Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.6
4.6
Journals
Molecules
Special Issues
Computational Approaches in Drug Discovery and Design
molecules-logo
Submit to Molecules Review for Molecules Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Computational Approaches in Drug Discovery and Design

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

Deadline for manuscript submissions: closed (19 December 2022) | Viewed by 13623

Special Issue Editors

Dr. Valeria V. Kleandrova

E-Mail Website
Guest Editor
Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production, Volokolamskoe Shosse 11, 125080 Moscow, Russia
Interests: QSAR-based approaches; chemoinformatics; computer-aided drug discovery; virtual screening
Prof. Dr. Alejandro Speck-Planche

E-Mail Website
Guest Editor
Department of Chemical Engineering, College of Science and Engineering, Universidad San Francisco de Quito (USFQ), Quito, Ecuador
Interests: multi-scale de novo drug design; multi-scale models; QSAR-based approaches; chemoinformatics; nanoinformatics; computer-aided drug discovery; virtual screening

Special Issue Information

Dear Colleagues,

The process known as drug discovery has provided a series of therapeutic solutions to treat and/or eradicate diseases. Yet, over time, current drugs have become less efficacious due to the emergence of phenomena such as drug resistance. At the same time, future drugs may be deemed to be not effective enough because of their mono-target mechanisms of action when treating diseases that are clearly multi-factorial (e.g., multiple proteins associated with disease progression). It is now well established that computational approaches have become essential in modern drug discovery projects, filtering the vast chemical space estimated to contain around 1060 pharmacologically active molecules. In this sense, computational models can accelerate either the design of new molecules with desired biological profile (improved activity, reduced toxicity, and adequate pharmacokinetic properties) or the discovery of novel therapeutic applications for existing drugs.

This Special Issue of Molecules aims to highlight the importance of computational approaches in different areas within drug discovery, which include, but are not limited to, pharmacology, toxicology, and pharmacokinetics. Thus, we invite the scientific community to submit their original contributions in the form of research articles, short communications, or reviews.

Dr. Valeria V. Kleandrova
Prof. Dr. Alejandro Speck-Planche
Guest Editors

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.

Keywords

  • multi-target drug discovery
  • chemoinformatics
  • QSAR-based approaches
  • structure- and ligand-based drug discovery
  • machine learning
  • bioinformatics
  • complex networks

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 1268 KiB  
Article
MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug–Enzyme Interactions
by Riccardo Concu, Maria Natália Dias Soeiro Cordeiro, Martín Pérez-Pérez and Florentino Fdez-Riverola
Molecules 2023, 28(3), 1182; https://doi.org/10.3390/molecules28031182 - 25 Jan 2023
Cited by 2 | Viewed by 3000
Abstract
Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a [...] Read more.
Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug–enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software. Full article
(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design)
►▼ Show Figures

Graphical abstract

21 pages, 13460 KiB  
Article
Structure-Activity Relationship Studies Based on 3D-QSAR CoMFA/CoMSIA for Thieno-Pyrimidine Derivatives as Triple Negative Breast Cancer Inhibitors
by Jin-Hee Kim and Jin-Hyun Jeong
Molecules 2022, 27(22), 7974; https://doi.org/10.3390/molecules27227974 - 17 Nov 2022
Cited by 10 | Viewed by 2338
Abstract
Triple-negative breast cancer (TNBC) is defined as a kind of breast cancer that lacks estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptors (HER2). This cancer accounts for 10–15% of all breast cancers and has the features of high invasiveness [...] Read more.
Triple-negative breast cancer (TNBC) is defined as a kind of breast cancer that lacks estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptors (HER2). This cancer accounts for 10–15% of all breast cancers and has the features of high invasiveness and metastatic potential. The treatment regimens are still lacking and need to develop novel inhibitors for therapeutic strategies. Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses, based on a series of forty-seven thieno-pyrimidine derivatives, were performed to identify the key structural features for the inhibitory biological activities. The established comparative molecular field analysis (CoMFA) presented a leave-one-out cross-validated correlation coefficient q2 of 0.818 and a determination coefficient r2 of 0.917. In comparative molecular similarity indices analysis (CoMSIA), a q2 of 0.801 and an r2 of 0.897 were exhibited. The predictive capability of these models was confirmed by using external validation and was further validated by the progressive scrambling stability test. From these results of validation, the models were determined to be statistically reliable and robust. This study could provide valuable information for further optimization and design of novel inhibitors against metastatic breast cancer. Full article
(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design)
►▼ Show Figures

