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Abstract <p>The dysregulation of the cell cycle in cancer underscores the therapeutic potential of targeting WEE1 kinase, a key regulator of the G2/M checkpoint. This study harnessed artificial intelligence (AI)-driven methodologies, particularly the MORLD platform, to identify novel WEE1 inhibitors. Starting with clinically validated WEE1 inhibitors as references, we generated 20,000 structurally diverse compounds optimized for binding affinity, synthetic accessibility, and drug-likeness. A rigorous cheminformatics pipeline—comprising PAINS filtering, physicochemical property assessments, and molecular fingerprinting—refined this library to 242 promising candidates. Dimensionality reduction using UMAP and clustering via <i>K</i>-means enabled the prioritization of structurally unique leads. Molecular docking studies highlighted two compounds, MORLD5036 and MORLD6305, with exceptional binding affinities and interactions with key WEE1 active site residues. Molecular dynamics simulations and MM-GBSA binding free energy calculations further validated MORLD5036 as the most stable and potent inhibitor. Scaffold analysis revealed novel chemotypes distinct from existing inhibitors, enhancing potential for intellectual property. Comprehensive ADME profiling confirmed favorable pharmacokinetics, while synthetic accessibility evaluations indicated practicality for experimental validation. The identified lead compound, MORLD5036, exhibits favorable pharmacokinetics and novel chemotypes, positioning it as a potential therapeutic candidate for cancers reliant on WEE1-mediated cell cycle control. This integrated, AI-driven pipeline expedites the identification of next-generation WEE1 inhibitors, paving the way for advancements in precision oncology. Unlike traditional methods reliant on pre-existing datasets, this study leverages MORLD’s reinforcement learning framework to autonomously generate inhibitors, enabling exploration of uncharted chemical space. These findings establish MORLD5036 as a computationally promising WEE1 inhibitor candidate warranting further experimental validation.</p> Graphical abstract <p></p>

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Integrated AI and machine learning pipeline identifies novel WEE1 kinase inhibitors for targeted cancer therapy

  • 期刊论文

  • 发表日期：

    2025年3月19日

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  • Jaikanth Chandrasekaran,
  • Dhanushya Gopal,
  • Lokesh Vishwa Sureshkumar,
  • Infant Xavier Santhiyagu,
  • Varsha Senthil Kumar,
  • Bhuvaneshwari Munuswamy,
  • Beevi Fathima Harshatha Mohamed Yousuf Gani,
  • Mohit Agrawal

摘要

Abstract

The dysregulation of the cell cycle in cancer underscores the therapeutic potential of targeting WEE1 kinase, a key regulator of the G2/M checkpoint. This study harnessed artificial intelligence (AI)-driven methodologies, particularly the MORLD platform, to identify novel WEE1 inhibitors. Starting with clinically validated WEE1 inhibitors as references, we generated 20,000 structurally diverse compounds optimized for binding affinity, synthetic accessibility, and drug-likeness. A rigorous cheminformatics pipeline—comprising PAINS filtering, physicochemical property assessments, and molecular fingerprinting—refined this library to 242 promising candidates. Dimensionality reduction using UMAP and clustering via K-means enabled the prioritization of structurally unique leads. Molecular docking studies highlighted two compounds, MORLD5036 and MORLD6305, with exceptional binding affinities and interactions with key WEE1 active site residues. Molecular dynamics simulations and MM-GBSA binding free energy calculations further validated MORLD5036 as the most stable and potent inhibitor. Scaffold analysis revealed novel chemotypes distinct from existing inhibitors, enhancing potential for intellectual property. Comprehensive ADME profiling confirmed favorable pharmacokinetics, while synthetic accessibility evaluations indicated practicality for experimental validation. The identified lead compound, MORLD5036, exhibits favorable pharmacokinetics and novel chemotypes, positioning it as a potential therapeutic candidate for cancers reliant on WEE1-mediated cell cycle control. This integrated, AI-driven pipeline expedites the identification of next-generation WEE1 inhibitors, paving the way for advancements in precision oncology. Unlike traditional methods reliant on pre-existing datasets, this study leverages MORLD’s reinforcement learning framework to autonomously generate inhibitors, enabling exploration of uncharted chemical space. These findings establish MORLD5036 as a computationally promising WEE1 inhibitor candidate warranting further experimental validation.

Graphical abstract