TL;DR
The findings demonstrate the potential of combining generative AI with high-throughput computational screening as an efficient, reliable, and scalable approach to identify inhibitors for breast cancer targets, although these inhibitors need to be tested in the lab to validate their biological activity and potential for translation.
This study demonstrates the viability of the deep learning models to discover structurally novel anticancer agents that are distinct from conventional drugs, thereby expanding the therapeutic arsenal for TNBC patients.
This review emphasizes the influence of new AI platforms like AlphaFold3, molecular interactions are structurally optimized (MISATO), and ZairaChem on the discovery of oncology drugs and examines how AI reconciles chemical design with pharmacological feasibility.
Several 1,2,3-triazole hybrids based on cabotegravir were assessed in an integrated computational methodology for their potential as anticancer agents by targeting a lung cancer-associated protein.
The combined computational and experimental approach enabled the identification of peptides with selective cytotoxic effects and favorable predicted immunogenic profiles, demonstrating that integrating computational screening with experimental validation is an effective strategy for accelerating the discovery of select...
WI23-B is highlighted as a promising lead peptide with potent WWP1 inhibitory activity and synergistic antiproliferative effects when combined with PI3K inhibitors, and has the potential to reshape therapeutic strategies for BC and TNBC by enabling more effective and less toxic treatment regimens.
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