1 code implementation • 29 Mar 2024 • Kaiyuan Gao, Qizhi Pei, Jinhua Zhu, Kun He, Lijun Wu
Molecular docking is a pivotal process in drug discovery.
2 code implementations • 3 Mar 2024 • Qizhi Pei, Lijun Wu, Kaiyuan Gao, Jinhua Zhu, Yue Wang, Zun Wang, Tao Qin, Rui Yan
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.
1 code implementation • 27 Feb 2024 • Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, Rui Yan
However, previous efforts like BioT5 faced challenges in generalizing across diverse tasks and lacked a nuanced understanding of molecular structures, particularly in their textual representations (e. g., IUPAC).
Ranked #1 on Molecule Captioning on ChEBI-20
1 code implementation • 11 Oct 2023 • Qizhi Pei, Wei zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, Rui Yan
Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery.
Ranked #2 on Text-based de novo Molecule Generation on ChEBI-20
1 code implementation • NeurIPS 2023 • Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Kun He, Tie-Yan Liu, Rui Yan
In this work, we propose $\mathbf{FABind}$, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding.
1 code implementation • 27 Aug 2023 • Kaiyuan Gao, Sunan He, Zhenyu He, Jiacheng Lin, Qizhi Pei, Jie Shao, Wei zhang
Generative pre-trained transformer (GPT) models have revolutionized the field of natural language processing (NLP) with remarkable performance in various tasks and also extend their power to multimodal domains.
no code implementations • 22 May 2023 • Jinsong Chen, Chang Liu, Kaiyuan Gao, Gaichao Li, Kun He
Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity on the number of nodes when handling large graphs.
no code implementations • 26 Oct 2022 • Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Tianbo Peng, Yingce Xia, Liang He, Shufang Xie, Tao Qin, Haiguang Liu, Kun He, Tie-Yan Liu
Specifically, we first pre-train an antibody language model based on the sequence data, then propose a one-shot way for sequence and structure generation of CDR to avoid the heavy cost and error propagation from an autoregressive manner, and finally leverage the pre-trained antibody model for the antigen-specific antibody generation model with some carefully designed modules.
1 code implementation • 10 Jun 2022 • Jinsong Chen, Kaiyuan Gao, Gaichao Li, Kun He
In this work, we observe that existing graph Transformers treat nodes as independent tokens and construct a single long sequence composed of all node tokens so as to train the Transformer model, causing it hard to scale to large graphs due to the quadratic complexity on the number of nodes for the self-attention computation.
1 code implementation • 16 Feb 2021 • Shengjie Luo, Kaiyuan Gao, Shuxin Zheng, Guolin Ke, Di He, LiWei Wang, Tie-Yan Liu
The language embedding can be either added to the word embedding or attached at the beginning of the sentence.