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Computational Approaches in Drug Discovery and Design: From Molecular Modeling to Translational Applications

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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: 20 July 2026 | Viewed by 6356

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Dr. Przemysław Czeleń

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Guest Editor

Department of Physical Chemistry, Faculty of Pharmacy, Collegium Medicum, Nicolaus Copernicus University, Kurpinskiego 5, 85-096 Bydgoszcz, Poland
Interests: molecular dynamics; competitive inhibition; drug nanocarriers; ADMET; anticancer drugs; personalised medicine; molecular docking
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Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight recent advances in computational methods that are transforming modern drug discovery and development. Emphasis will be placed on the integration of molecular modeling, AI-driven drug design, molecular dynamics simulations, quantum chemical approaches, structure-based and ligand-based drug design, as well as multi-scale modeling in predicting pharmacokinetics, toxicity, and efficacy.

Topics of interest include but are not limited to the following: in silico screening and optimisation of drug candidates; molecular docking and molecular dynamics simulations in target interaction studies; AI and machine learning in drug repurposing and de novo drug design; computational toxicology and safety prediction; pharmacophore modeling and virtual screening workflows; integration of omics data with modeling tools for personalised therapeutics; modeling of nanodrug behaviour and interactions at the cellular level; predictive models for ADME/T properties; and drug–drug interactions.

This Issue will serve as a platform for interdisciplinary research, bridging computational science, chemistry, pharmacology, and systems biology, with the goal of accelerating and improving the drug development pipeline.

Dr. Przemysław Czeleń
Guest Editor

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Keywords

computational drug discovery
molecular modeling
AI-driven drug design
molecular dynamics simulations
quantum chemistry
structure-based drug design
machine learning in pharmacology
virtual screening and docking
ADMET prediction
personalized medicine modeling

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Published Papers (5 papers)

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Research

23 pages, 3096 KB

Open AccessArticle

Network Pharmacology and Molecular Docking-Based Approach Revealing the Potential Anticancer Compounds and Molecular Mechanisms of Paris polyphylla Against Colorectal Cancer

by Chakkrit Khanaree, Ratchanon Inpan, Weerakit Taychaworaditsakul and Nahathai Dukeaw

Int. J. Mol. Sci. 2026, 27(9), 3874; https://doi.org/10.3390/ijms27093874 - 27 Apr 2026

Viewed by 265

Abstract

Colorectal cancer (CRC) remains a major cause of cancer-related morbidity and mortality worldwide, highlighting the need for safer and more effective therapeutic agents. This study investigated the potential anticancer compounds and mechanisms of Paris polyphylla against CRC using an integrated approach combining network pharmacology, molecular docking, and in vitro validation. Bioactive compounds were screened from multiple databases, and their putative targets were intersected with CRC-related genes. Protein–protein interaction and enrichment analyses were performed to identify key targets and pathways, followed by the docking of selected compounds with major hub proteins. The cytotoxic and molecular effects of P. polyphylla rhizome extract (PPRE) were then evaluated in SW480 and HCT116 cells. A total of 74 compounds were identified, of which 12 were retained for target prediction, yielding 180 overlapping genes between P. polyphylla targets and CRC-associated genes. Network analysis highlighted STAT3, EGFR, SRC, IL-6, and AKT1 as key hub targets, with enrichment in cancer-related, EGFR resistance, and PI3K–Akt pathways. Docking showed favorable binding affinities, particularly between prosapogenin A and AKT1. Experimentally, PPRE reduced CRC cell viability and downregulated STAT3, EGFR, SRC, IL-6, and AKT1 expression. These findings suggest that P. polyphylla exerts anticancer effects through the coordinated modulation of multiple oncogenic pathways in CRC. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design: From Molecular Modeling to Translational Applications)

