Pharmaceutical Modelling in Physical Chemistry

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• pharmaceutical modelling
• virtual screening
• machine learning
• molecular dynamics
• enhanced sampling
• ab initio calculations