Computer-Aided Drug Design Across Breast Cancer Subtypes: Methods, Applications and Translational Outlook

Area of Science:

• Oncology
• Computational Chemistry
• Pharmacology

Background:

• Breast cancer is a complex disease with diverse molecular subtypes, necessitating tailored therapeutic strategies.
• Traditional drug discovery is slow and costly, highlighting the need for efficient, subtype-aware methods.
• Computer-aided drug design (CADD) offers a promising avenue for accelerating breast cancer drug discovery and optimizing lead compounds.

Purpose of the Study:

• To review CADD advancements for breast cancer therapy development from a subtype-specific perspective.
• To outline the application of CADD techniques in identifying targets and inhibitors for Luminal, HER2-positive, and triple-negative breast cancer.
• To highlight the integration of artificial intelligence (AI) and machine learning (ML) with traditional CADD methods.

Main Methods:

• Molecular docking, virtual screening (VS), pharmacophore modeling, and molecular dynamics (MD) simulations were applied.
• AI-enabled methods and hybrid workflows combining machine learning and physics-based simulations were explored.
• Subtype-specific compound identification and lead drug refinement for enhanced efficacy and reduced toxicity were investigated.

Main Results:

• CADD techniques successfully identified potential targets and inhibitors across distinct breast cancer subtypes.
• AI and hybrid approaches demonstrated efficiency in navigating chemical space and providing mechanistic validation.
• Subtype-specific compounds were discovered, and candidate drugs were refined to improve therapeutic profiles.

Conclusions:

• CADD, particularly with AI integration, significantly advances subtype-specific breast cancer drug discovery.
• Challenges include tumor heterogeneity, drug resistance, and the necessity of experimental validation.
• Future progress relies on integrating AI, ML, multi-omics, and digital pathology for personalized cancer therapies.