Figure 1

55 pages, 18625 KiB  
Article
In Silico Approach for the Evaluation of the Potential Antiviral Activity of Extra Virgin Olive Oil (EVOO) Bioactive Constituents Oleuropein and Oleocanthal on Spike Therapeutic Drug Target of SARS-CoV-2
by Elena G. Geromichalou and George D. Geromichalos
Molecules 2022, 27(21), 7572; https://doi.org/10.3390/molecules27217572 - 4 Nov 2022
Cited by 7 | Viewed by 3648
Abstract
Since there is an urgent need for novel treatments to combat the current coronavirus disease 2019 (COVID-19) pandemic, in silico molecular docking studies were implemented as an attempt to explore the ability of selected bioactive constituents of extra virgin olive oil (EVOO) to [...] Read more.
Since there is an urgent need for novel treatments to combat the current coronavirus disease 2019 (COVID-19) pandemic, in silico molecular docking studies were implemented as an attempt to explore the ability of selected bioactive constituents of extra virgin olive oil (EVOO) to act as potent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) antiviral compounds, aiming to explore their ability to interact with SARS-CoV-2 Spike key therapeutic target protein. Our results suggest that EVOO constituents display substantial capacity for binding and interfering with Spike (S) protein, both wild-type and mutant, via the receptor-binding domain (RBD) of Spike, or other binding targets such as angiotensin-converting enzyme 2 (ACE2) or the RBD-ACE2 protein complex, inhibiting the interaction of the virus with host cells. This in silico study provides useful insights for the understanding of the mechanism of action of the studied compounds at a molecular level. From the present study, it could be suggested that the studied active phytochemicals could potentially inhibit the Spike protein, contributing thus to the understanding of the role that they can play in future drug designing and the development of anti-COVID-19 therapeutics. Full article
(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design)
►▼ Show Figures

Graphical abstract

20 pages, 636 KiB  
Article
Statistical Bioinformatics to Uncover the Underlying Biological Mechanisms That Linked Smoking with Type 2 Diabetes Patients Using Transcritpomic and GWAS Analysis
by Abu Sayeed Md. Ripon Rouf, Md. Al Amin, Md. Khairul Islam, Farzana Haque, Kazi Rejvee Ahmed, Md. Ataur Rahman, Md. Zahidul Islam and Bonglee Kim
Molecules 2022, 27(14), 4390; https://doi.org/10.3390/molecules27144390 - 8 Jul 2022
Cited by 2 | Viewed by 3677
Abstract
Type 2 diabetes (T2D) is a chronic metabolic disease defined by insulin insensitivity corresponding to impaired insulin sensitivity, decreased insulin production, and eventually failure of beta cells in the pancreas. There is a 30–40 percent higher risk of developing T2D in active smokers. [...] Read more.
Type 2 diabetes (T2D) is a chronic metabolic disease defined by insulin insensitivity corresponding to impaired insulin sensitivity, decreased insulin production, and eventually failure of beta cells in the pancreas. There is a 30–40 percent higher risk of developing T2D in active smokers. Moreover, T2D patients with active smoking may gradually develop many complications. However, there is still no significant research conducted to solve the issue. Hence, we have proposed a highthroughput network-based quantitative pipeline employing statistical methods. Transcriptomic and GWAS data were analysed and obtained from type 2 diabetes patients and active smokers. Differentially Expressed Genes (DEGs) resulted by comparing T2D patients’ and smokers’ tissue samples to those of healthy controls of gene expression transcriptomic datasets. We have found 55 dysregulated genes shared in people with type 2 diabetes and those who smoked, 27 of which were upregulated and 28 of which were downregulated. These identified DEGs were functionally annotated to reveal the involvement of cell-associated molecular pathways and GO terms. Moreover, protein–protein interaction analysis was conducted to discover hub proteins in the pathways. We have also identified transcriptional and post-transcriptional regulators associated with T2D and smoking. Moreover, we have analysed GWAS data and found 57 common biomarker genes between T2D and smokers. Then, Transcriptomic and GWAS analyses are compared for more robust outcomes and identified 1 significant common gene, 19 shared significant pathways and 12 shared significant GOs. Finally, we have discovered protein–drug interactions for our identified biomarkers. Full article
(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop