Pharmaceutical Modelling in Physical Chemistry
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Physical Chemistry".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 5216
Special Issue Editors
Interests: force field; enhanced sampling; host–guest binding; liquids
Special Issues, Collections and Topics in MDPI journals
Interests: gaussian accelerated dynamics simulations; binding free energy calculations; RNA–ligand identification
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; generative models; ab initio calculation; molecular dynamics
Special Issue Information
Dear Colleagues,
In recent years, the fusion of techniques with various origins (e.g., molecular dynamics and machine learning) has made molecular modelling a powerful tool in pharmaceutical research. At the macroscale, with the aid of advanced computational techniques, the chemical and biophysical research communities have witnessed a number of accelerated digital discoveries of pharmaceutically active agents. Semi-empirical and physics-based scoring functions, machine-learning predictors and atomistic free energy calculations have been dominantly applied in academic and industrial drug discovery projects. A ladder of computational tools is often constructed based on the predictive power and the computational cost. Notably, machine-learning techniques as a complement to biophysical models have exhibited exceptional potential in various areas involved in pharmaceutical research, e.g., 2D and 3D molecular generative models in the case of ligand-based or structure-based de novo drug designs, chemical synthesis accessibility predictions and retrosynthesis analysis to accelerate the iterations between wet and dry experiment in drug developments. On the other hand, for individual systems of great biophysical importance but without sufficient understanding at the atomistic level, molecular modelling contributes significantly to the elucidation of the underlying mechanisms of biochemical and biophysical events. For example, the binding pathway and multi-modal binding behaviours unobserved experimentally could be explored via enhanced sampling simulations with all-atom force fields for protein–ligand complexes.
Recognizing the recent development of novel strategies and pivotal applications in the molecular modelling and digital discovery of pharmaceutical agents, the Molecules journal provides an open invitation to the computational biophysics and chemistry research community to contribute to a Special Issue entitled ‘Pharmaceutical Modelling’. As suggested by the title, this Special Issue welcomes manuscripts relevant to the molecular modelling of pharmaceutical agents, including, e.g., molecular simulations of protein–protein, protein–ligand and host–guest complexes, machine-learning-augmented drug discovery and generative models on drug-like molecules and drug-biomacromolecule assemblies.
Dr. Zhaoxi Sun
Dr. Jianzhong Chen
Guest Editors
Dr. Mingyuan Xu
Dr. Meiting Wang
Guest Editor Assistants
Manuscript Submission Information
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Keywords
- pharmaceutical modelling
- virtual screening
- machine learning
- molecular dynamics
- enhanced sampling
- ab initio calculations
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