Submit to Special Issue Submit Abstract to Special Issue Review for IJMS Propose a Special Issue

Journal Menu

Journal Browser

Computer-Aided Drug Discovery: Insights from Computational Chemistry and Cheminformatics

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 3098

Share This Special Issue

Special Issue Editors

Dr. Ismail Hdoufane

E-Mail Website
Guest Editor

Laboratory of Molecular Chemistry, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech 40000, Morocco
Interests: computational chemistry; CADD; ligand-based and structure-based drug design; virtual screening; QSAR modeling

Dr. Mehdi Oubahmane

E-Mail Website
Guest Editor

International Water Research Institute (IWRI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco
Interests: cheminformatics; computational chemistry; molecular modeling; machine learning in drug design; computer-aided drug discovery

Special Issue Information

Dear Colleagues,

Computer-aided drug design (CADD) now plays an increasingly central role in pharmaceutical research, supporting the discovery and optimization of new therapeutic candidates. As computational power and data accessibility continue to grow, in silico methods allow researchers to explore chemical space more effectively, predict molecular behavior, and design biologically active compounds with greater accuracy and reduced experimental cost.

Advances in computational chemistry and cheminformatics, along with machine learning, artificial intelligence, and modern molecular modeling techniques, have greatly expanded the analytical and predictive capabilities available to researchers. These tools accelerate early-stage drug discovery, improve rational molecular design, and help prioritize molecules with favorable properties for further investigation.

This Special Issue aims to highlight recent advances, methodologies, and applications in CADD, spanning from molecular docking and molecular dynamics to data-driven cheminformatics pipelines, predictive modeling, and AI-enabled lead optimization. Contributions that integrate in silico strategies with experimental validation, predictive QSAR modeling, biomolecular target prioritization, or large-scale virtual screening are highly encouraged.

We welcome original research articles, short communications, and a limited number of review papers. Suitable topics include, but are not limited to, the following:

Computational drug design and virtual screening;
Cheminformatics and chemical data mining;
Machine learning and deep learning approaches in drug discovery;
Molecular docking, molecular dynamics, and QM/MM simulations;
Pharmacophore modeling and QSAR/QSPR studies;
Fragment-based and structure-based drug discovery;
Integration of computational and experimental methodologies.

We look forward to receiving your contributions.

Dr. Ismail Hdoufane
Dr. Mehdi Oubahmane
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

computer-aided drug design (CADD)
computational chemistry
cheminformatics
molecular dynamics simulations
molecular modeling
QSAR modeling
virtual screening
artificial intelligence in drug discovery

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 Show export options

Select all

Export citation of selected articles as:

Research

18 pages, 6905 KB

Open AccessArticle

Structure-Guided Repurposing of Approved Drugs Identifies Aprepitant and Mavorixafor as Putative δ-Opioid Receptor Agonist Candidates

by Rocco Buccheri, Carlo Reale, Alessandro Coco, Carmela Parenti, Lorella Pasquinucci and Antonio Rescifina

Int. J. Mol. Sci. 2026, 27(9), 3823; https://doi.org/10.3390/ijms27093823 - 25 Apr 2026

Viewed by 446

Abstract

δ-opioid receptor (DOR) is a promising therapeutic target for developing safer treatments for pain and neuroprotection. In this study, we applied a structure-guided drug-repurposing workflow to identify FDA-approved drugs with predicted DOR-binding and agonist-like structural features. Using a validated GNINA-based docking protocol with an active-state DOR model (PDB ID: 6PT3), we screened 2342 approved compounds and identified 39 candidates with predicted submicromolar binding affinities. These hits were further evaluated through molecular dynamics simulations, binding pocket volume analysis, and principal component analysis, which enabled the prioritization of two leading candidates, aprepitant and mavorixafor. Both compounds formed stable receptor-ligand complexes, maintained persistent interactions with Asp128, promoted contraction of the orthosteric pocket, and retained favorable redocking scores on the MD-refined receptor conformations. Overall, these results identify aprepitant and mavorixafor as promising putative DOR agonists and provide a rational foundation for their experimental validation through binding, functional, and in vivo pain studies in the future. Full article

