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Article

Identification of GPI-Anchored Wall Transfer Protein 1 Modulators for Fungal Infections through Generative AI and Physics-Based Approaches

by
Ibrahim A. Alsarra
1,
Rupesh Chikhale
2,
Abdullah M. Al-Mohizea
1 and
Md Ataul Islam
3,4,*
1
Department of Pharmaceutics, College of Pharmacy, King Saud University, National Address: RGSA8707, P.O. Box 2457, Riyadh 11451, Saudi Arabia
2
Department of Pharmaceutical and Biological Chemistry, School of Pharmacy, University College London, London WC1N 1AX, UK
3
SilicoScientia Private Limited, Nagananda Commercial Complex, No. 07/3, 15/1, 18th Main Road, Jayanagar 9th Block, Bengaluru 560 041, India
4
SilicoScientia Private Limited, Centre for Cellular and Molecular Platforms (C-CAMP), GKVK Campus, Bellary Road, Bengaluru 560 065, India
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2026, 27(11), 4767; https://doi.org/10.3390/ijms27114767 (registering DOI)
Submission received: 31 March 2026 / Revised: 30 April 2026 / Accepted: 22 May 2026 / Published: 25 May 2026

Abstract

Glycosylphosphatidylinositol (GPI) anchored wall transfer protein 1 (GWT1), a fungal-specific inositol acyltransferase, catalyzes the palmitoylation of GlcN-PI in GPI-anchor biosynthesis, crucial for mannoprotein trafficking and attachment, which are vital for cell wall integrity, biofilm formation, and virulence. More than 60,000 AI-generated molecules produced using REINVENT4 were screened using ADMET-AI and GNINA. DeepSA and PharmacoNet were used to select synthesizable and pharmacophorically rich molecules. The dynamic behaviour was explored using molecular dynamics (MD). Finally, molecular reactivity was assessed using density functional theory (DFT). After ADMET filtering, 6,190 compounds were docked against GWT1, of which 315 showed better predicted binding energies than the co-crystal ligand. DeepSA identified 105 readily synthesizable candidates, and PharmacoNet retained 32 compounds with favourable pharmacophoric features, from which four final candidates (AF_M1, AF_M2, AF_M3, and AF_M4) were prioritized for further analysis. MD simulation suggested stable binding behavior towards GWT1. DFT analysis indicated favourable electronic properties, low HOMO-LUMO energy gaps, and stable optimized geometries. These molecules could serve as promising lead candidates and potential new therapeutic agents for invasive fungal infections, pending validation.
Keywords: GPI-anchored wall transfer protein 1; fungal infections; generative artificial intelligence; machine learning; SilicoXplore; virtual screening GPI-anchored wall transfer protein 1; fungal infections; generative artificial intelligence; machine learning; SilicoXplore; virtual screening

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MDPI and ACS Style

Alsarra, I.A.; Chikhale, R.; Al-Mohizea, A.M.; Islam, M.A. Identification of GPI-Anchored Wall Transfer Protein 1 Modulators for Fungal Infections through Generative AI and Physics-Based Approaches. Int. J. Mol. Sci. 2026, 27, 4767. https://doi.org/10.3390/ijms27114767

AMA Style

Alsarra IA, Chikhale R, Al-Mohizea AM, Islam MA. Identification of GPI-Anchored Wall Transfer Protein 1 Modulators for Fungal Infections through Generative AI and Physics-Based Approaches. International Journal of Molecular Sciences. 2026; 27(11):4767. https://doi.org/10.3390/ijms27114767

Chicago/Turabian Style

Alsarra, Ibrahim A., Rupesh Chikhale, Abdullah M. Al-Mohizea, and Md Ataul Islam. 2026. "Identification of GPI-Anchored Wall Transfer Protein 1 Modulators for Fungal Infections through Generative AI and Physics-Based Approaches" International Journal of Molecular Sciences 27, no. 11: 4767. https://doi.org/10.3390/ijms27114767

APA Style

Alsarra, I. A., Chikhale, R., Al-Mohizea, A. M., & Islam, M. A. (2026). Identification of GPI-Anchored Wall Transfer Protein 1 Modulators for Fungal Infections through Generative AI and Physics-Based Approaches. International Journal of Molecular Sciences, 27(11), 4767. https://doi.org/10.3390/ijms27114767

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