The Application of Machine Learning to Molecular Dynamics Simulations
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: 20 April 2025 | Viewed by 58
Special Issue Editor
2. Net4Science Academic Spin-Off, Università “Magna Græcia” di Catanzaro, 88100 Catanzaro, Italy
Interests: computational chemistry; medicinal chemistry; infectiouse disease; drug repurposing; virtual screening; molecular dynamics; antioxidant activity
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Molecular dynamics (MD) allows for detailed study of the atomistic behavior of biomolecules, such as protein–ligand and protein–protein interactions and has played an important role in the field of drug discovery and development, providing powerful tools for improving the accuracy and efficiency of the process. Machine learning, through models like deep learning, accelerates the process, enabling faster predictions of key properties such as binding affinity, toxicity, and mechanisms of action. In molecular dynamics simulations, machine learning techniques can be used to analyze and optimize simulation data to improve simulation efficiency. By combining machine learning algorithms with molecular dynamics simulations, we can achieve faster and more accurate simulations, leading to a deeper understanding of the properties and behavior of molecular systems.
This Special Issue focuses on recent advances in machine learning to improve force fields, sampling, and property prediction in molecular dynamics simulations. The application of this approach can be primarily targeted at drug discovery, but can be extended to other aspects of protein structure and dynamics related to drug discovery. Innovative methods are also welcome to enhance the drug discovery process, the evaluation of mechanisms of action, and the study of atomistic details in biomolecular interactions, thereby contributing to a deeper and more precise understanding of molecular dynamics.
Dr. Isabella Romeo
Guest Editor
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Keywords
- molecular dynamics simulations
- machine learning
- AI in drug discovery
- pharmacokinetics prediction
- protein folding simulations
- toxicity prediction using ML
- free energy calculations
- virtual screening
- protein–ligand interactions
- protein–protein interactions
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