int
Externí odkaz:
http://arxiv.org/abs/2410.11550
Autor:
Cheng, Zhixiang, Xiang, Hongxin, Ma, Pengsen, Zeng, Li, Jin, Xin, Yang, Xixi, Lin, Jianxin, Deng, Yang, Song, Bosheng, Feng, Xinxin, Deng, Changhui, Zeng, Xiangxiang
Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish them. Our research indicates t
Externí odkaz:
http://arxiv.org/abs/2409.12926
Autor:
Wang, Li, Fu, Xiangzheng, Yang, Jiahao, Zhang, Xinyi, Ye, Xiucai, Liu, Yiping, Sakurai, Tetsuya, Zeng, Xiangxiang
Deep learning holds a big promise for optimizing existing peptides with more desirable properties, a critical step towards accelerating new drug discovery. Despite the recent emergence of several optimized Antimicrobial peptides(AMP) generation metho
Externí odkaz:
http://arxiv.org/abs/2406.02610
Autor:
Wang, Li, Li, Yiping, Fu, Xiangzheng, Ye, Xiucai, Shi, Junfeng, Yen, Gary G., Zeng, Xiangxiang
Antimicrobial peptides (AMPs) have exhibited unprecedented potential as biomaterials in combating multidrug-resistant bacteria. Despite the increasing adoption of artificial intelligence for novel AMP design, challenges pertaining to conflicting attr
Externí odkaz:
http://arxiv.org/abs/2405.00753
Autor:
Ma, Tengfei, song, Xiang, Tao, Wen, Li, Mufei, Zhang, Jiani, Pan, Xiaoqin, Lin, Jianxin, Song, Bosheng, Zeng, xiangxiang
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web. Although knowledge graph embedding (KGE) models have demo
Externí odkaz:
http://arxiv.org/abs/2404.03893
Autor:
Zhou, Peng, Wang, Jianmin, Li, Chunyan, Wang, Zixu, Liu, Yiping, Sun, Siqi, Lin, Jianxin, Wei, Leyi, Cai, Xibao, Lai, Houtim, Liu, Wei, Wang, Longyue, Liu, Yuansheng, Zeng, Xiangxiang
While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint mo
Externí odkaz:
http://arxiv.org/abs/2403.13244
Autor:
Ye, Geyan, Cai, Xibao, Lai, Houtim, Wang, Xing, Huang, Junhong, Wang, Longyue, Liu, Wei, Zeng, Xiangxiang
Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery. However, molecule optimization, a critical task in the drug discovery pipeli
Externí odkaz:
http://arxiv.org/abs/2401.10334
Autor:
Ma, Tengfei, Chen, Yujie, Tao, Wen, Zheng, Dashun, Lin, Xuan, Pang, Patrick Cheong-lao, Liu, Yiping, Wang, Yijun, Wang, Longyue, Song, Bosheng, Zeng, Xiangxiang, Yu, Philip S.
Molecular interaction prediction plays a crucial role in forecasting unknown interactions between molecules, such as drug-target interaction (DTI) and drug-drug interaction (DDI), which are essential in the field of drug discovery and therapeutics. A
Externí odkaz:
http://arxiv.org/abs/2312.06682
Recent data-driven image colorization methods have enabled automatic or reference-based colorization, while still suffering from unsatisfactory and inaccurate object-level color control. To address these issues, we propose a new method called DiffCol
Externí odkaz:
http://arxiv.org/abs/2308.01655
Autor:
Lin, Xuan, Dai, Lichang, Zhou, Yafang, Yu, Zu-Guo, Zhang, Wen, Shi, Jian-Yu, Cao, Dong-Sheng, Zeng, Li, Chen, Haowen, Song, Bosheng, Yu, Philip S., Zeng, Xiangxiang
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect
Externí odkaz:
http://arxiv.org/abs/2306.05257