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Computer-Aided Drug Discovery: Computational Chemistry and Cheminformatics

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A Special Issue of International Journal of Molecular Sciences (ISSN 1422-0067) belonging to the section "Molecular Informatics".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 2526

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Dr. Daniel Glossman-Mitnik

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Laboratorio Virtual NANOCOSMOS, Departamento de Medio Ambiente y Energía, Centro de Investigación en Materiales Avanzados, Chihuahua, Chih 31136, Mexico
Interests: computational chemistry; molecular modeling; computer-aided drug discovery; conceptual DFT; computational peptidology; AI and machine learning in drug discovery
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Special Issue Information

Dear Colleagues,

Computer-aided drug discovery has become a cornerstone of modern molecular medicine, enabling the rational design, optimization, and repurposing of therapeutic agents with unprecedented efficiency. Advances in computational chemistry, molecular modeling, and cheminformatics now allow researchers to explore chemical space, predict physicochemical and biological properties, and elucidate structure–activity relationships prior to experimental validation. These approaches significantly reduce development time and cost while improving success rates in lead identification and optimization.

This Special Issue aims to highlight recent theoretical and computational developments that contribute to drug discovery and molecular design, with particular emphasis on quantum-chemical methods, conceptual density functional theory (CDFT), molecular docking, molecular dynamics simulations, QSAR/QSPR modeling, virtual screening, and data-driven cheminformatics strategies. Contributions addressing ADMET prediction, reactivity descriptors, pharmacophore modeling, and machine learning-assisted workflows are especially welcome. Both methodological advances and applied studies on biologically relevant systems such as small molecules, peptides, natural products, and bioinspired compounds are within the scope of this Issue.

By bringing together experts from diverse computational disciplines, this Special Issue seeks to provide a comprehensive overview of current trends and emerging methodologies in computer-aided drug discovery, fostering interdisciplinary dialog and offering insights that support experimental drug development and translational research.

Dr. Daniel Glossman-Mitnik
Guest Editor

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Keywords

computer-aided drug discovery
computational chemistry
cheminformatics
conceptual density functional theory (CDFT)
molecular docking
molecular dynamics simulations
QSAR/QSPR modeling
ADMET prediction
machine learning in drug design

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Published Papers (5 papers)

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Research

28 pages, 8459 KB

Open AccessArticle

Identification of Potential SARS-CoV-2 Main Protease (MPro) Inhibitors Through Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Simulation Approaches

by Mohd Yasir Khan, Farah Maarfi, Abid Ullah Shah, Nithyadevi Duraisamy, Mohammed Cherkaoui and Maged Gomaa Hemida

Int. J. Mol. Sci. 2026, 27(17), 7684; https://doi.org/10.3390/ijms27177684 - 27 Aug 2026

Abstract

The main protease (MPro) of coronaviruses (CoVs) is an essential enzyme involved in viral replication and represents an attractive target for antiviral drug discovery. Based on the similar binding pocket residues within the MPro of different CoVs, this study aimed to identify potential inhibitors of SARS-CoV-2 MPro from PDB ID 6M2N using integrated computational approaches. Interaction-based pharmacophore modeling, virtual screening, molecular docking, MM-GBSA binding energy calculation, and molecular dynamics simulation (MDS) were performed using BIOVIA Discovery Studio. The validated pharmacophore model was utilized to screen the ZINC database, followed by docking and 100 ns MDS analyses of the top-ranked compounds. The pharmacophore model 01 demonstrated favorable predictive performance (AUC = 0.781). Virtual screening identified 483 compounds, from which 15 compounds were selected for docking studies. Among them, ZINC95473654 (Lig-1), ZINC95473725 (Lig-2), and ZINC08792368 (Lig-3) exhibited strong binding affinity toward MPro. Lig-1 demonstrated the best docking score and binding free energy, along with stable interactions with key catalytic residues HIS41, CYS145, and GLU166. MDS analyses further confirmed that Lig-1, Lig-2 and Lig-3 maintained stable conformations. The hydrogen bond distance monitoring and post MDS-MM-GBSA results suggest Lig-1 followed by Lig-3 as an inhibitor for MPro and persistent intermolecular interactions throughout the 100 ns simulation period. The findings suggest that Lig-1, followed by Lig-3, may serve as promising computational lead compounds targeting SARS-CoV-2 MPro, representing promising candidates for further experimental validation. Full article

