Jiahui Chen
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
Also affiliated: Hefei University of Technology (2025); Tianjin Chengjian University (2022); Xiamen University of Technology (2024)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Jiahui Chen is an Assistant Professor at the University of Arkansas at Fayetteville. Their research focuses on the evolution of SARS-CoV-2, investigating mutations that impact infectivity, vaccine effectiveness, and antibody resistance. Chen has published work analyzing SARS-CoV-2 substrains and novel variants in the United States and has explored mechanisms of viral evolution in Europe and America. Their publications also address prediction and mitigation strategies for mutation threats to COVID-19 vaccines and antibody therapies, alongside research on SARS-CoV-2 main protease inhibitors.
Further research interests include pharmaceutical cocrystals, with a review on their preparation, properties, and applications. Chen has also worked on the development of AI-based models for predicting heart disease within the Internet of Medical Things (IoMT) framework. Chen serves as PI on an NSF grant for a conference, The 10th SIAM Central States Section Annual Meeting, totaling $24,000. They maintain an active research lab and lead a research group, collaborating with colleagues at the University of Arkansas at Fayetteville.
Metrics
- h-index: 1
- Publications: 6
- Citations: 12
Selected Publications
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BPS2026 – Diffusion models for accelerating molecular dynamics: Synthetic trajectory generation and free-energy reconstruction (2026)
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Dimensionality reduction for k-means clustering of large-scale influenza mutation datasets (2026)
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<scp>PLNet</scp> : Persistent Laplacian neural network for protein–protein binding free energy prediction (2025)
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Drug Resistance Predictions Based on a Directed Flag Transformer (2025)
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BPS2025 - Free energy calculations using generative models trained based on molecular dynamics trajectories: A diffusion model approach (2025)
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Preventing future zoonosis: SARS-CoV-2 mutations enhance human–animal cross-transmission (2024)
Federal Grants 3 $856,640 total
Multiscale Differential Geometry Approaches to Protein Interaction Mechanisms
Collaboration Network
Top Collaborators
- Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation
- Topological deep learning based deep mutational scanning
- Preventing future zoonosis: SARS-CoV-2 mutations enhance human–animal cross-transmission
- Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation
- Preventing future zoonosis: SARS-CoV-2 mutations enhance human–animal cross-transmission
- Drug Resistance Predictions Based on a Directed Flag Transformer
- Drug Resistance Predictions Based on a Directed Flag Transformer
- Dimensionality reduction for k-means clustering of large-scale influenza mutation datasets.
- Topological deep learning based deep mutational scanning
- Topological deep learning based deep mutational scanning
- Topological deep learning based deep mutational scanning
- Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation
- BPS2025 - Free energy calculations using generative models trained based on molecular dynamics trajectories: A diffusion model approach
- BPS2025 - Free energy calculations using generative models trained based on molecular dynamics trajectories: A diffusion model approach
- BPS2025 - Free energy calculations using generative models trained based on molecular dynamics trajectories: A diffusion model approach
- Dimensionality reduction for k-means clustering of large-scale influenza mutation datasets.
- Drug Resistance Predictions Based on a Directed Flag Transformer
- Drug Resistance Predictions Based on a Directed Flag Transformer
- Drug Resistance Predictions Based on a Directed Flag Transformer
- Drug Resistance Predictions Based on a Directed Flag Transformer
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