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

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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: 23 October 2026 | Viewed by 6285

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Dr. Małgorzata Janicka

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Department of Physical Chemistry, Faculty of Chemistry, Maria Curie-Skłodowska University, Lublin, Poland
Interests: liquid chromatography; planar chromatography lipophilicity; QRARs; QSARs; pharmacokinetics; chemometrics
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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

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Keywords

biomimetic chromatography
QSARs
QRARs
molecular modelling
chemoinformatics
pharmacokinetics

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Published Papers (4 papers)

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30 pages, 4011 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1983

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 (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 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2592

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 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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21 pages, 5045 KB

Open AccessArticle

Coprogen B from Talaromyces marneffei ΔsreA: Rapid Iron Chelation and Favorable Partitioning to Deferoxamine

by Bishant Pokharel, Wachiraporn Tipsuwan, Monsicha Pongpom, Teera Chewonarin, Pimpisid Koonyosying, Agostino Cilibrizzi and Somdet Srichairatanakool

Int. J. Mol. Sci. 2025, 26(23), 11281; https://doi.org/10.3390/ijms262311281 - 21 Nov 2025

Viewed by 659

Abstract

Iron (Fe) chelators are used to treat iron-overloaded disorders, metal detoxification, radionuclides, and molecular imaging; however, they can cause side effects. In this study, we identified and characterized Coprogen B (CPGB), a hexadentate trihydroxamate siderophore secreted by the opportunistic dimorphic fungus Talaromyces marneffei and compared its properties with deferoxamine (DFO). Siderophore production was enriched from a ΔsreA strain and purified via Amberlite XAD2 and Sephadex LH20 chromatography, followed by reverse-phase HPLC. Active fractions were confirmed by Ultraviolet–Visible (UV–Vis) spectral fingerprints (≈230 nm) for hydroxamate, with a band at 430–450 nm upon Fe(III) complexation, as well as by chrome azurol A assay, Nuclear Magnetic Resonane (NMR) spectroscopy, High-Performance Liquid Chromatography–Mass Spectrometry (HPLC-MS), and Matrix-Assisted Laser Desorption/Ionization–Time-of-Flight Mass Spectrometry (MALDI-TOF-MS). CPGB exhibited strong molar absorptivity and rapid, concentration-dependent chelation of Fe(III), yielding a sustained binding profile that matched or exceeded that of DFO over time. In determining n-octanol/water partitioning for CPGB and DFO (230 nm) and their Fe(III) complexes, the partitioning (P) assay revealed that CPGB was moderately hydrophilic (P = 0.505 ± 0.063; cLogP = −0.299 ± 0.053), while DFO was strongly hydrophilic (P = 0.098 ± 0.005; cLogP = −1.010 ± 0.022). Fe(III) complexation reduced lipophilicity: CPGB–Fe partitioned ~30–35% into octanol, while DFO–Fe complex partitioned ~7–8%, remaining largely aqueous. Overall, this outcome potentially suggested improved clearance in vivo. These data nominate CPGB as a promising alternative to existing iron chelators. The siderophore exhibited greater lipophilicity, emphasizing better passive membrane permeability than DFO, while siderophore–Fe(III) binding indicated increased biases toward the aqueous phase. Future in vivo studies are warranted to confirm its pharmacokinetics, safety, and therapeutic efficacy. 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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22 pages, 7189 KB

Open AccessArticle

Dual-Site Acetylcholinesterase Inhibition and Multiscale Stability of Fused Quinoline Sulfonamides: A Chemoinformatic GA-MLR and Molecular Dynamics Study

by Shrikant S. Nilewar, Apurva D. Chavan, Ankita R. Pradhan, Anshuman A. Tripathy, Nagaraju Bandaru, Prashik B. Dudhe, Perli Kranti Kumar, Sandesh Lodha, Ghazala Muteeb, Ivan Peredo-Valderrama, Antonio Jose Naranjo-Redondo and Tushar Janardan Pawar

Int. J. Mol. Sci. 2026, 27(7), 3286; https://doi.org/10.3390/ijms27073286 - 4 Apr 2026

Viewed by 518

Abstract

Alzheimer’s disease (AD) represents an escalating global neuropharmacological crisis, with prevalence in high-growth demographic regions such as India projected to exceed 14 million by 2040. This study addresses the urgent need for high-potency, dual-site acetylcholinesterase (AChE) inhibitors through an integrated computational pipeline. We address the failure of mono-target paradigms by designing scaffolds capable of simultaneously anchoring the Catalytic Active Site (CAS) and the Peripheral Anionic Site (PAS). A robust GA-MLR QSAR model was developed from 115 quinoline analogs using 11,135 descriptors. Lead candidates were prioritized via cavity directed molecular docking (7XN1) and 100 ns molecular dynamics (MD) simulations. The five-descriptor model (R² = 0.7569,

Q_{L O O}^{2}

= 0.7244) was validated by an external set of 8 experimental compounds (

R_{e x t}^{2}

= 0.8620). Lead Compound 19 emerged as a superior candidate (ΔG = −11.1 kcal/mol), exhibiting a stable MD trajectory (PL-RMSD ≈ 2.4 Å) and preserving essential Gly121-His447 catalytic anti-correlations. This study provides a statistically validated scaffold and computational mechanistic foundation for future in vitro experimental validation, advancing the high throughput screening of neuroprotective agents on a global scale. 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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