Cancer Therapeutics: Drug Repurposing and Computational Strategies

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "AI in Drug Development".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 761

Special Issue Editor


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Guest Editor
Pharmaceutical Biochemistry, College of Pharmacy, Dongguk University, Goyang 10326, Republic of Korea
Interests: cancer molecular biology; computer-aided drug design (CADD); drug repurposing; molecular dynamics simulation; undruggable targets (KRAS, p53, MYC); virtual screening; structure-based drug design; targeted therapy

Special Issue Information

Dear Colleagues,

The discovery of effective cancer treatments is increasingly focused on challenging targets previously deemed "undruggable," such as transcription factors, protein-protein interactions, and intrinsically disordered proteins. Drug repurposing, combined with advanced computational strategies, offers a powerful avenue to tackle these difficult targets by uncovering novel binding sites and cryptic pockets.

This Special Issue, "Cancer Therapeutics: Drug Repurposing and Computational Strategies", aims to highlight the synergy between in silico predictions and experimental validation. We specifically encourage contributions that demonstrate the ability of computational tools including molecular dynamics (MD) simulations, deep learning, and virtual screening to identify potential compounds against known undruggable targets (e.g., KRAS, p53, MYC). We welcome original research and reviews that leverage these modern drug design approaches to repurpose existing drugs or discover novel scaffolds for refractory cancer targets, bridging the gap between computational prediction and clinical potential.

Dr. Minh Tuan Nguyen
Guest Editor

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Keywords

  • cancer therapeutics
  • drug repurposing
  • computer-aided drug design (CADD)
  • molecular dynamics simulation
  • virtual screening
  • targeted therapy
  • structure-based drug design
  • molecular docking
  • pharmacophore modeling

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Published Papers (1 paper)

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Research

29 pages, 7368 KB  
Article
An Integrative Computational Pipeline for CK2 Inhibitor Discovery in Triple-Negative Breast Cancer Using Virtual Screening, Molecular Dynamics, Machine Learning, and Density Functional Theory
by Abbas Khan, Fahad M. Alshabrmi, Anwar Mohammad, Mohanad Shkoor, Raed M. Al-Zoubi, Long Chiau Ming and Abdelali Agouni
Pharmaceuticals 2026, 19(5), 694; https://doi.org/10.3390/ph19050694 - 28 Apr 2026
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Abstract
Background: Triple-negative breast cancer (TNBC) remains among the most aggressive and therapeutically unresponsive subtypes due to the absence of ER, PR, and HER2 targets. Casein Kinase II (CK2), a pleiotropic serine/threonine kinase overexpressed in TNBC, represents a compelling target for rational drug design. [...] Read more.
Background: Triple-negative breast cancer (TNBC) remains among the most aggressive and therapeutically unresponsive subtypes due to the absence of ER, PR, and HER2 targets. Casein Kinase II (CK2), a pleiotropic serine/threonine kinase overexpressed in TNBC, represents a compelling target for rational drug design. Methods: Here, we present an AI-integrated benchmarking framework combining virtual drug discovery, molecular dynamics simulations, machine learning-driven QSAR modeling, and quantum-mechanical electronic structure analysis to identify potent CK2 inhibitors from natural product chemical space. Results: A validated XP docking protocol (ROC–AUC = 0.748) screened ~480,000 compounds, yielding seven hits, with superior binding to the reference inhibitor CX-4945. Among these, Anastatin B, 3,4,8,9,10-pentahydroxy-dibenzo-[b,d]pyran-6-one, Rhein, and aloe emodin acetate exhibited highly favorable docking scores (−11.6 to −13.1 kcal mol−1) and stable 200 ns binding dynamics, reflected by RMSD ≤ 2 Å and compact Rg trajectories. MM-PBSA/MM-GBSA analyses confirmed robust thermodynamic stability, while DFT-derived HOMO–LUMO gaps (3.8–4.3 eV) suggested optimal electronic reactivity for kinase inhibition. Machine learning QSAR models demonstrated strong predictive performance, with the best stacking models achieving test R2 ≈ 0.69 and consistent cross-validation performance (CV R2 ≈ 0.66–0.69), supporting reliable prediction of pIC50 values and prioritization of top-ranked scaffolds. Conclusions: Collectively, this integrative framework bridges AI-based learning and biophysical validation, establishing a reproducible paradigm for de novo CK2 inhibitor discovery in TNBC. Full article
(This article belongs to the Special Issue Cancer Therapeutics: Drug Repurposing and Computational Strategies)
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