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24 pages, 2481 KB

Open AccessArticle

Design and Evaluation of New 6-Trifluoromethoxy-Isatin Derivatives as Potential CDK2 Inhibitors

by Przemysław Czeleń and Beata Szefler

Int. J. Mol. Sci. 2026, 27(4), 1802; https://doi.org/10.3390/ijms27041802 - 13 Feb 2026

Viewed by 410

Abstract

Cyclin-dependent kinase 2 (CDK2) plays a central role in cell cycle regulation and represents an important molecular target in anticancer drug development. In this study, a series of novel isatin derivatives substituted with a trifluoromethoxy group at the C6 position were designed and evaluated as potential CDK2 inhibitors using a comprehensive in silico approach. Density functional theory calculations were applied to analyze the electronic properties of the proposed compounds. Molecular docking and molecular dynamics simulations were used to investigate binding modes, conformational stability, and key interactions within the CDK2 active site. Binding free energies were estimated using the Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) method, while QSAR-based (Quantitative Structure–Activity Relationship) ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analyses were performed to assess drug-likeness and pharmacokinetic profiles. The results indicate that the investigated derivatives form stable complexes with CDK2, supported by persistent hydrogen bonds in the hinge region and favorable hydrophobic interactions. The trifluoromethoxy substituent significantly affects ligand orientation and promotes deeper insertion into the hydrophobic pocket compared with previously studied isatin analogues. ADMET predictions suggest generally favorable absorption and toxicity profiles, with moderate solubility limitations. Overall, these findings support the potential of 6-trifluoromethoxy-isatin derivatives as promising CDK2 inhibitors and provide a basis for further experimental studies. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design: From Molecular Modeling to Translational Applications)

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21 pages, 3313 KB

Open AccessArticle

MGF-DTA: A Multi-Granularity Fusion Model for Drug–Target Binding Affinity Prediction

by Zheng Ni, Bo Wei and Yuni Zeng

Int. J. Mol. Sci. 2026, 27(2), 947; https://doi.org/10.3390/ijms27020947 - 18 Jan 2026

Viewed by 581

Abstract

Drug–target affinity (DTA) prediction is one of the core components of drug discovery. Despite considerable advances in previous research, DTA tasks still face several limitations with insufficient multi-modal information of drugs, the inherent sequence length limitation of protein language models, and single attention mechanisms that fail to capture critical multi-scale features. To alleviate the above limitations, we developed a multi-granularity fusion model for drug–target binding affinity prediction, termed MGF-DTA. This model is composed of three fusion modules, specifically as follows. First, the model extracts deep semantic features of SMILES strings through ChemBERTa-2 and integrates them with molecular fingerprints by using gated fusion to enhance the multi-modal information of drugs. In addition, it employs a residual fusion mechanism to integrate the global embeddings from ESM-2 with the local features obtained by the k-mer and principal component analysis (PCA) method. Finally, a hierarchical attention mechanism is employed to extract multi-granularity features from both drug SMILES strings and protein sequences. Comparative analysis with other mainstream methods on the Davis, KIBA, and BindingDB datasets reveals that the MGF-DTA model exhibits outstanding performance advantages. Further, ablation studies confirm the effectiveness of the model components and case study illustrates its robust generalization capability. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design: From Molecular Modeling to Translational Applications)

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34 pages, 15926 KB

Open AccessArticle

Rescuing Verubecestat: An Integrative Molecular Modeling and Simulation Approach for Designing Next-Generation BACE1 Inhibitors

by Doni Dermawan and Nasser Alotaiq

Int. J. Mol. Sci. 2025, 26(24), 12143; https://doi.org/10.3390/ijms262412143 - 17 Dec 2025

