Volume 10 Number 2 2026
AI-Driven Target Identification Reshapes Early-Stage Drug Discovery in Triple-Negative Breast Cancer
Li Kar Stella Tan1, Jhi Biau Foo1, 2,, Yong Sze Ong3, Chee Wun How3
Integrative Biomedical Research 10 (2) 1-8 https://doi.org/10.25163/biomedical.10210953
Submitted: 10 September 2026 Revised: 02 November 2026 Accepted: 10 November 2026 Published: 12 November 2026
Triple-negative breast cancer (TNBC) has long resisted targeted therapy, largely because it lacks the estrogen, progesterone, and HER2 receptors that give clinicians a molecular handle elsewhere in breast cancer. That absence left oncologists relying on broadly cytotoxic chemotherapy and a slow, hypothesis-driven discovery process that struggles against so heterogeneous and unstable a tumor. This review asks a direct question: has artificial intelligence actually changed that picture, or is enthusiasm running ahead of the evidence? Drawing on eighty peer-reviewed sources spanning dermatological and oncological applications of machine learning, deep learning, and computer-aided drug design, we trace how computational methods are reshaping early-stage target identification in TNBC, while situating that work within the broader AI-oncology landscape from which many of its methods originated. The synthesis points to a genuinely convergent picture: phenotypic screening, transcriptomic feature selection, digital pathology, and generative chemistry, developed independently across research groups, keep arriving at an overlapping shortlist of biologically plausible targets — AKT, FGFR2, MFGE8, TGFβR1, and BRCA1/2-linked synthetic lethal networks among them. At the same time, the translational picture is not as settled as some performance metrics suggest; dataset bias, algorithmic opacity, and a shortage of prospective clinical validation remain real constraints on how much of this promise has reached patients. What this review offers, ultimately, is less a verdict than a structured accounting of where the evidence is strong, where it is preliminary, and what a more explainable, federated, clinically validated AI ecosystem would need to look like before TNBC target discovery moves from the computer screen to the clinic.
Keywords: Triple-negative breast cancer; Artificial intelligence; Machine learning; Target identification; Computer-aided drug design; Digital pathology; Synthetic lethality
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