Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.7
4.8
Journals
Pharmaceuticals
Special Issues
QSAR and Chemoinformatics in Drug Design and Discovery
share announcement

QSAR and Chemoinformatics in Drug Design and Discovery

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 447

Special Issue Editors

Dr. Elena Cichero

E-Mail Website
Guest Editor
Department of Pharmacy, University of Genoa, Viale Benedetto XV, 16132 Genoa, Italy
Interests: GPCR-targeting compounds; enzymes; QSAR; virtual screening; computational methods; drug design; pharmacophore modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Naomi Scarano

E-Mail Website
Guest Editor
Institute of Technologies in Biomedicine, National Research Council, Via Fratelli Cervi 93, 20090 Segrate, Italy
Interests: medicinal chemistry; molecular modeling; docking; molecular dynamics; drug design; virtual screening; computer-aided drug design

Special Issue Information

Dear Colleagues,

The use of information resources and computers in search for novel bioactive compounds is a well-established practice in the field of pharmaceutical sciences. Chemoinformatics significantly contributes to the efficiency and quality of hit finding, as well as to candidate optimization, in early drug development. Virtual screening, QSAR, and machine learning models are only a few examples of the chemoinformatics techniques that have relevance to the drug design process. In addition, toxicoinformatics is devoted to the prediction of compounds’ toxicity and adverse effects. The most relevant advantages of computational techniques are the cost and time reduction resulting from the prioritization of only a subset of most probable hits. Today, these methods are widely applied and have allowed for advances in the clinics of several candidates. These techniques are constantly evolving, allowing for to perform molecular simulations on a larger scale and with improved performance. In this vibrant scenario, this Special Issue invites authors to submit original papers that exploit QSAR and chemoinformatics for the design of new bioactive molecules. Review articles reporting the state of the art and future perspectives are welcome as long as they align with the scope of this Special Issue. Topics of interest include (but are not limited to) advanced QSAR modeling, machine learning applications in drug discovery, ligand-based virtual screening and design, computer-aided drug repurposing, and toxicology- and pharmacokinetic-related predictive studies using computational methods.

Dr. Elena Cichero
Dr. Naomi Scarano
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. Pharmaceuticals 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 2900 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

  • QSAR modeling
  • machine learning
  • ligand-based virtual screening and design
  • computer-aided drug repurposing
  • computational methods
  • molecular modeling
  • molecular dynamics
  • drug design

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 (1 paper)

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

Research

22 pages, 8682 KiB  
Article
Predicting EGFRL858R/T790M/C797S Inhibitory Effect of Osimertinib Derivatives by Mixed Kernel SVM Enhanced with CLPSO
by Shaokang Li, Wenzhe Dong and Aili Qu
Pharmaceuticals 2025, 18(8), 1092; https://doi.org/10.3390/ph18081092 - 23 Jul 2025
Viewed by 259
Abstract
Background/Objectives: The resistance mutations EGFRL858R/T790M/C797S in epidermal growth factor receptor (EGFR) are key factors in the reduced efficacy of Osimertinib. Predicting the inhibitory effects of Osimertinib derivatives against these mutations is crucial for the development of more effective inhibitors. This study aims [...] Read more.
Background/Objectives: The resistance mutations EGFRL858R/T790M/C797S in epidermal growth factor receptor (EGFR) are key factors in the reduced efficacy of Osimertinib. Predicting the inhibitory effects of Osimertinib derivatives against these mutations is crucial for the development of more effective inhibitors. This study aims to predict the inhibitory effects of Osimertinib derivatives against EGFRL858R/T790M/C797S mutations. Methods: Six models were established using heuristic method (HM), random forest (RF), gene expression programming (GEP), gradient boosting decision tree (GBDT), polynomial kernel function support vector machine (SVM), and mixed kernel function SVM (MIX-SVM). The descriptors for these models were selected by the heuristic method or XGBoost. Comprehensive learning particle swarm optimizer was adopted to optimize hyperparameters. Additionally, the internal and external validation were performed by leave-one-out cross-validation (QLOO2), 5-fold cross validation (Q5−fold2) and concordance correlation coefficient (CCC), QF12, and QF22. The properties of novel EGFR inhibitors were explored through molecular docking analysis. Results: The model established by MIX-SVM whose kernel function is a convex combination of three regular kernel functions is best: R2 and RMSE for training set and test set are 0.9445, 0.1659 and 0.9490, 0.1814, respectively; QLOO2, Q5−fold2, CCC, QF12, and QF22 are 0.9107, 0.8621, 0.9835, 0.9689, and 0.9680. Based on these results, the IC50 values of 162 newly designed compounds were predicted using the HM model, and the top four candidates with the most favorable physicochemical properties were subsequently validated through PEA. Conclusions: The MIX-SVM method will provide useful guidance for the design and screening of novel EGFRL858R/T790M/C797S inhibitors. Full article
(This article belongs to the Special Issue QSAR and Chemoinformatics in Drug Design and Discovery)
►▼ Show Figures

Graphical abstract

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