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Equilibrium and non-equilibrium thermodynamics in drug repurposing: Machine learning-guided discovery of

Stalin Arulsamy1, Tathagata Chanda2, Wajid Aslam Khan3

  • 1Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab, 144411, India.

Journal of Molecular Graphics & Modelling
|May 18, 2026
PubMed

Insights

This study uses machine learning and thermodynamics to find new WEE1 kinase inhibitors for cancer therapy. The computational approach accelerates drug repurposing, identifying promising candidates like acarbose and quercetin derivatives.

Area of Science:

  • Oncology
  • Computational Chemistry
  • Pharmacology

Background:

  • WEE1 kinase is a key regulator of cell cycle checkpoints and a significant therapeutic target in cancer treatment.
  • Conventional drug discovery methods for WEE1 inhibitors are time-consuming and resource-intensive.
  • Accelerating the identification of novel WEE1 inhibitors is crucial for advancing cancer therapy.

Purpose of the Study:

  • To develop and validate a computational framework for rapid identification of WEE1 kinase inhibitors.
  • To leverage machine learning and thermodynamic analyses for drug repurposing from FDA-approved libraries.
  • To discover novel WEE1 inhibitor candidates with potential therapeutic applications.

Main Methods:

  • Integrated machine learning with equilibrium and non-equilibrium thermodynamic analyses.
  • Employed structure-based virtual screening, molecular docking, and molecular dynamics simulations.
  • Utilized steered molecular dynamics for kinetic dissociation analysis and machine learning for activity prediction.

Main Results:

  • Identified acarbose and quercetin derivatives as promising WEE1 inhibitor candidates.
  • Demonstrated superior binding profiles of identified candidates compared to existing kinase inhibitors.
  • Machine learning model achieved high accuracy in predicting compound activity, enabling efficient candidate prioritization.

Conclusions:

  • The study presents a novel computational framework for accelerated drug repurposing.
  • Non-equilibrium thermodynamics provided unique insights into inhibitor unbinding mechanisms.
  • Experimental validation is essential to confirm the efficacy of computationally predicted WEE1 inhibitors.
Keywords:
Drug repurposingIn-silico screeningWEE1 kinase

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