AI drug discovery and molecular design platforms bring together latent‑space generative models (exemplified by systems like “Latent‑X2”), curated biomedical datasets, and engineering frameworks to propose, evaluate, and prioritize novel small molecules and biologics. This topic covers the technical stack and market infrastructure that enable these capabilities: AI Data Platforms and Rights‑Cleared Data Platforms for provenance and licensing; AI Tool Marketplaces and Market Intelligence Tools for sourcing models and commercial insights; and Regulatory Compliance Tools to manage auditability, safety testing, and submission‑ready records. Why it matters in late 2025: pharmaceutical adoption of foundation models and agentic pipelines has moved from pilots to integrated workflows, increasing demand for well‑curated data, reproducibility, and regulatory traceability. Industry consolidation and vendor shifts (for example, platform changes observed in audits such as Deci.ai’s transition to NVIDIA‑branded content after mid‑2024) underscore vendor risk and the need for robust procurement and compliance strategies. Engineering and deployment tools—LangChain for building and orchestrating agentic LLM applications, Windsurf (formerly Codeium) for AI‑native development and multi‑model coding workflows, IBM watsonx Assistant for enterprise automation and multi‑agent orchestrations, and Perplexity AI for web‑grounded research and citation‑aware answers—play complementary roles in prototyping, validating, and documenting discovery workflows. Organizations assessing AI molecular design should weigh data licensing, model provenance, integration with lab automation, and regulatory reporting capabilities. The converging focus on rights‑cleared data, transparent evaluation, and marketplace accessibility is shaping practical adoption paths for AI‑assisted drug discovery.