2. Academic Validation
  3. Identification of novel DYRK1A inhibitors as treatment options for alzheimer's disease through comprehensive in silico approaches

Identification of novel DYRK1A inhibitors as treatment options for alzheimer's disease through comprehensive in silico approaches

  • Sci Rep. 2025 Oct 15;15(1):36114. doi: 10.1038/s41598-025-23431-y.
Ibrahim Akindeji Makinde 1,  Sheriffdeen Oladimeji Hammed 2 3,  Neeraj Kumar 4,  Najwa Ahmad Kuthi 5,  Haruna Isiyaku Umar 6 7,  Temitayo Abidat Hassan 3 8,  Idayat Oyinkansola Kehinde 3,  Ridwan Opeyemi Bello 3 9,  Abdullahi Tunde Aborode 3 10,  Tajudeen Ogundare Jimoh 11,  Al-Djazouli O Mahamat 12,  Mohammad Khalid 13,  Omar A Almohammed 14
Affiliations

Affiliations

  • 1 Department of Information Systems, School of Computing (SOC), Federal University of Technology Akure, P.M.B 704, Akure, Nigeria.
  • 2 Department of Biotechnology, Federal University of Technology, P. M. B. 704, Akure, Nigeria.
  • 3 Computer-Aided Therapeutic Discovery and Design Laboratory, Federal University of Technology, P. M. B. 704, Akure, Nigeria.
  • 4 Department of Pharmaceutical Chemistry, Bhupal Nobles' College of Pharmacy, Udaipur, 313001, Rajasthan, India.
  • 5 Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia.
  • 6 Computer-Aided Therapeutic Discovery and Design Laboratory, Federal University of Technology, P. M. B. 704, Akure, Nigeria. ariwajoye3@gmail.com.
  • 7 Department of Biochemistry, Federal University of Technology, P. M. B. 704, Akure, Ondo State, Nigeria. ariwajoye3@gmail.com.
  • 8 Department of Biochemistry, Federal University of Technology, P. M. B. 704, Akure, Ondo State, Nigeria.
  • 9 Faculty of Medicine, University of Queensland, Brisbane, Australia.
  • 10 Department of Research and Development, Healthy Africans Platform, Ibadan, Nigeria.
  • 11 Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
  • 12 Faculty of Exact Applied and Sciences, Department of Earth Sciences, N Djamena University, N Djamena, Chad. djazoulimahamatresearcher@gmail.com.
  • 13 Department of Pharmaceutics, College of Pharmacy, King Khalid University, Abha, 61421, Asir, Saudi Arabia.
  • 14 Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. oalmohammed@KSU.EDU.SA.
PMID: 41094020 DOI: 10.1038/s41598-025-23431-y
Abstract

This study aims to identify potential DYRK1A inhibitors from a curated database and utilize a QSAR model to predict the bioactivity of drug compounds in inhibiting the enzyme involved in Tau Protein oligomerization, a key process in AD pathology. 192 compounds were sourced from the SuperNatural 3.0 database and docked against DYRK1A using Maestro 12.5. The top five lead compounds and the reference drug Abemaciclib underwent ADMET profiling via the AI Drug Lab Server and a 200 nanosecond molecular dynamics simulation using Desmond. A machine learning-based Quantitative Structure-Activity Relationship (QSAR) analysis was then performed to predict their biological activity based on pIC50 values. The top five compounds, identified as 45,934,388, CNP0344929, CNP0360040, CNP0309850, and CNP0426983, demonstrated binding affinities of -13.337, -12.746, -11.712, -11.656, and - 11.416 kcal/mol, respectively, outperforming Abemaciclib (-6.528 kcal/mol). None of the compounds violated Lipinski's Rule of Five, and all exhibited favorable ADMET profiles, including optimal blood-brain barrier penetration and structural stability. The QSAR model successfully predicted the pIC50 values of the hit compounds (6.16, 5.758, 5.752, 6.003, 5.982), comparable to Abemaciclib (6.32). These findings highlight five promising DYRK1A inhibitors with potential therapeutic applications for AD.

Keywords

Alzheimer’s disease; DYRK1A inhibitors; Machine learning; Molecular docking; QSAR; Virtual screening.

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