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Pharmaceuticals
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Integrating Machine Learning (ML) into Medicinal Chemistry and Cheminformatics, 2nd...
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Integrating Machine Learning (ML) into Medicinal Chemistry and Cheminformatics, 2nd Edition

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

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 1605

Special Issue Editor

Dr. Vartika Tomar

E-Mail Website
Guest Editor
School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
Interests: chemotherapy; tumor; nanomedicine; drug development and delivery; cystic fibrosis; gene therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning (ML) has rapidly become a critical tool in computer-aided drug discovery, offering a powerful alternative to traditional physical models such as quantum chemistry and molecular dynamics simulations. Unlike these explicit models, ML techniques rely on pattern recognition algorithms to uncover mathematical relationships between empirical data and predict the chemical, biological, and physical properties of novel compounds. ML’s efficiency and scalability make it particularly well-suited for handling large datasets, which provides a significant advantage over computationally intensive physical models. In drug discovery, ML enhances our understanding of the relationships between chemical structures and their biological activities, integrating seamlessly with chemoinformatics and quantitative structure–activity relationship (QSAR) modeling to drive predictive molecular design and analysis. Recent advances in computational power and deep learning algorithms have further propelled ML, addressing previously unmet challenges in pharmaceutical research. The surge in chemical “big data” from high-throughput screening (HTS) and combinatorial synthesis underscores ML’s role in mining large compound databases and designing drugs with critical biological properties.

This Special Issue on “Integrating Machine Learning (ML) into Medicinal Chemistry and Cheminformatics, 2nd Edition” invites original research and review articles that explore the latest ML and deep learning applications in computational drug design. Topics of interest include cheminformatics, QSAR, novel ML applications in drug development, molecular descriptors, molecular similarity, structure-based and ligand-based screening, homology modeling, molecular docking, and the stability of drug–receptor interactions. This Special Issue aims to highlight the transformative impact of ML in advancing drug discovery and design.

Dr. Vartika Tomar
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. 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

  • small molecules
  • molecular descriptors
  • molecular similarity
  • structure-based and ligand-based screening
  • homology modeling
  • molecular docking
  • drug–receptor docking
  • biological activity

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Published Papers (1 paper)

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Research

14 pages, 2486 KB  
Article
Machine Learning-Integrated Explainable Artificial Intelligence Approach for Predicting Steroid Resistance in Pediatric Nephrotic Syndrome: A Metabolomic Biomarker Discovery Study
by Fatma Hilal Yagin, Feyza Inceoglu, Cemil Colak, Amal K. Alkhalifa, Sarah A. Alzakari and Mohammadreza Aghaei
Pharmaceuticals 2025, 18(11), 1659; https://doi.org/10.3390/ph18111659 - 1 Nov 2025
Cited by 3 | Viewed by 1312
Abstract
Aim: Nephrotic syndrome (NS) represents a complex glomerular disorder with significant clinical heterogeneity across pediatric and adult populations. Although glucocorticosteroids have constituted the mainstay of therapeutic intervention for more than six decades, primary treatment resistance manifests in approximately 20% of pediatric patients and [...] Read more.
Aim: Nephrotic syndrome (NS) represents a complex glomerular disorder with significant clinical heterogeneity across pediatric and adult populations. Although glucocorticosteroids have constituted the mainstay of therapeutic intervention for more than six decades, primary treatment resistance manifests in approximately 20% of pediatric patients and 50% of adult cohorts. Steroid-resistant nephrotic syndrome (SRNS) is associated with substantially greater morbidity compared to steroid-sensitive nephrotic syndrome (SSNS), characterized by both iatrogenic glucocorticoid toxicity and progressive nephron loss with attendant decline in renal function. Based on this, the current study aims to develop a robust machine learning (ML) model integrated with explainable artificial intelligence (XAI) to distinguish SRNS and identify important biomarker candidate metabolites. Methods: In the study, biomarker candidate compounds obtained from proton nuclear magnetic resonance (1 H NMR) metabolomics analyses on plasma samples taken from 41 patients with NS (27 SSNS and 14 SRNS) were used. We developed ML models to predict steroid resistance in pediatric NS using metabolomic data. After preprocessing with MICE-LightGBM imputation for missing values (<30%) and standardization, the dataset was randomly split into training (80%) and testing (20%) sets, repeated 100 times for robust evaluation. Four supervised algorithms (XGBoost, LightGBM, AdaBoost, and Random Forest) were trained and evaluated using AUC, sensitivity, specificity, F1-score, accuracy, and Brier score. XAI methods including SHAP (for global feature importance and model interpretability) and LIME (for individual patient-level explanations) were applied to identify key metabolomic biomarkers and ensure clinical transparency of predictions. Results: Among four ML algorithms evaluated, Random Forest demonstrated superior performance with the highest accuracy (0.87 ± 0.12), sensitivity (0.90 ± 0.18), AUC (0.92 ± 0.09), and lowest Brier score (0.20 ± 0.03), followed by LightGBM, AdaBoost, and XGBoost. The superiority of the Random Forest model was confirmed by paired t-tests, which revealed significantly higher AUC and lower Brier scores compared to all other algorithms (p < 0.05). SHAP analysis identified key metabolomic biomarkers consistently across all models, including glucose, creatine, 1-methylhistidine, homocysteine, and acetone. Low glucose and creatine levels were positively associated with steroid resistance risk, while higher propylene glycol and carnitine concentrations increased SRNS probability. LIME analysis provided patient-specific interpretability, confirming these metabolomic patterns at individual level. The XAI approach successfully identified clinically relevant metabolomic signatures for predicting steroid resistance with high accuracy and interpretability. Conclusions: The present study successfully identified candidate metabolomic biomarkers capable of predicting SRNS prior to treatment initiation and elucidating critical molecular mechanisms underlying steroid resistance regulation. Full article
(This article belongs to the Special Issue Integrating Machine Learning (ML) into Medicinal Chemistry and Cheminformatics, 2nd Edition)
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