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

Journal Menu

Journal Browser

Benchmarking of Modeling and Informatic Methods in Molecular Sciences

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: 20 June 2026 | Viewed by 7972

Share This Special Issue

Special Issue Editor

Prof. Dr. Abby Parrill

E-Mail Website
Guest Editor

Department of Chemistry, University of Memphis, Memphis, TN 38152, USA
Interests: computer aided drug design; molecular docking; QSAR; pharmacophore modeling; virtual screening; structural bioinformatics; molecular modeling; machine learning; G protein-coupled receptors (GPCRs); medicinal chemistry

Special Issue Information

Dear Colleagues,

Modeling and informatic methods are commonly applied in research in areas such as drug discovery, chemical biology, macromolecular engineering and design, genome analysis and annotation, and in the design of nanomolecular structures. Numerous software applications and web-based tools are available for specific modeling or informatic tasks. Rigorous comparisons of the results extracted via these available methods made against each other and against results from appropriate experiments are essential benchmarking tasks that help determine which method will provide the most accurate results in different application areas, discern the influence of user selection operational parameters on the quality of the results obtained, and inform the selection of the most accurate software application or tool in future research studies. Contributions to this Special Issue will provide rigorous benchmarking results to guide the selection of modeling and informatic methods for future research in the molecular sciences.

Prof. Dr. Abby Parrill
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 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

modeling
informatics
simulation
benchmarking
performance comparison
modeling method evaluation
informatic method evaluation

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 (6 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

23 pages, 16103 KB

Open AccessArticle

From Local Tissue Repair to Fibrosis: Deciphering Gene Co-Expression Networks in Benign Pulmonary Nodules and Idiopathic Pulmonary Fibrosis Comorbidity via Bioinformatics and Machine Learning

by Yaoyu Xie, Jingzhe Gao, Yifan Ren, Xiaoran Sun, Siju Lou, Guangli Yan, Ning Zhang, Hui Sun and Xijun Wang

Int. J. Mol. Sci. 2026, 27(8), 3647; https://doi.org/10.3390/ijms27083647 - 19 Apr 2026

Viewed by 215

Abstract

With increasing environmental pollution and a high incidence of respiratory infections, pulmonary nodules (PN) are being detected more frequently. Although most are benign, they are often accompanied by chronic inflammation and localized fibrosis, which may predispose patients to progression toward idiopathic pulmonary fibrosis (IPF). However, the biological relationship between benign pulmonary nodules (BPNs) and IPF remains poorly understood. Therefore, this study aims to investigate the shared molecular mechanisms and identify potential biomarkers linking BPN and IPF, with the goal of elucidating the pathogenic transition from BPN to IPF. In this study, microarray data from GEO datasets were systematically analyzed to explore shared molecular mechanisms, immune infiltration characteristics, and potential early intervention strategies linking BPN and IPF. Differential expression analysis, protein–protein interaction (PPI) networks, weighted gene co-expression network analysis (WGCNA), and integrative machine learning approaches identified MME and ANKRD23 as key hub genes associated with the transition from BPN to IPF. Both genes demonstrated strong diagnostic performance, with Area Under the Curve (AUC) values exceeding 0.7, and were significantly correlated with immune cell infiltration, particularly effector memory CD8⁺ T cells. Functional enrichment and gene set enrichment analyses indicated that these genes were mainly involved in immune-related processes in BPN, while in IPF, ANKRD23 was linked to cytoskeletal organization and genomic stability, and MME was enriched in profibrotic pathways such as TGF-β signaling. The diagnostic value of these biomarkers was further validated in a bleomycin-induced IPF mouse model using quantitative polymerase chain reaction (qPCR). In addition, drug–gene interaction prediction and molecular docking analyses highlighted several naturally derived compounds with favorable binding affinity and anti-inflammatory properties, among which folic acid, curcumin, and arbutin emerged as promising candidates for safe early intervention. Collectively, these findings identify MME and ANKRD23 as potential biomarkers for early identification of BPN patients at risk of developing IPF and provide a theoretical basis for early diagnosis and targeted preventive strategies. Full article

(This article belongs to the Special Issue Benchmarking of Modeling and Informatic Methods in Molecular Sciences)

► Show Figures

Figure 1

30 pages, 4440 KB

Open AccessArticle

Computational Identification of Potential Novel Allosteric IHF Inhibitors Using QSAR Modeling to Inhibit Plasmid-Mediated Antibiotic Resistance

by Oscar Saurith-Coronell, Olimpo Sierra-Hernandez, Juan David Rodríguez-Macías, José R. Mora, Noel Perez-Perez, Jackson J. Alcázar, Ricardo Olimpio de Moura, Igor José dos Santos Nascimento, Edgar A. Márquez Brazón and Yovani Marrero-Ponce

Int. J. Mol. Sci. 2026, 27(6), 2526; https://doi.org/10.3390/ijms27062526 - 10 Mar 2026