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

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25 pages, 3310 KB

Open AccessArticle

SurroDock: A Deep Learning Surrogate for Accelerated Pre-Docking Ligand Prioritization in Structure-Based Virtual Screening

by Jongkeun Choi

Int. J. Mol. Sci. 2026, 27(15), 6663; https://doi.org/10.3390/ijms27156663 - 26 Jul 2026

Viewed by 335

Abstract

The rapid expansion of make-on-demand and public chemical libraries has made exhaustive docking-based structure-based virtual screening increasingly difficult. This study introduces SurroDock, a lightweight deep-learning surrogate designed to approximate AutoDock Vina docking scores from low-cost two-dimensional molecular features, serving as a practical pre-filter for docking. SurroDock was evaluated for estrogen receptor alpha using two distinct conformations: an agonist-bound (PDB ID: 1GWR) and an antagonist/SERM-bound (PDB ID: 3ERT). The dataset comprised approximately 334,000 unique compounds curated from the NCI Open Database, PubChem, and BindingDB, all docked using a standardized AutoDock Vina workflow. The model was trained on concatenated 2D molecular representations comprising Morgan fingerprints, MACCS keys, RDKit physicochemical descriptors, Vina-inspired ligand descriptors, atom-pair fingerprints, and 2D pharmacophore fingerprints. The docking-score distributions differed substantially between receptor states, with 3ERT exhibiting more favorable scores than 1GWR and weak inter-state score correlation supporting state-specific modeling. Using the integrated Unified-200k training set (200,000 compounds randomly sampled per receptor from the three docked sources), SurroDock achieved strong held-out validation performance, with R² values of approximately 0.88 for 1GWR and 0.93 for 3ERT. In retrospective screening-style evaluation, SurroDock recovered substantial fractions of Vina’s top-ranked compounds at the top-1% recall (Recall@1%) of approximately 0.57 and 0.61 for 1GWR and 3ERT, respectively, yielding corresponding enrichment factors (EF@1%) of approximately 57-fold and 61-fold relative to random selection. Overall, the results indicate that 2D-based docking-score surrogate modeling can provide a reproducible and retrainable strategy for large-scale structure-based virtual screening by concentrating docking resources on a smaller, enriched subset of compounds. Because SurroDock emulates a docking scoring function rather than experimental binding affinity, its predictions should be used as prioritization aids and complemented by confirmatory docking, pose inspection, and experimental validation. Full article

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

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23 pages, 5457 KB

Open AccessArticle

In Silico Design of Pyrimidine Derivatives as Potential α-Glucosidase Inhibitors: QSAR, Molecular Docking, ADMET, and Molecular Dynamics Studies

by Oussama Abchir, Bouchra Rossafi, Amal Bouribab, Bouchra Es-Sounni, Rodouan Touti, Imane Yamari, Abdelouahid Samadi and Samir Chtita

Int. J. Mol. Sci. 2026, 27(13), 5696; https://doi.org/10.3390/ijms27135696 - 24 Jun 2026

Cited by 1 | Viewed by 518

Abstract

Diabetes mellitus remains a major metabolic disorder requiring the development of new and effective α-glucosidase inhibitors. The present study aimed to identify, design, and optimize novel 3-amino-2,4-diarylbenzo[4,5]imidazo[1,2-α]pyrimidine derivatives with promising inhibitory activity against the α-glucosidase enzyme using a comprehensive in silico strategy. Approximately 300 molecular descriptors were calculated to characterize a dataset of 32 compounds (Peytam et al.) and to investigate the structural factors governing their biological activity. Based on these descriptors, a multiple linear regression model was developed to predict the inhibitory activities of the compounds against alpha-glucosidase. The developed model demonstrated satisfactory predictive performance and was internally and externally validated to ensure its accuracy, robustness, and reproducibility. In addition, the applicability domain analysis confirmed the reliability of the predictions. Using the validated QSAR model, seven new derivatives were designed with predicted pIC₅₀ values exceeding the maximum activity of the parent compounds. The leverage analysis demonstrated that all newly designed compounds were located within the applicability domain of the model, supporting the reliability of the predictions. To further evaluate their inhibitory potential, molecular docking studies were performed to investigate the interactions between the designed compounds and the α-glucosidase active site. The docking results revealed favorable binding interactions comparable to those reported for known α-glucosidase inhibitors. Furthermore, ADMET analysis indicated generally favorable pharmacokinetic properties, although potential CYP3A4 inhibition-related pharmacokinetic risks were identified and discussed. Molecular dynamics simulations, including replicated runs and MM/GBSA binding free energy calculations, confirmed the stability of the most promising protein–ligand complexes throughout the simulation period. In conclusion, this study proposes a robust and integrated computational workflow combining descriptor generation, QSAR modeling, applicability domain analysis, molecular docking, ADMET prediction, and molecular dynamics simulations for the rational design of potential α-glucosidase inhibitors. The findings highlight the therapeutic potential of the designed derivatives and provide a valuable in silico framework for the future development of antidiabetic agents. Full article

