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New Horizons in Structure and AI-Based Drug Design

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 September 2026 | Viewed by 2621

Special Issue Editor


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Guest Editor
Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
Interests: molecular modeling based drug discovery; artificial Intelligence (AI) based drug discovery; computer-aided drug design
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Special Issue Information

Dear Colleagues,

Traditional drug discovery faces significant challenges, including high costs, long development timelines, and low success rates in clinical translation. These limitations highlight the urgent need for innovative approaches to accelerate and improve the efficiency of drug development. The convergence of structural biology and artificial intelligence (AI) has emerged as a transformative force, enabling researchers to overcome these barriers through advanced computational methods and data-driven insights.

This Special Issue focuses on cutting-edge advances in structure- and AI-based drug design, emphasizing molecular-level applications that bridge computational predictions with experimental validation. We welcome studies that leverage AI/ML algorithms, molecular dynamics simulations, and free energy calculations to address key challenges in drug discovery, such as target identification, binding mechanism elucidation, and lead optimization. By integrating these technologies, researchers can achieve unprecedented precision in designing small-molecule therapeutics, optimizing protein-ligand interactions, and predicting drug-like properties. We encourage submissions that demonstrate tangible impact, whether through novel methodologies or validated case studies targeting specific protein classes (e.g., kinases and GPCRs) or therapeutic areas.

Dr. Huiyong Sun
Guest Editor

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • machine learning
  • MD simulation
  • free energy calculation
  • drug design

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Published Papers (2 papers)

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Research

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22 pages, 776 KB  
Article
Mapping of Phenotype Specific Host–Microbiome Protein–Protein Interaction Networks in Colorectal Cancer Using Deep Learning
by Despoina P. Kiouri, Georgios C. Batsis, Ippokratis Messaritakis, John Souglakos and Christos T. Chasapis
Int. J. Mol. Sci. 2026, 27(10), 4232; https://doi.org/10.3390/ijms27104232 - 9 May 2026
Viewed by 389
Abstract
Colorectal cancer (CRC) pathogenesis is driven by complex protein–protein interactions (PPIs) between the host and the gut microbiome, yet these molecular dialogs remain largely unmapped. This study utilizes a Deep Learning framework, enhanced by protein structure embeddings, to predict approximately 8.9 billion interspecies [...] Read more.
Colorectal cancer (CRC) pathogenesis is driven by complex protein–protein interactions (PPIs) between the host and the gut microbiome, yet these molecular dialogs remain largely unmapped. This study utilizes a Deep Learning framework, enhanced by protein structure embeddings, to predict approximately 8.9 billion interspecies PPIs from clinical metagenomic data. The model achieved high accuracy with an AUROC of 0.9960, identifying a high-confidence interactome representing roughly 16% of evaluated protein pairs. Phenotype-specific analysis revealed that while microbial hubs shift—transitioning from metabolic enzymes in healthy states to transport and regulatory proteins in CRC—the primary human targets remain remarkably consistent across both cohorts. These core human interactors are predominantly metalloproteins and regulators of ubiquitination, apoptosis, and zinc transport, suggesting these pathways are primary focal points for microbial manipulation regardless of disease state. Furthermore, co-occurring bacterial genera exhibit over 99% overlap in host target profiles, indicating significant functional redundancy in microbial engagement with the host. These findings suggest that CRC probably arises from network-level perturbations of stable host signaling hubs, offering a blueprint for identifying novel therapeutic targets and biomarkers. Full article
(This article belongs to the Special Issue New Horizons in Structure and AI-Based Drug Design)
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Review

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23 pages, 1788 KB  
Review
A Comparative Review of Artificial Intelligence Applications in Small Molecule Versus Peptide Drug Discovery
by Han Lin, Horst Vogel and Huawei Zhang
Int. J. Mol. Sci. 2026, 27(7), 3142; https://doi.org/10.3390/ijms27073142 - 30 Mar 2026
Viewed by 1857
Abstract
Traditional drug discovery processes are typically expensive, time-consuming, and have a very high failure rate. Artificial intelligence (AI) is currently reshaping this field in unprecedented ways, promising to significantly improve the efficiency and success rate of drug development. This article systematically compares and [...] Read more.
Traditional drug discovery processes are typically expensive, time-consuming, and have a very high failure rate. Artificial intelligence (AI) is currently reshaping this field in unprecedented ways, promising to significantly improve the efficiency and success rate of drug development. This article systematically compares and analyzes the application of AI for two major drug types: small molecule vs. peptide drugs. It explores their applications in several key stages of drug development, including virtual screening, lead compound optimization, de novo drug design, ADMET (absorption, distribution, metabolism, excretion, and toxicity) property prediction, and chemical synthesis planning. While both drug types benefit from AI-driven approaches, fundamental differences exist in molecular representation, data availability, key challenges, and model adaptability. For small molecule drugs, AI focuses on drug efficacy, synthetic feasibility, and accurate structure–activity relationship prediction. In contrast, for peptide drugs, AI faces more unique biological challenges, such as inherent flexibility, complex biological functions, stability, and immunogenicity. Finally, this article provides a forward-looking perspective on the future of AI-driven drug discovery, highlighting the immense potential of basic models, multimodal integrated systems, and autonomous discovery platforms, which will collectively drive the next wave of precision drug development. Full article
(This article belongs to the Special Issue New Horizons in Structure and AI-Based Drug Design)
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