(This article belongs to the Special Issue Computer-Aided Drug Discovery: Insights from Computational Chemistry and Cheminformatics)

► Show Figures

Figure 1

41 pages, 15575 KB

Open AccessArticle

Network Pharmacology-Guided Identification of Candida albicans Secondary Metabolites as Modulators of HIV Latency via Oncogenic Signaling Pathways

by Ernest Oduro-Kwateng, Ugochukwu J. Anyaneji, Asiphe Fanele, Ntokozo Ntanzi, Mahmoud E. Soliman and Nompumelelo P. Mkhwanazi

Int. J. Mol. Sci. 2026, 27(7), 3125; https://doi.org/10.3390/ijms27073125 - 30 Mar 2026

Viewed by 729

Abstract

HIV latency, driven by a complex interplay of host factors, remains a key barrier to viral clearance. Current latency-reversing agents (LRAs) demonstrate limited efficacy and specificity, and none have been approved for clinical use. Although natural products have shown promise as LRAs, the therapeutic potential of fungal metabolites remains underexplored. Candida albicans, a prevalent human commensal and opportunistic pathogen, produces diverse secondary metabolites that can influence host pathways, affecting latency dynamics. This study aimed to investigate the latency-modulating potential of secondary metabolites of C. albicans using an integrative network pharmacology and computational pipeline. C. albicans secondary metabolites were retrieved from the literature, screened for drug-likeness, and mapped to human targets and biological pathways annotated in HIV latency. Key metabolites, hub genes, and pathways were systematically characterized through network and computational analyses. Six drug-like candidates, identified from 185 absorption, distribution, metabolism, excretion, and toxicity (ADMET)-screened metabolites, collectively mapped to 369 human genes with a 6.5% overlap in HIV latency (176 shared and 20 hub genes). These overlapping genes were significantly enriched for signal transduction, membrane localization, and adaptive responses to chemical stimuli. Kyoto encyclopedia of genes and genomes (KEGG) enrichment revealed oncogenic diseases (non-small cell lung, pancreatic, and prostate cancers) and latency-associated cascades, including PD-L1/PD-1, HIF-1, Ras, PI3K-Akt, calcium, and cAMP signaling. Six hub targets (MAPK1, PIK3CA, MAPK3, EGFR, MTOR, and AKT1) were consistently annotated within the top 30 KEGG pathways and displayed strong binding affinities for MET 15 and MET 119. Molecular dynamics (MD) simulations confirmed favorable binding free energies (BFEs) and stable conformational dynamics for the top-ranked metabolite MET 15. C. albicans secondary metabolites preferentially target oncogenic signaling networks central to HIV latency maintenance, notably PI3K/AKT/MTOR and MAPK/ERK, which regulate cell survival, metabolic homeostasis, and viral transcriptional repression. MET 15 is a top-ranked candidate metabolite for HIV latency-reversing therapeutics and warrants experimental validation in established latency models. Full article

(This article belongs to the Special Issue Computer-Aided Drug Discovery: Insights from Computational Chemistry and Cheminformatics)

► Show Figures

Graphical abstract

29 pages, 5236 KB

Open AccessArticle

QSAR-Guided and Fragment-Based Drug Design of Monoterpenoid Inhibitors Targeting Ebola Virus Glycoprotein

by Nouhaila Ait Lahcen, Wissal Liman, Saad Zekri, Adnane Ait Lahcen, Ashwag S. Alanazi, Mohammed M. Alanazi, Christelle Delaite, Mohamed Maatallah and Driss Cherqaoui

Int. J. Mol. Sci. 2026, 27(7), 2987; https://doi.org/10.3390/ijms27072987 - 25 Mar 2026

