Structure-Based Drug Discovery for Invasive Lobular Carcinoma: Druggable Targets and Translational Opportunities

Background: Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer and is characterized by the loss of E-cadherin resulting from alterations in the CDH1 gene. Despite accounting for approximately 10–15% of breast cancers, ILC remains understudied compared with invasive ductal carcinoma and presents unique challenges related to diagnosis, endocrine resistance, metastatic progression, and treatment. Recent molecular studies have revealed distinct genomic alterations and signaling dependencies that may provide novel therapeutic opportunities. Methods: This review summarizes current knowledge of the molecular landscape of ILC and critically examines established and emerging therapeutic targets, including PI3K/AKT/mTOR, FGFR1, ROS1, SRC/FAK, Hippo–YAP/TEAD, and estrogen receptor signaling pathways. The application of structure-based drug discovery (SBDD) approaches, including molecular docking, pharmacophore modeling, virtual screening, molecular dynamics simulations, binding free-energy calculations, and artificial intelligence-assisted methodologies, is discussed in the context of ILC drug discovery. Translational opportunities involving natural products, drug repurposing, combination therapies, and precision oncology are also evaluated. Results: Accumulating evidence indicates that CDH1 loss creates unique molecular vulnerabilities that can be therapeutically exploited. Several signaling pathways involved in tumor growth, survival, invasion, and endocrine resistance have emerged as promising therapeutic targets. Advances in structural biology, computational chemistry, and artificial intelligence have expanded opportunities for rational drug design and target prioritization. Furthermore, drug repurposing strategies, natural product-derived compounds, and multi-target therapeutic approaches offer additional avenues for accelerating the development of ILC-specific treatments. Conclusions: ILC possesses a distinct molecular architecture that supports the development of precision therapeutic strategies beyond those traditionally applied to other breast cancer subtypes. The integration of structural biology, SBDD, artificial intelligence, and translational oncology provides a promising framework for identifying and optimizing therapies targeting CDH1-loss-associated vulnerabilities. Continued efforts combining computational discovery with experimental validation are expected to accelerate the development of personalized treatment strategies and improve clinical outcomes for patients with ILC.