Viewed by 720

Abstract

The rapid spread of antibiotic resistance through plasmid-mediated conjugation remains a primary global health concern. Despite its critical role in horizontal gene transfer, no approved drugs currently target this process, leaving a critical therapeutic gap. Integration Host Factor (IHF), a DNA-binding protein essential for plasmid replication and mobilization, emerges as a promising yet underexplored target for anti-conjugation strategies. This work aimed to develop a predictive computational model and identify small molecules that disrupt IHF function, thereby reducing plasmid transfer and limiting resistance gene dissemination. A curated dataset of 65 compounds with reported anti-plasmid activity was analyzed using a 3D-QSAR model based on algebraic descriptors computed with QuBiLS-MIDAS. The model was validated through leave-one-out cross-validation (Q² = 0.82), Tropsha’s criteria, and Y-scrambling. Representative compounds were selected via pharmacophore clustering and evaluated through molecular docking at both the DNA-binding site and a predicted allosteric pocket of IHF. The most promising complexes underwent 200 ns molecular dynamics simulations to assess stability and interaction patterns. The QSAR model demonstrated strong predictive performance (R² = 0.90). Docking simulations revealed more favorable binding energies at the allosteric site (up to −12.15 kcal/mol) compared to the DNA-binding site. Molecular dynamics confirmed the stability of these interactions, with allosteric complexes showing lower RMSD fluctuations and consistent binding energy profiles. Dynamic cross-correlation analysis revealed that allosteric ligand binding induces conformational changes in key catalytic residues, including Pro65, Pro61, and Leu66. These alterations may compromise DNA recognition and disrupt the initiation of replication. To our knowledge, this is the first computational study proposing allosteric inhibition of IHF as an anti-conjugation strategy. These findings provide a foundation for experimental validation and the development of novel agents to prevent horizontal gene transfer, offering a promising approach to restoring antibiotic efficacy against multidrug-resistant pathogens. Full article

(This article belongs to the Special Issue Benchmarking of Modeling and Informatic Methods in Molecular Sciences)

► Show Figures

Figure 1

18 pages, 1311 KB

Open AccessArticle

Benchmarking edgeR and methylKit for the Detection of Differential DNA Methylation: A Methodological Evaluation

by Iraia Muñoa-Hoyos, Manu Araolaza, Irune Calzado, Mikel Albizuri and Nerea Subirán

Int. J. Mol. Sci. 2026, 27(4), 1964; https://doi.org/10.3390/ijms27041964 - 18 Feb 2026

Viewed by 675

Abstract

Despite the improvements in tool development for DNA methylation analysis, there is a lack of a consensus on computational and statistical models used for differentially methylated cytosine (DMC) identification. This variability complicates the interpretation of findings and raises concerns about the reproducibility and biological significance of the detected results. In this regard, here we conducted a comparative evaluation of edgeR and methylKit tools to assess their performance, concordance, and biological relevance in detecting DMCs following a morphine exposure model in mouse embryonic stem cells (mESCs). Both pipelines were applied to the same WGBS dataset (GEO accession number: GSE292082), and concordance was calculated at both single-base and gene levels. Although the total number of DMCs identified differed between tools, both pipelines detected a global hypomethylation pattern. Genomic distribution analysis revealed that DMCs predominantly localized to intergenic and intronic regions, as well as to open sea regions. Despite differences in sensitivity, both pipelines demonstrated moderate concordance at the DMC level (~56%) and high concordance at the gene level (~90%), identifying largely overlapping sets of differentially methylated genes (DMGs). Comparative assessments further showed that the choice of statistical metric can influence the perceived magnitude of biological effects. Sensitivity analyses indicated that threshold selection and normalization methods influence DMC detection, whereas aggregation at gene level reduces discrepancies. Overall, our findings underscore the complementary strengths of methylKit and edgeR and highlight the importance of careful tool selection for epigenetic studies. As a conclusion, we recommend integrating both pipelines to ensure a balanced interpretation of effect sizes, particularly in studies with complex experimental designs. Full article

(This article belongs to the Special Issue Benchmarking of Modeling and Informatic Methods in Molecular Sciences)

► Show Figures

Graphical abstract

24 pages, 3665 KB

Open AccessArticle

In Silico Development of Novel Quinazoline-Based EGFR Inhibitors via 3D-QSAR, Docking, ADMET, and Molecular Dynamics

by Mohamed Moussaoui, Soukayna Baammi, Mouna Baassi, Said Kerraj, Hatim Soufi, Younes Rachdi, Mohammed El Idrissi, Mohammed Salah, Mohammed Elalaoui Belghiti, Rachid Daoud and Said Belaaouad

Int. J. Mol. Sci. 2026, 27(2), 1050; https://doi.org/10.3390/ijms27021050 - 21 Jan 2026

