Biomedical Applications of Molecular Simulation and Machine Learning in Disease Modeling and Drug Repurposing
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: closed (31 January 2024) | Viewed by 2495
Special Issue Editor
Special Issue Information
Dear Colleagues,
Machine learning (ML) and molecular simulation have the potential to transform all areas of medical sciences, including disease modeling and drug discovery/repurposing. By combining these two technologies within a precision medicine framework, we can reproduce biological systems that mirror complex diseases and their processes using artificial intelligence, and screen new candidate compounds from molecular simulation experiments powered by deep neural networks.
While there are many open problems to be addressed [1], we know that ML and molecular simulation hold the key to revolutionizing drug discovery and improving our understanding of the molecular mechanisms underlying complex diseases. Several of the open challenges in the simulation of molecular systems can be formulated as ML problems, ranging from an atomistic point-of-view (such as potential energy surfaces and free energy surfaces estimations) to coarse-grained models of complex molecular systems, such as proteins and smart materials for drug delivery.
This special issue aims to collect papers that explore end-to-end artificial intelligence applications in the biomedical field, focusing on the molecular characteristics of medical conditions and providing insights into their molecular mechanisms. Specifically, we seek ML and Deep Learning (DL) applications in drug discovery, repurposing, and disease modeling, including novel approaches to disease stratification.
Additionally, we aim to collect papers that demonstrate the use of ML and DL in molecular simulation to solve traditional challenges in this field [1], and to address disease modeling [2], with a priority on Explainable Artificial Intelligence approaches.
Overall, this special issue seeks to advance the field by presenting novel approaches and insights that demonstrate the potential of ML and molecular simulation in the biomedical field. We encourage submissions from diverse perspectives and scientific communities, including computer science, chemistry, physics, engineering, and biology. We also welcome submissions from interdisciplinary research teams that integrate these fields.
Potential topics for submission to this special issue include, but are not limited to:
- Machine learning models for drug discovery and repurposing
- Molecular simulation approaches for drug discovery and repurposing
- End-to-end artificial intelligence applications for disease modeling
- Explainable artificial intelligence approaches for molecular simulation and drug discovery
- Advanced molecular modeling techniques for drug delivery and biomaterials
- Molecular dynamics simulations of proteins and nucleic acids
- Quantum mechanical/molecular mechanical simulations
- Multi-scale/multi-physics simulations of biological systems
- Artificial intelligence-assisted design of molecules and materials for biomedicine
- Applications of molecular simulation and machine learning in precision medicine and personalized therapy.
We welcome original research articles, reviews, and perspectives on the latest advances and challenges in the field. Our goal is to publish a comprehensive collection of works that demonstrate the potential of molecular simulation and machine learning in advancing biomedical research.
We believe that this special issue will be a valuable resource for researchers, students, and practitioners in the fields of computational and experimental biomedical sciences. We look forward to receiving your submissions and hope that this special issue will facilitate the development of new methodologies and insights in the field of molecular simulation and machine learning for biomedical applications.
- Noé, F., Tkatchenko, A., Müller, K. R., & Clementi, C. (2020). Machine learning for molecular simulation. Annual review of physical chemistry, 71, 361–390.
- Park, D. J., Park, M. W., Lee, H., Kim, Y. J., Kim, Y., & Park, Y. H. (2021). Development of machine learning model for diagnostic disease prediction based on laboratory tests. Scientific reports, 11(1), 7567.
Dr. Andrea Termine
Guest Editor
Manuscript Submission Information
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Keywords
- molecular modeling
- molecular simulation
- drug discovery
- drug repurposing
- machine learning
- deep learning
- precision medicine
- artificial intelligence
- disease stratification
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