01
RNA Structure–Sequence Co-design
Developing generative models that couple RNA sequence and structure through explicit biological and topological constraints.
Focussequence · structure · topology
AI × RNA × Biomolecular Discovery
Zhiqi Ma
AI for Biomolecular Design & Scientific Discovery.
I develop generative and optimization methods for RNA, therapeutic sequences, and reliable molecular design — with the long-term goal of closing the loop between scientific reasoning, molecular generation, and biological experiments.
Visiting Researcher · MedAI Lab, Westlake UniversityJoint PhD Program Pre-admit · Zhongguancun AcademyGenerative AIRNA / siRNABiomolecular Design
Research
Three directions toward biomolecular discovery.
02
Therapeutic Sequence Design
Developing generative approaches for functional RNA and peptide therapeutics under biological constraints.
FocussiRNA · peptide · reliable optimization
03
Reliable Generative Optimization
Studying when generative optimization genuinely improves biological candidates—and when it exploits imperfect predictors, benchmarks, or evaluation protocols.
Focusobjectives · evaluation · reliability
Publications & Preprints
Scientific questions behind the work.
siProGenA: Generative siRNA Candidate Construction via Position Proposal and Guide Generation
Scientific question: Where to target and what guide sequence to design.
A generative framework for transcript-level siRNA candidate construction through joint target position proposal and guide generation.
siRNA-mRNA Dual Diffusion Model for RNAi Drug Design
Scientific question: How to generate effective RNAi candidates with diffusion models under biological constraints.
A diffusion-based framework for RNAi candidate generation by modeling target context and guide sequence design.
PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-Modal Omics Analysis
Scientific question: How to learn robust biological representations from spatial multimodal omics data.
A prototype-aware graph learning framework for multimodal spatial omics representation.
Background
Experience & education.
Research Experience
Visiting Researcher
Medical Artificial Intelligence Laboratory, Westlake University · Supervised by Prof. Yefeng Zheng
Jun 2026–Present
Research Assistant Intern
Peking University
Jan–May 2026
Research Assistant Intern
Shanghai Jiao Tong University School of Medicine · Supervised by Prof. Haicang Zhang
Jun–Oct 2025
Research Assistant Intern
Great Bay University · Supervised by Prof. Xubin Zheng
Jun 2024–Jun 2025
Business Intelligence Researcher
NetEase Games · Generative AI
Jul–Oct 2023
Education
M.Sc. in Bioinformatics
The Chinese University of Hong Kong, Shenzhen
2024–2026
B.Eng. in Information and Computing Science
Jinan University–University of Birmingham International Joint Programme
2020–2024
Long-term research vision
Beyond benchmark optimization.
UnderstandLearning representations that capture biological structure, function, and constraints.
Generate & ReasonBuilding AI systems that generate molecular designs and reason about scientific hypotheses.
ValidateTesting computational predictions through independent evaluation and biological experiments.
DiscoverTurning validated models and hypotheses into new scientific insights.
My long-term goal is to develop reliable AI systems that move beyond black-box optimization toward scientifically grounded discovery. I am particularly interested in connecting generative models, scientific reasoning, and biological validation—and in building scientific agents that can support this iterative process.
About
I am a researcher working at the intersection of generative AI, computational biology, and scientific discovery.
I received my M.Sc. in Bioinformatics from The Chinese University of Hong Kong, Shenzhen, and I am currently a Visiting Researcher at Westlake University.
My research focuses on generative models for RNA and therapeutic biomolecules, particularly sequence–structure co-design, siRNA generation, and reliable molecular optimization. Across these problems, I am interested in how structural and biological constraints can be incorporated into generative models, and how model-guided optimization can be evaluated reliably.
Beyond biomolecular design, I am also interested in scientific agents that can support researchers in reasoning, hypothesis generation, and iterative experimentation.
Beyond Research
Outside research, I enjoy films and music. One song I keep returning to is The Last Day of the End of the World by NoMBe.