58 citations · 162 across the 18 of their papers we have counts for
18 papers
Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors
Qizhi Pei, Lijun Wu, Zhenyu He +4
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 subo…
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text Modeling
Qizhi Pei, Rui Yan, Kaiyuan Gao +2
The integration of molecular and natural language representations has emerged as a focal point in molecular science, with recent advancements in Language Models (LMs) demonstrating…
FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation
Kaiyuan Gao, Qizhi Pei, Gongbo Zhang +3
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…
Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
Qizhi Pei, Zhimeng Zhou, Kaiyuan Gao +6
The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and…
BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning
Qizhi Pei, Lijun Wu, Kaiyuan Gao +6
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,…
BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
Qizhi Pei, Wei Zhang, Jinhua Zhu +5
Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several…
FABind: Fast and Accurate Protein-Ligand Binding
Qizhi Pei, Kaiyuan Gao, Lijun Wu +7
Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements i…
Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design
Kaiyuan Gao, Lijun Wu, Jinhua Zhu +8
Antibodies are versatile proteins that can bind to pathogens and provide effective protection for human body. Recently, deep learning-based computational antibody design has attrac…
Unified 2D and 3D Pre-Training of Molecular Representations
Jinhua Zhu, Yingce Xia, Lijun Wu +5
Molecular representation learning has attracted much attention recently. A molecule can be viewed as a 2D graph with nodes/atoms connected by edges/bonds, and can also be represent…
SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction
Qizhi Pei, Lijun Wu, Jinhua Zhu +6
Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with…
Direct Molecular Conformation Generation
Jinhua Zhu, Yingce Xia, Chang Liu +9
Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology. Previou…
Discovering Drug-Target Interaction Knowledge from Biomedical Literature
Yutai Hou, Yingce Xia, Lijun Wu +6
The Interaction between Drugs and Targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedic…
Dual-view Molecule Pre-training
Jinhua Zhu, Yingce Xia, Tao Qin +3
Inspired by its success in natural language processing and computer vision, pre-training has attracted substantial attention in cheminformatics and bioinformatics, especially for m…
IOT: Instance-wise Layer Reordering for Transformer Structures
Jinhua Zhu, Lijun Wu, Yingce Xia +5
With sequentially stacked self-attention, (optional) encoder-decoder attention, and feed-forward layers, Transformer achieves big success in natural language processing (NLP), and…
Return-Based Contrastive Representation Learning for Reinforcement Learning
Guoqing Liu, Chuheng Zhang, Li Zhao +5
Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxi…
Masked Contrastive Representation Learning for Reinforcement Learning
Jinhua Zhu, Yingce Xia, Lijun Wu +4
Improving sample efficiency is a key research problem in reinforcement learning (RL), and CURL, which uses contrastive learning to extract high-level features from raw pixels of in…
Microsoft Research Asia's Systems for WMT19
Yingce Xia, Xu Tan, Fei Tian +11
We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks. We won the first place for 8 of the 11 directions and the second place fo…
Soft Contextual Data Augmentation for Neural Machine Translation
Jinhua Zhu, Fei Gao, Lijun Wu +5
While data augmentation is an important trick to boost the accuracy of deep learning methods in computer vision tasks, its study in natural language tasks is still very limited. In…