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Recent Research on Biomimetic Chromatography, QSAR and Chemoinformatics in Molecular Modeling and Drug Design

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 October 2025 | Viewed by 738

Special Issue Editor

Dr. Małgorzata Janicka

E-Mail Website
Guest Editor
Department of Physical Chemistry, Faculty of Chemistry, Maria Curie-Skłodowska University, Lublin, Poland
Interests: liquid chromatography; planar chromatography lipophilicity; QRARs; QSARs; pharmacokinetics; chemometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The search for new bioactive substances with desirable properties is a challenge that modern science will face. The aim is to improve the quality and length of human life and increase agricultural productivity without undesirable side effects, while preserving environmental biodiversity and preventing biodegradation. The capabilities and advantages of biomimetic chromatography make it an important in vitro technique to support the testing and modelling of bioactive compounds. In combination with in silico methods such as QSAR and/or QRAR and chemometrics, it enables the prediction of the physicochemical, biological and pharmacokinetic properties of bioactive substances. Such studies are used to obtain the most promising and representative structures that can be further used to design and enhance the success of drug discovery. Predicting mechanisms of action and the molecular factors influencing them provides valuable clues for the design and optimization of lead compounds. These innovative combined investigations offer an alternative to highly unethical animal testing and reduce not only costs but also the time required for testing.

This Special Issue focuses on the application of various biomimetic chromatographic techniques, molecular simulation and related computational methods to the drug discovery process, including virtual screening. As IJMS is a journal of molecular science, pure clinical studies are not suitable for this journal. However, clinical or pure model submissions with biomolecular experiments are welcome.

Dr. Małgorzata Janicka
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 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. 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

  • biomimetic chromatography
  • QSARs
  • QRARs
  • molecular modelling
  • chemoinformatics
  • pharmacokinetics

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Published Papers (2 papers)

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30 pages, 4011 KiB  
Article
Multitarget Design of Steroidal Inhibitors Against Hormone-Dependent Breast Cancer: An Integrated In Silico Approach
by Juan Rodríguez-Macías, Oscar Saurith-Coronell, Carlos Vargas-Echeverria, Daniel Insuasty Delgado, Edgar A. Márquez Brazón, Ricardo Gutiérrez De Aguas, José R. Mora, José L. Paz and Yovanni Marrero-Ponce
Int. J. Mol. Sci. 2025, 26(15), 7477; https://doi.org/10.3390/ijms26157477 - 2 Aug 2025
Viewed by 382
Abstract
Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha [...] Read more.
Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha (ER-α), and HER2. Using a robust 3D-QSAR model (R2 = 0.86; Q2_LOO = 0.86) built from 52 steroidal structures, we identified molecular features associated with high anticancer potential, specifically increased polarizability and reduced electronegativity. From a virtual library of 271 DFT-optimized analogs, 31 compounds were selected based on predicted potency (pIC50 > 7.0) and screened via molecular docking against PR (PDB 2W8Y), HER2 (PDB 7JXH), and ER-α (PDB 6VJD). Seven candidates showed strong binding affinities (ΔG ≤ −9 kcal/mol for at least two targets), with Estero-255 emerging as the most promising. This compound demonstrated excellent conformational stability, a robust hydrogen-bonding network, and consistent multitarget engagement. Molecular dynamics simulations over 100 nanoseconds confirmed the structural integrity of the top ligands, with low RMSD values, compact radii of gyration, and stable binding energy profiles. Key interactions included hydrophobic contacts, π–π stacking, halogen–π interactions, and classical hydrogen bonds with conserved residues across all three targets. These findings highlight Estero-255, alongside Estero-261 and Estero-264, as strong multitarget candidates for further development. By potentially disrupting the PI3K/AKT/mTOR signaling pathway, these compounds offer a promising strategy for overcoming resistance in hormone-driven breast cancer. Experimental validation, including cytotoxicity assays and ADME/Tox profiling, is recommended to confirm their therapeutic potential. Full article
(This article belongs to the Special Issue Recent Research on Biomimetic Chromatography, QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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23 pages, 5953 KiB  
Article
Computational Profiling of Monoterpenoid Phytochemicals: Insights for Medicinal Chemistry and Drug Design Strategies
by André Nogueira Cardeal dos Santos, Paulo Elesson Guimarães de Oliveira, José Ednésio da Cruz Freire, Sara Araújo dos Santos, José Eduardo Ribeiro Honório Júnior, Claudia Roberta de Andrade, Bruno Lopes de Sousa, Wildson Max Barbosa da Silva, Ariclécio Cunha de Oliveira, Vânia Marilande Ceccatto, José Henrique Leal Cardoso, Adélia Justina Aguiar Aquino and Andrelina Noronha Coelho de Sousa
Int. J. Mol. Sci. 2025, 26(16), 7671; https://doi.org/10.3390/ijms26167671 - 8 Aug 2025
Viewed by 193
Abstract
Monoterpenoids are a structurally diverse class of natural products with a long-standing history of therapeutic use. Despite their promising bioactivities, their clinical development has been limited by dose-dependent toxicities, poor pharmacokinetics, and suboptimal drug-like properties. In this work, a comprehensive in silico pipeline [...] Read more.
Monoterpenoids are a structurally diverse class of natural products with a long-standing history of therapeutic use. Despite their promising bioactivities, their clinical development has been limited by dose-dependent toxicities, poor pharmacokinetics, and suboptimal drug-like properties. In this work, a comprehensive in silico pipeline was employed to evaluate 1175 monoterpenoid compounds retrieved from ChEBI, aiming to identify structurally diverse candidates that possess favorable drug-like characteristics. A total of 54 molecular parameters were calculated using thirteen computational tools, covering physicochemical parameters, ADMET profiles, and toxicological risk assessments. Stepwise filtering was employed to retain only compounds meeting stringent thresholds across multiple domains, followed by chemoinformatic analysis. Structure–activity relationship mapping and target prediction were subsequently conducted to explore mechanistic plausibility. This workflow led to the identification of seven top-performing monoterpenoids that exhibited ideal physicochemical profiles, high gastrointestinal absorption, low predicted toxicity, and full compliance with medicinal chemistry rules. Notably, target prediction revealed a convergence on GPCRs, enzymatic and nuclear receptors, highlighting potential anti-inflammatory and neuromodulatory effects. The identification of conserved pharmacophores across selected scaffolds further reinforces their translational potential. Our results highlight the value of multi-parameter computational triage in natural product drug discovery and reveal a subset of overlooked monoterpenoids with promising preclinical applications. Full article
(This article belongs to the Special Issue Recent Research on Biomimetic Chromatography, QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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