This topic surveys the ecosystem of AI models and platforms applied to drug discovery—exemplified by model families such as Latent‑X2 (latent‑space generative chemistry) and MolNet (graph/transformer ensembles for property prediction and virtual screening)—and the tools used to build, run, and deploy them. It covers three practical categories: AI data platforms that curate and serve chemical and biological datasets; data analytics and engineering tools that train, evaluate, and orchestrate models; and AI tool marketplaces and galleries that accelerate reuse and deployment. Relevance and timing (2025‑12‑26): model specialization for molecular tasks, tighter integration of multimodal biochemical data, and industry consolidation around inference/optimization stacks have accelerated production use. Signals such as the NVIDIA takeover of Deci (site audit shows Deci content folded into NVIDIA after May 2024) point to continued hardware/ML‑ops alignment. At the same time, demand for reproducible pipelines, data governance, and no‑code deployment pathways has risen as labs and CROs move from prototypes to regulated workflows. Key tools and their roles: LangChain and similar engineering frameworks are used to orchestrate multi‑step discovery agents and evaluation pipelines; IBM watsonx Assistant and MindStudio provide enterprise and no‑code routes to assemble agentic assistants and automate experimental workflows; Dataisland and other AI data platforms ingest and index documents, assay results, and SOPs for searchable knowledge bases; Vellum and marketplaces supply reusable prompt/workflow templates; AutoGPT‑style runtimes enable autonomous experiment orchestration; developer tools like GitHub Copilot speed model and automation coding. Together these layers form the operational stack for turning models such as Latent‑X2 and MolNet into validated, auditable drug‑discovery applications.