FigureAsia 35 Under 35 · Healthcare
Shuangjia Zheng
Age 31 · AI for RNA-targeted drug discovery · Shanghai, China
Corresponding author and team leader on GerNA-Bind, a 2025 approach to RNA–ligand specificity in an underdeveloped part of molecular discovery.
- Approximate age at 31 December 2025
- 31
- Field
- Healthcare
- Country or region
- Shanghai, China
- FigureAsia U35 Assessment
- 82.2 / 100
Profile
Career and documented record
RNA is an increasingly important therapeutic target, but computational drug discovery has been built far more extensively around proteins. Shuangjia Zheng led 2025 work on GerNA-Bind, a framework designed to model RNA–ligand specificity rather than treating RNA as a minor variation of a protein-binding problem.
As corresponding author and team leader, Zheng's agency is clear. His Shanghai laboratory has built a larger programme across molecular generation, interaction prediction and computer-aided drug design; institutional records report more than 50 papers and over 5,000 citations, although those cumulative figures are not the basis of the 2026 score. The assessed contribution is the 2025 RNA-specific work and its open scientific record.
GerNA-Bind remains computational and preclinical. A benchmark improvement does not establish that a generated molecule can be synthesized, reach its target safely or become a medicine. The value lies in focusing model design on a therapeutic modality where data and structural conventions are still immature, and in doing so from an independent young laboratory in China.
FigureAsia selection
Why Shuangjia Zheng is on the list
FigureAsia selected Zheng for intellectual leadership in a neglected but strategically important area of drug discovery. The work is specific enough to be tested and broad enough to influence future RNA-targeted programmes. The score is constrained by its preclinical maturity and the absence of a disclosed therapeutic outcome; it rewards a credible research capability, not a drug candidate.
Verified work
The 2025–26 record
Principal milestone
2025 corresponding-author RNA–ligand specificity paper
Evidence record
Independent laboratory in Shanghai
Scale or implementation
Institution-reported record of more than 50 papers and 5,000 citations
Field context
The work in its field
Within ai for rna-targeted drug discovery, the relevant test is whether a result can survive scrutiny of maturity, attribution, validity and practical fit. That distinction matters: completed evidence is not projected benefit, and individual responsibility is not interchangeable with the wider team’s achievement.
FigureAsia U35 Assessment
Assessment breakdown
82.2out of 100
Substantive 2025–2026 contribution
18 / 20
The score reflects completed 2025–26 work in ai for rna-targeted drug discovery, assessed at the documented maturity of computational drug-discovery research.
Verified impact
10.5 / 15
Impact credit is limited to the measured study, regulatory, implementation or operating record stated in the profile; unsupported patient benefit is excluded.
Originality and distinction
9 / 10
The work creates or materially advances a distinctive capability within ai for rna-targeted drug discovery rather than relying on title or institutional association.
Field and industry influence
8 / 10
The assessment recognises demonstrated effects on research, product development, care delivery or professional practice, with publicity alone carrying no weight.
Individual agency
9 / 10
Named authorship and the documented role of Tenure-track Assistant Professor and Doctoral Supervisor establish individual responsibility while preserving credit for collaborators.
Durability and trajectory
4.5 / 5
The cited work forms part of a continuing programme, platform or research trajectory rather than a single uncompleted announcement.
Asian significance and global relevance
5 / 5
The Asian connection is material to the person's identity, operating base or populations served: Chinese scientist trained and working in China.
Clinical and scientific validity
5.6 / 7
Clinical and scientific validity is calibrated to computational drug-discovery research, with the profile retaining the evidence boundary attached to the result.
Safety, quality and responsible governance
4.9 / 7
Safety and governance credit reflects accurate regulatory language, study limitations, data stewardship and the refusal to turn early evidence into clinical certainty.
Translation and care-pathway fit
4.2 / 6
The work is scored for its demonstrated fit with a laboratory, regulatory, clinical, operational or public-health pathway, not for projected future adoption.
Access, equity and resource stewardship
3.5 / 5
Access credit reflects documented reach, capacity, affordability or inclusion while distinguishing service volume from proven clinical outcome.