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Special Issue "Computational Approaches for Drug Discovery"

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 31 March 2019

Special Issue Editor

Guest Editor
Dr. Simone Brogi

Department of Pharmacy, Department of Excellence 2018-2022, University of Naples “Federico II”, Via D. Montesano 49, 80131 Napoli, Italy
Department of Biotechnology, Chemistry and Pharmacy, Department of Excellence 2018-2022, University of Siena, Via A. Moro 2, 53100 Siena, Italy
Website 1 | Website 2 | E-Mail
Interests: molecular modelling; computational medicinal chemistry; computer-aided drug design; drug discovery; bioinformatics

Special Issue Information

Dear Colleagues,

Nowadays, in silico methodologies have become a crucial part of the drug discovery process; mostly because they can boost the whole drug development trajectory, identifying and discovering new potential drugs with a significant reduction of the costs and time. Furthermore, computer-aided drug design (CADD) approaches are important for reducing the experimental use of animals for in vivo testing, for helping the design of safer drugs and for repositioning known drugs, assisting medicinal chemists in each step (design, discovery, development, and hit-optimization) during the drug discovery process. The conventional methods for drug discovery imply the costly random screening of synthesized compounds or natural products. On the other hand, the computational procedures can be very multifarious, requiring interdisciplinary studies and application of computer science to rationally design effective and commercially feasible drugs. Remarkable progresses have been made both in computer science field, that have speeded up the drug discovery research, and in the development of new experimental procedures for the characterization of biological targets. Among the methods in drug discovery, pharmacophore modelling, three-dimensional quantitative structure activity relationships (3D-QSAR), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) remain the preferred ligand-based (LB) methods for fast virtual screening (VS) procedures and for rationalizing the activities of a set of ligands. In a recent breakthrough, a novel approach in QSAR field is represented by the combination of the Molecular Dynamics (MD), and the relative computed descriptors, with the generation of QSAR models. This approach provides computational tools, the so-called MD-QSAR models, with an enhanced predictive power. When the information of the 3D structure of the targets in complex with ligands are known, structure-based (SB) drug design approaches such as SB pharmacophore models including excluded volumes or high throughput dockings are the elected methods for identifying novel chemical entities for a selected target. If we want to investigate ligand–receptor complexes and in general the dynamics and thermodynamics of biological systems, MD simulations represent one of the major computational resources and still remain the most representative technique for this kind of investigation. In addition, for better characterizing biological systems, understanding the mechanism of action of enzymes also in complex with ligands, quantum mechanics/molecular mechanics (QM/MM) calculations can be helpful in drug discovery and design. Currently, QM/MM can be combined with MD (QM/MM-MD) to completely characterize enzymatic mechanisms.

These tools can help the scientists to shorten the cycle of drug discovery, and thus make the process more cost-affordable. The huge technological progresses in hardware and software resources, algorithms design, as well as the biological advances for identifying new drug targets, make computer-assisted approaches the most valuable methods in pre-clinical research. For this Special Issue of Molecules, we invite researchers in the computational drug discovery field, to submit original research articles, short communications and review articles related to the in silico approaches used in Medicinal Chemistry.

Dr. Simone Brogi
Guest Editor

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. Molecules is an international peer-reviewed open access monthly 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 1800 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.


  • Molecular Modelling
  • Computer-Aided-Drug Design
  • Computational Chemistry
  • Drug Discovery
  • Computational Methods in Medicinal Chemistry

Published Papers (1 paper)

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Open AccessArticle A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs
Molecules 2018, 23(9), 2385; https://doi.org/10.3390/molecules23092385
Received: 24 August 2018 / Revised: 11 September 2018 / Accepted: 12 September 2018 / Published: 18 September 2018
PDF Full-text (5955 KB) | HTML Full-text | XML Full-text | Supplementary Files
A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure–toxicity relationship (QSTR) of these compounds. For the prediction research, a Protein-Protein Interaction (PPI) network was built
[...] Read more.
A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure–toxicity relationship (QSTR) of these compounds. For the prediction research, a Protein-Protein Interaction (PPI) network was built from the extraction of useful information about protein interactions connected with aconitine cardiotoxicity, based on nearly a decade of literature and the STRING database. The software Cytoscape and the PharmMapper server were utilized to screen for essential proteins in the constructed network. The Calcium-Calmodulin-Dependent Protein Kinase II alpha (CAMK2A) and gamma (CAMK2G) were identified as potential targets. To obtain a deeper insight on the relationship between the toxicity and the structure of aconitine alkaloids, the present study utilized QSAR models built in Sybyl software that possess internal robustness and external high predictions. The molecular dynamics simulation carried out here have demonstrated that aconitine alkaloids possess binding stability for the receptor CAMK2G. In conclusion, this comprehensive method will serve as a tool for following a structural modification of the aconitine alkaloids and lead to a better insight into the cardiotoxicity induced by the compounds that have similar structures to its derivatives. Full article
(This article belongs to the Special Issue Computational Approaches for Drug Discovery)

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