PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 6, 2026BMC Pharmacology and Toxicology1 citationsOpen Access

Discovery of compound candidates for the treatment of allergic diseases: integration of DFT analyses, molecular docking, molecular dynamics simulations, and ADMET profiling

VUVelid UnsalEÖErkan ÖnerRYReşit Yıldız

Key Points

  • This research aims to find effective natural compounds against allergic diseases through computational analyses.
  • Applied density functional theory (DFT) for analyzing molecular structures and electronic properties
  • Conducted molecular docking on key proteins involved in allergic reactions
  • Performed molecular dynamics simulations to assess the stability of compound-protein interactions
  • Used principal component analysis (PCA) and free energy landscape (FEL) to analyze conformational changes
  • Beta carotene and vitamin D3 were identified as promising candidates due to their strong binding affinity and favorable properties.
  • Molecular dynamics simulations showed stable complexes between beta carotene and several target proteins, indicating effective interactions.
  • Physicochemical properties of some compounds were noted to require further development for optimal efficacy.

Abstract

In this study, a combination of computational analyses (drug similarity, physicochemical properties, pharmacokinetic properties, molecular docking, molecular dynamics simulation, principal component analysis (PCA), free energy landscape (FEL), density functional theory (DFT), and absorption, distribution, metabolism, and excretion (ADMET) analysis) was used to evaluate the efficacy of natural compounds (aloe emodin, fisetin, vitamin D3, β-carotene, berberine, and cinnamic acid) against allergic diseases and compare them with pharmaceuticals such as cetirizine and mizolastine. Elementary molecular structures and electronic properties were determined through DFT calculations accounting for reactivity by natural bond orbital (NBO) and nonlinear optical properties (NLO) analyses. Molecular docking studies were performed on critical proteins interleukin-10 (IL-10), filaggrin, thymic stromal lymphopoietin (TSLP), and high-affinity Fc receptor for immunoglobulin E (FcεRI) to elucidate the energetics of binding and to reveal interaction mechanisms facilitated by hydrophobic and hydrogen bonds. Highlighted in molecular docking, molecular dynamics (MD) simulation analysis of beta carotene and mizolastine was performed for IL-10, TSLP, FcεRI, and filaggrin, respectively. MD simulation revealed that the beta carotene-IL-10 complex exhibited higher structural stability. MD simulation showed that both filaggrin apoprotein and the mizolastine-filaggrin complex maintained their structural stability with consistent root mean square standard deviation, root mean square fluctuation, radius of gyration, and solvent accessible surface area (SASA) values. MD simulation revealed that both TSLP apoprotein and the beta carotene-TSLP complex maintained their stable conformations. MD simulation showed that both the FcεRI apoprotein and the beta carotene-FcεRI complex achieved stable structures with consistent radius of gyration and SASA values. PCA and FEL analyses revealed ligand-induced conformational variability. Although some physicochemical properties require further development, β-carotene, exhibiting very strong protein binding, and vitamin D3, particularly displaying good integration, superior drug-like properties, low toxicity, and good binding affinity, have been identified as promising candidates for the treatment of allergic diseases. This comprehensive approach combines structural chemistry, energy, and drug discovery to create a robust model for the rational design of allergy medications. This comprehensive approach combines structural chemistry, energetics, and drug discovery to create a powerful model for the rational design of allergy medications.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Unsal et al. (2026) studied this question.

synapsesocial.com/papers/69aa701a531e4c4a9ff59855https://doi.org/10.1186/s40360-026-01104-4
Ask AI
Helpful
Bookmark
Share
View Full Paper