Viewed by 835

Abstract

A library of thirty-one quinazoline derivatives was assessed as potential inhibitors of epidermal growth factor receptor kinase (EGFR) using 3D-QSAR methods, namely Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Training and test sets were generated by aligning the molecules to the lowest-energy conformer of the most active compound. The optimized models exhibited strong statistical performance, with R² values of 0.981 (CoMFA) and 0.978 (CoMSIA), and cross-validation coefficients (Q²) of 0.645 and 0.729, respectively. External validation confirmed their predictive power, yielding R² values of 0.929 and 0.909. Guided by these models, eighteen new quinazoline candidates were designed and evaluated for drug likeness and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties using in silico approaches. Molecular docking and molecular dynamics simulations highlighted the binding features and stability of these derivatives, with compound Pred65 demonstrating superior affinity and stability compared to Erlotinib. Collectively, the study provides valuable insights for the optimization of quinazoline scaffolds as EGFR inhibitors, supporting the development of promising anticancer leads. Full article

(This article belongs to the Special Issue Benchmarking of Modeling and Informatic Methods in Molecular Sciences)

► Show Figures

Figure 1

21 pages, 9432 KB

Open AccessArticle

Exploring the Anticancer Potential of Proton Pump Inhibitors by Targeting GRP78 and V-ATPase: Molecular Docking, Molecular Dynamics, PCA, and MM-GBSA Calculations

by Abdo A. Elfiky, Kirolos R. Mansour, Yousef Mohamed, Yomna Kh. Abdelaziz and Ian A. Nicholls

Int. J. Mol. Sci. 2025, 26(17), 8170; https://doi.org/10.3390/ijms26178170 - 22 Aug 2025

Viewed by 1910

Abstract

Cancer cells can adapt to their surrounding microenvironment by upregulating glucose-regulated protein 78 kDa (GRP78) and vacuolar-type ATPase (V-ATPase) proteins to increase their proliferation and resilience to anticancer therapy. Therefore, targeting these proteins can obstruct cancer progression. A comprehensive computational study was conducted to investigate the inhibitory potential of four proton pump inhibitors (PPIs), dexlasnoprazole (DEX), esomeprazole (ESO), pantoprazole (PAN), and rabeprazole (RAB), against GRP78 and V-ATPase. Molecular docking revealed high-affinity scores for PPIs against both proteins. Moreover, molecular dynamics showed favorable root mean square deviation values for GRP78 and V-ATPase complexes, whereas root mean square fluctuations were high at the substrate-binding subdomains of GRP78 complexes and the α-helices of V-ATPase. Meanwhile, the radius of gyration and the surface-accessible surface area of the complexes were not significantly affected by ligand binding. Trajectory projections of the first two principal components showed similar motions of GRP78 structures and the fluctuating nature of V-ATPase structures, while the free-energy landscape revealed the thermodynamically favored GRP78-RAB and V-ATPase-DEX conformations. Furthermore, the binding free energy was −16.59 and −18.97 kcal/mol for GRP78-RAB and V-ATPase-DEX, respectively, indicating their stability. According to our findings, RAB and DEX are promising candidates for GRP78 and V-ATPase inhibition experiments, respectively. Full article

(This article belongs to the Special Issue Benchmarking of Modeling and Informatic Methods in Molecular Sciences)

► Show Figures

Figure 1

Review

Jump to: Research

23 pages, 1450 KB

Open AccessReview

Bacterial Systematic Genetics and Integrated Multi-Omics: Beyond Static Genomics Toward Predictive Models

by Tatsuya Sakaguchi, Yuta Irifune, Rui Kamada and Kazuyasu Sakaguchi

Int. J. Mol. Sci. 2025, 26(19), 9326; https://doi.org/10.3390/ijms26199326 - 24 Sep 2025

Cited by 1 | Viewed by 2952

Abstract

The field of bacterial systems biology is rapidly advancing beyond static genomic analyses, and moving toward dynamic, integrative approaches that connect genetic variation with cellular function. This review traces the progression from genome-wide association studies (GWAS) to multi-omics frameworks that incorporate transcriptomics, proteomics, and interactome mapping. We emphasize recent breakthroughs in high-resolution transcriptomics, including single-cell, spatial, and epitranscriptomic technologies, which uncover functional heterogeneity and regulatory complexity in bacterial populations. At the same time, innovations in proteomics, such as data-independent acquisition (DIA) and single-bacterium proteomics, provide quantitative insights into protein-level mechanisms. Experimental and AI-assisted strategies for mapping protein–protein interactions help to clarify the architecture of bacterial molecular networks. The integration of these omics layers through quantitative trait locus (QTL) analysis establishes mechanistic links between single-nucleotide polymorphisms and systems-level phenotypes. Despite persistent challenges such as bacterial clonality and genomic plasticity, emerging tools, including deep mutational scanning, microfluidics, high-throughput genome editing, and machine-learning approaches, are enhancing the resolution and scope of bacterial genetics. By synthesizing these advances, we describe a transformative trajectory toward predictive, systems-level models of bacterial life. This perspective opens new opportunities in antimicrobial discovery, microbial engineering, and ecological research. Full article

(This article belongs to the Special Issue Benchmarking of Modeling and Informatic Methods in Molecular Sciences)

► Show Figures

Journal Menu

Journal Browser

Benchmarking of Modeling and Informatic Methods in Molecular Sciences

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (6 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI