Toward clinical translation of AI-Led drug discovery in endometrial cancer

Abstract

INTRODUCTION: Deep learning (DL) is transforming cancer research by enabling data-driven drug discovery. However, its clinical translation, particularly in endometrial cancer (EC), faces significant challenges. AREAS COVERED: This review discusses recent DL applications across drug discovery stages in EC, including target identification, virtual screening, and de novo drug design. We highlight key obstacles that hinder clinical translation, such as data scarcity, limited model explainability, biological validation gaps, and regulatory uncertainty, and propose practical solutions. Literature was sourced from PubMed, Web of Science, and relevant AI repositories, with an emphasis on peer-reviewed studies from the past five years. EXPERT OPINION: Despite early success, DL must overcome multiple translational bottlenecks to impact EC therapeutics meaningfully. A multidisciplinary approach that incorporates data quality improvements, functional validation, regulatory engagement, and clinician-focused decision support is essential to fully realize the clinical promise of DL-driven drug discovery in EC.

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