Cited by 1 | Viewed by 672

Abstract

Ebola virus disease remains one of the most serious viral infections with no approved small-molecule treatments. The Ebola virus glycoprotein (EBOV-GP), which enables the virus’s entry to host cells, is a promising target for drug discovery. In this study, a multistage computer-aided drug discovery approach was used to identify new specific EBOV-GP inhibitors. A reliable QSAR model was built using 55 terpenoid derivatives. This model was able to predict the activity of newly designed compounds with good accuracy and validated statistical metrics (

R_{t r}^{2}

= 0.70;

R_{e x t}^{2}

= 0.73). It was subsequently applied to screen over 15,500 newly generated compounds from three lead molecules by fragment-based design tools. Predicted activity, binding affinity toward EBOV-GP, and good ADMET drug-like properties prioritized the eleven most promising hits. Through 150 ns molecular dynamics simulations, these compounds remained stable in the EBOV-GP binding site. Further binding free energy analysis (MM/PBSA) showed strong binding affinities, especially for the compounds L-60, L-832, M-1618, and L-1366. This study showed how combining QSAR, fragment-based design, docking, ADMET, and molecular dynamics could help in identifying potent and safe small molecules against the EBOV-GP. The top compounds are ready for further experimental and in vitro biological testing. Full article

(This article belongs to the Special Issue Computer-Aided Drug Discovery: Insights from Computational Chemistry and Cheminformatics)

► Show Figures

Graphical abstract

28 pages, 5436 KB

Open AccessArticle

Discovery of Novel Molecular Scaffolds to Overcome Pseudomonas aeruginosa Aminoglycoside Resistance: Insights for a Consensus Scoring Rational Design Approach

by Francesco Iesce, Jochem Nelen, Alejandro Rodríguez-Martínez, Carlos Martínez-Cortés, Cristina Minnelli, Giovanna Mobbili, Alessandra Di Gregorio, Carla Vignaroli, Horacio Pérez-Sánchez and Roberta Galeazzi

Int. J. Mol. Sci. 2026, 27(6), 2642; https://doi.org/10.3390/ijms27062642 - 13 Mar 2026

Viewed by 596

Abstract

The berberine derivative 13-(2-methylbenzyl)-berberine (BED) has been shown to inhibit the MexXY-OprM efflux system of Pseudomonas aeruginosa (PA), a key contributor to aminoglycoside resistance, by interacting with the inner membrane protein MexY at an allosteric pocket (ALP). To enhance binding efficacy, this study aims to identify novel chemical scaffolds that target the MexY allosteric pocket through an integrated computational strategy. In this work, a ligand-based virtual screening (LBVS) approach was employed using a 2D/3D pharmacophore model derived from BED to perform in silico screening of an Enamine compound library, which encompasses a broad and diverse chemical space. A key objective was to compare the predictive performance of this pharmacophore-based workflow with a structure-based (SB) strategy incorporating molecular docking and molecular dynamics (MD) simulations. Notably, the top-ranked LBVS hits were consistently validated by docking and MD analyses, showing stable binding and interaction patterns comparable or superior to those of BED. This convergence between ligand-based (LB) and SB methods highlights the internal coherence of the workflow and supports the robustness of the pharmacophore hypothesis. The identified scaffolds generally displayed high hydrophobicity, consistent with the physicochemical nature of the binding site, but resulting in limited aqueous solubility and complicating their experimental evaluation. While these features confirm the importance of hydrophobic interactions in MexY recognition, with a particular focus on some few residues, such as Phe560, it also underscores the need for formulation strategies or rational scaffold modifications introducing moderate polarity without weakening key contacts. Overall, the integrated computational strategy not only yields promising lead chemical structures but also provides a solid basis for their future optimization, ultimately supporting the design of new efflux pump inhibitors (EPIs) capable of contributing to improved antibiotic susceptibility in multidrug-resistant PA strains. Full article

(This article belongs to the Special Issue Computer-Aided Drug Discovery: Insights from Computational Chemistry and Cheminformatics)

► Show Figures

Journal Menu

Journal Browser

Computer-Aided Drug Discovery: Insights from Computational Chemistry and Cheminformatics

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI