Research Areas
Biography and Research Information
OverviewAI-generated summary
Soheil Jamali's research utilizes molecular dynamics simulations and advanced computational techniques to investigate complex biological systems. His work focuses on understanding the mechanisms of drug delivery, particularly the role of lipid nanoparticles. Jamali also explores the prediction of peptide hydrophobicity and the calculation of free energy using generative models trained on molecular dynamics trajectories, including diffusion model approaches. He has contributed to the development of methods for accelerating molecular dynamics simulations through synthetic trajectory generation.
Jamali collaborates with researchers at the University of Arkansas at Fayetteville, including Fauzia Haque, Mahmoud Moradi, and Jiahui Chen, with whom he has co-authored multiple publications. His scholarly output includes five publications with an h-index of 1 and a total of 4 citations. Jamali's recent activity indicates ongoing engagement in his research areas.
Metrics
- h-index: 1
- Publications: 5
- Citations: 4
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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BPS2025 - Free energy calculations using generative models trained based on molecular dynamics trajectories: A diffusion model approach (2025)
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Molecular dynamics simulation analysis of the effects and mechanisms of lipid nanoparticles in drug delivery systems (2024)
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Utility of a time-lagged autoencoder for calculating free energy by generating a large number of synthetic trajectories based on molecular dynamics (2024)
Collaboration Network
Top Collaborators
- Molecular dynamics simulation analysis of the effects and mechanisms of lipid nanoparticles in drug delivery systems
- Utility of a time-lagged autoencoder for calculating free energy by generating a large number of synthetic trajectories based on molecular dynamics
- BPS2025 - Free energy calculations using generative models trained based on molecular dynamics trajectories: A diffusion model approach
- BPS2026 – Diffusion models for accelerating molecular dynamics: Synthetic trajectory generation and free-energy reconstruction
- Molecular dynamics simulation analysis of the effects and mechanisms of lipid nanoparticles in drug delivery systems
- Utility of a time-lagged autoencoder for calculating free energy by generating a large number of synthetic trajectories based on molecular dynamics
- BPS2025 - Free energy calculations using generative models trained based on molecular dynamics trajectories: A diffusion model approach
- BPS2026 – Diffusion models for accelerating molecular dynamics: Synthetic trajectory generation and free-energy reconstruction
- BPS2025 - Free energy calculations using generative models trained based on molecular dynamics trajectories: A diffusion model approach
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