AI-Powered Biomolecular Modeling: Bridging Scales and Accelerating Discovery
A special issue of Molecules (ISSN 1420-3049).
Deadline for manuscript submissions: 31 December 2025 | Viewed by 26
Special Issue Editors
Interests: molecular modelling; machine learning; host-guest binding; protein-X interaction
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; residence time prediction; Markov model; drug resistance; enhanced sampling simulation
Special Issues, Collections and Topics in MDPI journals
Interests: structure-effect relationship; nanoparticle; self-assembly; mechanism of action; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The fusion of artificial intelligence (AI) and molecular modeling is revolutionizing our capacity to decode molecular interactions, predict emergent behaviors, and design functional biomolecules at unprecedented scales. While traditional molecular simulations face limitations in resolving the time scale (long-timescale dynamics, rare events, and high-dimensional complex conformational landscapes) and Hamiltonian (force-field accuracy) issues, AI offers transformative tools such as neural network potentials and reinforcement learning to overcome these barriers. The current Special Issue will spotlight innovative frameworks where AI augments or redefines molecular modeling, enabling rapid exploration of complex landscapes in biomolecular systems (e.g., protein-ligand binding, allosteric regulation, and protein folding) with high-accuracy Hamiltonians (e.g., machine-learning potentials, multi-scale treatments, and recalibrated force fields). We emphasize hybrid approaches that integrate physics-based models with data-driven architectures, addressing challenges like interpretability, scalability, and generalization across diverse biological systems. By fostering collaboration between computational chemists, biophysicists, and AI specialists, this initiative aims to catalyze breakthroughs in rational molecule design and adaptive biomaterials. We welcome contributions that demonstrate novel algorithms, benchmark AI-surrogate models against experimental data, or uncover latent biophysical principles, propelling the field toward a new era of predictive, efficient, and actionable molecular science.
Dr. Zhaoxi Sun
Dr. Jianzhong Chen
Dr. Hui Wang
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- molecular simulations
- machine learning
- force field development
- enhanced sampling
- ab initio calculations
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