Special Issue "Computational Methods in Drug Design"
Deadline for manuscript submissions: 31 October 2021.
Molecular modeling and computational chemistry have become essential in the medicinal chemistry field today. In silico strategies represent powerful weapons commonly applied to accelerate drug discovery, design, and optimization campaigns, as well as to improve the knowledge and understanding of the biological processes implied in the mechanism of action of known drugs. Virtual screening protocols combining receptor-based and ligand-based techniques, such as molecular docking, pharmacophore modeling, and various types of ligand-similarity strategies, can speed up the identification of novel hit compounds endowed with inhibitory activity toward the targets of interest. Computational studies employing these and other techniques can help the rationalization of structure–activity relationships among chemical series of pharmacologically active compounds and guide hit-to-lead and lead optimization studies aimed at improving both the activity and pharmacokinetic properties of the ligands, in the search for suitable drug candidates. Moreover, advanced in silico methods based on molecular dynamics simulations and related techniques, in combination with experimental studies, can help to shed light on drug–target interactions, thus facilitating the design of more potent compounds. Finally, machine learning and artificial intelligence models have recently attracted interest for their application in the prediction of various ligand properties and biological activities, as well as in the prediction of potential receptors for active compounds with unknown molecular target (target-fishing).
On these basis, this Special Issue is focused on the development of valuable and innovative computer-aided drug design approaches, as well as on successful applications of in silico techniques and strategies in all aspects and stages of the drug design process. Scientists are thus invited to submit original research articles and reviews dealing with all kinds of molecular modeling studies applied to drug design, such as virtual screening studies, computer-aided hit-to-lead and lead optimization campaigns, molecular modeling studies focused on drug–target interactions and dynamics, development and application of target-fishing approaches, generation of innovative computational tools and models for the prediction of pharmacodynamics, and pharmacokinetic ligand properties.
Dr. Giulio Poli
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 papers will be 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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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.
- virtual screening
- molecular docking
- molecular dynamics
- pharmacophore modeling
- ligand-based similarity
- artificial intelligence
- computer-aided drug design