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Latest Advances in Computational Drug Discovery

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pharmacology".

Deadline for manuscript submissions: closed (30 August 2025) | Viewed by 846

Special Issue Editor


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Guest Editor
Department of Organic Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovičova 6, 842 15 Bratislava, Slovakia
Interests: medicinal chemistry; organic chemistry; heterocyclic chemistry; drug discovery; molecular docking

Special Issue Information

Dear Colleagues,

Molecular docking is a computational method used to predict how a small molecule (ligand) interacts with a biological target (protein/enzyme). It plays a crucial role in drug discovery by identifying potential drug candidates and optimizing their binding properties. In silico ADME and toxicity (ADME-Tox) prediction helps pharmaceutical companies choose drug candidates wisely before starting expensive clinical trials. Finding molecular targets is essential for developing disease-specific therapies. Over the last two decades, molecular docking has significantly accelerated drug development, reducing costs and testing time in the pharmaceutical industry. It has been widely used to discover new drugs for cancer, infectious diseases, diabetes, Alzheimer’s disease, rare diseases, neurological disorders, anti-fungal treatments, antimicrobial treatments, and other areas.

In this Special Issue, we will highlight design, synthesis, and molecular docking, the computational study of, all types of heterocyclic compounds, natural products, bioactive compounds, nanomaterial, lipids, fats, carbohydrates, proteins metal complexes, methodological reviews, novel molecular docking approaches, and new performance benchmarks to guide future methodological development. Innovative applications of current docking methods, docking tools, QSAR modelling, molecular dynamics, DFT, AI-driven molecular docking, pharmacokinetic and pharmacodynamic models, etc., are also of interest.

Dr. Sunil Gaikwad
Guest Editor

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Keywords

  • design
  • synthesis
  • molecular docking
  • methodological reviews
  • novel docking approaches
  • ADME
  • toxicity
  • AI-driven molecular docking
  • molecular dynamics
  • bioisosteric replacement in drug design
  • rational drug design and synthesis
  • computational tools and algorithms
  • lead optimization and structure-based drug design
  • virtual screening of drug candidates
  • QSAR modelling
  • DFT

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Published Papers (1 paper)

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Research

23 pages, 1668 KB  
Article
Quercetin and Its Structural Analogs as NUDT5 Inhibitors: A Preliminary In Silico Study
by Emilia Gligorić, Milica Vidić, Branislava Teofilović and Nevena Grujić-Letić
Int. J. Mol. Sci. 2025, 26(18), 8843; https://doi.org/10.3390/ijms26188843 - 11 Sep 2025
Viewed by 319
Abstract
Nucleotide diphosphate hydrolase type 5 (NUDT5) plays a significant role in the estrogen-signaling pathway and is overexpressed in breast cancer. This study aimed to explore the anti-breast cancer potential of quercetin and its 52 structural analogs by targeting the NUDT5 enzyme using the [...] Read more.
Nucleotide diphosphate hydrolase type 5 (NUDT5) plays a significant role in the estrogen-signaling pathway and is overexpressed in breast cancer. This study aimed to explore the anti-breast cancer potential of quercetin and its 52 structural analogs by targeting the NUDT5 enzyme using the in silico molecular docking method. Moreover, Molecular Mechanics/General Born Surface Area (MM/GBSA) calculations were performed for compounds with superior binding affinity scores than quercetin. Their drug-likeness, according to Lipinski’s rule of five, water solubility, and Caco-2 permeability were predicted. In addition, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile was determined for the top-scoring compounds from the docking studies and MM/GBSA calculations, as well as for those that complied with the rules of Lipinski and exhibited high permeability. The obtained results showed that all the tested ligands interact with the active site of NUDT5. Their binding energies ranged from −11.24 to −7.36 kcal/mol. The MM/GBSA calculations further supported the binding affinity predictions. ADMET analysis enabled the selection of compounds with favorable pharmacokinetic profiles in comparison to quercetin. Quercetin analogs L1 and L28 were identified as promising anti-breast cancer drug candidates worthy of further experimental evaluation. Full article
(This article belongs to the Special Issue Latest Advances in Computational Drug Discovery)
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