Viewed by 851

Abstract

β-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a central therapeutic target in Alzheimer’s disease, as it catalyzes the rate-limiting step in amyloid-β production. Verubecestat (VER), a clinical BACE1 inhibitor, failed in late-stage trials due to limited efficacy and safety concerns. This study employed an integrative computational approach to design VER derivatives with improved binding affinity, stability, and pharmacokinetic profiles. Structural similarity analysis, Molecular docking, frontier molecular orbital (FMO) analysis, pharmacophore modeling, 200 ns molecular dynamics (MD) simulations, MM/PBSA free energy calculations, and per-residue decomposition were performed. In silico ADMET profiling assessed drug-likeness, absorption, and safety parameters. Docking and pharmacophore analyses identified derivatives with stronger complementarity in the BACE1 catalytic pocket. MD simulations revealed that VERMOD-33 and VERMOD-57 maintained low root mean square deviations (RMSDs) and stable binding orientations and induced characteristic flexibility in the flap and catalytic loops surrounding the catalytic dyad (Asp93 and Asp289), consistent with inhibitory activity. MM/PBSA confirmed the superior binding free energies of VERMOD-33 (−51.12 kcal/mol) and VERMOD-57 (−43.85 kcal/mol), both outperforming native VER (−35.33 kcal/mol). Per-residue decomposition highlighted Asp93, Asp289, and adjacent flap residues as major energetic contributors. ADMET predictions indicated improved oral absorption, BBB penetration, and no mutagenicity or toxicity alerts. Rationally designed VER derivatives, particularly VERMOD-33 and VERMOD-57, displayed enhanced binding energetics, stable inhibitory dynamics, and favorable pharmacokinetic properties compared with native VER. These findings provide a computational framework for rescuing VER and support further synthesis and experimental validation of next-generation BACE1 inhibitors for Alzheimer’s disease. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design: From Molecular Modeling to Translational Applications)

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16 pages, 4966 KB

Open AccessArticle

Salvia miltiorrhiza for Viral Myocarditis: Multi-Computational Pharmacological Exploration and Meta-Analytic Efficacy Validation

by Xingxin Cao, Mingxue Li, Xueqian Xie, Zhun Feng, Weihua Jin, Yanyan Li, Fengmei Yang, Suqin Duan and Zhanlong He

Int. J. Mol. Sci. 2025, 26(23), 11753; https://doi.org/10.3390/ijms262311753 - 4 Dec 2025

Viewed by 1572

Abstract

Viral myocarditis (VMC) is the predominant type of myocarditis and currently lacks specific therapies. Salvia miltiorrhiza (Danshen) injection has demonstrated beneficial effects as a supplementary VMC treatment, yet its pharmacological mechanisms are ambiguous, and its efficacy lacks robust evidence. This study aims to preliminarily address these issues through computational approaches and meta-analysis. Using network pharmacology, we identified 257 therapeutic targets, 106 hub genes, and 4 key S. miltiorrhiza ingredients implicated in VMC treatment. Integrating transcriptome data with LASSO and SVM machine learning algorithm yielded six core therapeutic targets from the hub genes—TNF, JUN, PECAM1, KDR, TIMP1, and EPAS1—which are primarily associated with anti-inflammatory activity, vascular remodeling, and fibrosis suppression. GO analysis identified the “inflammatory response” as the most prominent biological process. Concurrently, the PI3K-Akt, TNF, and HIF-1 signaling pathways—each closely associated with inflammation—appeared among the top 20 KEGG pathways. Overall, these results indicate that suppressing excessive inflammation is likely the primary pharmacological mechanism. In molecular docking, four key ingredients—dan-shexinkum D, danshenol A, cryptotanshinone, and methylrosmarinate—exhibited strong binding to the core therapeutic targets, with dan-shexinkum D showing the lowest total binding energy and stable binding confirmed by molecular dynamics simulations. The meta-analysis indicates that S. miltiorrhiza injection improves clinical outcomes and significantly reduces TNF-α, hs-CRP, CK-MB, cTnT, and H-FABP levels. This study used multiple computational approaches to explore the pharmacological mechanisms and identify key active components of S. miltiorrhiza in treating VMC, thereby establishing an evidence-based foundation and providing preliminary groundwork for subsequent clinical application and translational research. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design: From Molecular Modeling to Translational Applications)

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