5 citations · 12 across the 10 of their papers we have counts for
13 papers
Structure-aware Reinforcement Learning for Protein Directed Evolution
Zikun Nie, Suyuan Zhao, Yizhen Luo +2
Protein optimization remains a longstanding goal in life sciences. Existing machine learning-assisted directed evolution (MLDE) methods primarily rely on sequence-only features, ov…
CellMSA: Context Modeling for Single-Cell Representation Learning
Suyuan Zhao, Minghao Liu, Yizhen Luo +1
Single-cell transcriptomics enables profiling of cellular states at unprecedented resolution, but its high dimensionality, sparsity, and technical batch effects pose significant ch…
AbGaze: Attentive Geometric Representation Learning for End-to-End Antibody Design
Jiashuo Wang, Siqi Fan, Yizhen Luo +1
Computational antibody design requires representations that capture the geometric patterns underlying antigen--antibody interactions, yet existing approaches often rely on scalar d…
Co-Generative De Novo Functional Protein Design
Xinrui Chen, Yizhen Luo, Siqi Fan +1
De novo functional protein design aims to generate protein sequences that realize specified biochemical functions without relying on evolutionary templates, enabling broad applicat…
Reinforced Reasoning for End-to-End Retrosynthetic Planning
Chenyang Zuo, Siqi Fan, Yizhen Luo +1
Retrosynthetic planning is a fundamental task in organic chemistry, yet remains challenging due to its combinatorial complexity. To address this, conventional approaches typically…
SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics
Suyuan Zhao, Yizhen Luo, Ganbo Yang +3
Spatial Transcriptomics (ST) technologies provide biologists with rich insights into single-cell biology by preserving spatial context of cells. Building foundational models for ST…
PharMolixFM: All-Atom Foundation Models for Molecular Modeling and Generation
Yizhen Luo, Jiashuo Wang, Siqi Fan +1
Structural biology relies on accurate three-dimensional biomolecular structures to advance our understanding of biological functions, disease mechanisms, and therapeutics. While re…
MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering
Yizhen Luo, Zikun Nie, Massimo Hong +3
Studying protein mutations within amino acid sequences holds tremendous significance in life sciences. Protein language models (PLMs) have demonstrated strong capabilities in broad…
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge
Yizhen Luo, Kai Yang, Massimo Hong +4
Capturing molecular knowledge with representation learning approaches holds significant potential in vast scientific fields such as chemistry and life science. An effective and gen…
LangCell: Language-Cell Pre-training for Cell Identity Understanding
Suyuan Zhao, Jiahuan Zhang, Yushuai Wu +2
Cell identity encompasses various semantic aspects of a cell, including cell type, pathway information, disease information, and more, which are essential for biologists to gain in…
DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation
Yushuai Wu, Ting Zhang, Hao Zhou +11
The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i.e., the therapeutic domain calls for more treatment alternatives,…
ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
Kangjie Zheng, Siyu Long, Tianyu Lu +6
Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale…
Towards Unified AI Drug Discovery with Multiple Knowledge Modalities
Yizhen Luo, Xing Yi Liu, Kai Yang +5
In recent years, AI models that mine intrinsic patterns from molecular structures and protein sequences have shown promise in accelerating drug discovery. However, these methods pa…