Hybrid Quantum–Classical (QM/MM) and Classical (MM) Molecular Dynamics (MD) Approaches in Pharmaceutical Research
A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".
Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 443
Special Issue Editor
Special Issue Information
Dear Colleagues,
With ever-increasing computing power, the continuous improvement of existing approaches, and the development of novel techniques, the field of biosimulations is undergoing constant advances. This is particularly true for a plethora of methodologies that are not always the first choice in highly automated drug discovery pipelines, owing to their perceived computational cost and poor portability. Such methodologies include classical atomistic molecular dynamics simulations (both biased and unbiased) and hybrid quantum–classical (QM/MM) calculations, which are still not as frequently employed as more automated methods and less expensive methods, such as docking and pharmacophore-based virtual screening.
Yet, not only have these tools become more affordable in recent years, but their relevance to drug discovery has also increased, with strategies moving from the inhibition of orthosteric sites in traditional therapeutic targets to drugging distal allosteric sites in novel targets with more subtle repercussions on reactivity. Furthermore, the emergence of increasingly sophisticated machine learning algorithms has paved the way for an even more significant reduction in the computational costs associated with these methods, potentially making them even more attractive.
In light of the increasing relevance of more elaborate biosimulation methods to pharmaceutical research, the aim of this Special Issue is to collect the latest contributions in this rapidly evolving area, with particular emphasis on the benefits that computational methods are able to offer when used in synergy with experimental drug discovery resources.
Dr. Stefano A. Serapian
Guest Editor
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Keywords
- computational medicinal chemistry
- QM/MM methods
- molecular dynamics simulations
- allosteric ligands
- machine learning in drug design
- multiscale simulation methods
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