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

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21 pages, 4485 KB

Open AccessArticle

A Leakage-Aware Drug Discovery Workflow for PKM2 and MAPK1 Integrating Scaffold Validation, Molecular Docking and Structural Triage

by Ferhat Ucar and Nida Kati

Int. J. Mol. Sci. 2026, 27(11), 4751; https://doi.org/10.3390/ijms27114751 - 25 May 2026

Viewed by 668

Abstract

Computer-aided drug discovery increasingly depends on virtual-screening workflows that remain reliable under severe class imbalance, chemical redundancy and early-recognition constraints. In this study, we developed a leakage-aware prioritization workflow for two cancer-relevant targets, pyruvate kinase M2 (PKM2) and mitogen-activated protein kinase 1 (MAPK1/ERK2), using the LIT-PCBA benchmark. The workflow combines canonical-SMILES curation, duplicate and label-conflict auditing, scaffold-aware validation, a non-learning nearest-active Tanimoto baseline, imbalance-aware machine-learning models, repeated-seed robustness analysis, isotonic probability calibration, ensemble-disagreement estimation, absorption, distribution, metabolism, excretion and toxicity (ADMET)-aware triage, molecular docking, and residue-level contact analysis. Benchmark enrichment is interpreted alongside calibration, ADMET filtering, docking and residue-contact evidence, rather than as a standalone discovery claim. PKM2 emerged as the clearer machine-learning case, with scaffold-aware tree models improving early recognition beyond the nearest-active similarity baseline and yielding top-ranked candidates supported by calibrated activity scores, ADMET profiles, docking scores, and residue-contact fingerprints. MAPK1 provided a biologically relevant contrast target, where ligand-neighborhood similarity remained competitive and downstream structural triage became more decisive than ligand-based ranking alone. These results support a conservative drug-discovery workflow in which leakage-aware benchmarking, calibration, uncertainty, and molecular-level triage remain visible throughout candidate prioritization. Full article

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

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20 pages, 1608 KB

Open AccessArticle

Motif-Level Graph Learning Enables Interpretable Prediction of Drug-Induced QT Prolongation via Cooperative Substructural Determinants

by Wulin Long, Shengqiu Zhai, Yuheng Liu, Menglong Li and Zhining Wen

Int. J. Mol. Sci. 2026, 27(11), 4706; https://doi.org/10.3390/ijms27114706 - 23 May 2026

Cited by 1 | Viewed by 448

Abstract

Drug-induced QT interval prolongation is a critical safety concern in drug development, yet accurate and mechanistically interpretable prediction from chemical structure remains challenging due to the limited substructural resolution of existing approaches. Here, we present a motif-level graph learning framework for interpretable QT risk prediction. In this framework, molecules are decomposed into chemically meaningful motifs, enabling representation at an intermediate structural scale between atoms and predefined structural alerts. Motif features are encoded using a pre-trained chemical language model, and inter-motif relationships are modeled via attention-based graph learning with cross-scale integration. The model is trained and evaluated on two clinically grounded datasets derived from regulatory drug labeling (DIQTA) and real-world pharmacovigilance data (FAERS), achieving strong and consistent predictive performance with robust generalization across data sources. Importantly, motif-level attention reveals that QT liability is associated with the cooperative organization of compact cationic centers and heteroatom-rich, conformationally adaptable scaffolds, rather than isolated functional groups. These patterns are consistent with known determinants of human ether-à-go-go-related (hERG) channel blockade while providing a more structured and chemically specific interpretation beyond conventional structural alerts. Overall, this work establishes a generalizable and interpretable framework for QT risk prediction and highlights motif-level graph learning as an effective strategy for structure-based modeling of adverse drug reactions. Full article

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

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