10 citations · 21 across the 11 of their papers we have counts for
13 papers
Multi-Scale Temporal Flows for Peptide Trajectory Generation
Xichen Sun, Wentao Wei, Xiaoxi Zhang +2
Peptides occupy a particularly challenging regime of biomolecular dynamics. Short peptides lack a stable folded core and populate broad conformational ensembles, while cyclization…
BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening
Anjie Qiao, Zhen Wang, Yaliang Li +2
Virtual screening aims to efficiently identify active ligands from massive chemical libraries for a given target pocket. Recent CLIP-style models such as DrugCLIP enable scalable v…
De Novo Molecular Generation from Mass Spectra via Many-Body Enhanced Diffusion
Xichen Sun, Wentao Wei, Jiahua Rao +2
Molecular structure generation from mass spectrometry is fundamental for understanding cellular metabolism and discovering novel compounds. Although tandem mass spectrometry (MS/MS…
A Novel Framework for Multi-Modal Protein Representation Learning
Runjie Zheng, Zhen Wang, Anjie Qiao +3
Accurate protein function prediction requires integrating heterogeneous intrinsic signals (e.g., sequence and structure) with noisy extrinsic contexts (e.g., protein-protein intera…
Fitness aligned structural modeling enables scalable virtual screening with AuroBind
Zhongyue Zhang, Jiahua Rao, Jie Zhong +22
Most human proteins remain undrugged, over 96% of human proteins remain unexploited by approved therapeutics. While structure-based virtual screening promises to expand the druggab…
A 3D pocket-aware and affinity-guided diffusion model for lead optimization
Anjie Qiao, Junjie Xie, Weifeng Huang +7
Molecular optimization, aimed at improving binding affinity or other molecular properties, is a crucial task in drug discovery that often relies on the expertise of medicinal chemi…
RiboFlow: Conditional De Novo RNA Co-Design via Synergistic Flow Matching
Runze Ma, Zhongyue Zhang, Zichen Wang +4
Ribonucleic acid (RNA) binds to molecules to achieve specific biological functions. While generative models are advancing biomolecule design, existing methods for designing RNA tha…
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
Jiahua Rao, Jiancong Xie, Hanjing Lin +3
Graph Neural Networks (GNNs) have gained considerable traction for their capability to effectively process topological data, yet their interpretability remains a critical concern.…
Retrieval-based Knowledge Augmented Vision Language Pre-training
Jiahua Rao, Zifei Shan, Longpo Liu +2
With the recent progress in large-scale vision and language representation learning, Vision Language Pre-training (VLP) models have achieved promising improvements on various multi…
Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction
Jiahua Rao, Shuangjia Zheng, Sijie Mai +1
Illuminating the interconnections between drugs and genes is an important topic in drug development and precision medicine. Currently, computational predictions of drug-gene intera…
Learning Attributed Graph Representations with Communicative Message Passing Transformer
Jianwen Chen, Shuangjia Zheng, Ying Song +2
Constructing appropriate representations of molecules lies at the core of numerous tasks such as material science, chemistry and drug designs. Recent researches abstract molecules…
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction
Jiahua Rao, Shuangjia Zheng, Yuedong Yang
Advances in machine learning have led to graph neural network-based methods for drug discovery, yielding promising results in molecular design, chemical synthesis planning, and mol…
Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks
Shuangjia Zheng, Jiahua Rao, Zhongyue Zhang +2
Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing…