6 papers
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…
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…
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…