AI-driven chemistry and materials science platforms integrate machine learning models, curated experimental and simulation data, and automation orchestration to accelerate discovery, scale reproducibility, and shorten design cycles. This topic examines how vendors and open-source projects assemble data platforms and agentic automation layers to support tasks from molecular generative design and predictive simulation to automated experiment planning and execution. Relevance in late 2025 reflects two converging trends: (1) growing adoption of agentic frameworks and developer tooling (LangChain, Windsurf, Warp) that make it easier to compose multi-step ML workflows and lab-facing agents; and (2) hardware and inference consolidation — highlighted by NVIDIA’s post‑2024 activity around Deci.ai and products like NVIDIA Alchemi — plus energy‑efficient inference efforts (Rebellions.ai) that reduce runtime cost for large models. Enterprise controls and developer productivity tools (IBM watsonx Assistant, Tabnine, GitHub Copilot) are increasingly paired with domain platforms to enable governed model use, private code and data workflows, and reproducible pipelines. Research and knowledge discovery layers (Perplexity AI) surface grounded literature and real‑world data to feed models and experimental planning. Key categories: AI Data Platforms (data ingestion, simulation outputs, labeling, feature stores) and AI Automation Platforms (agent orchestration, no‑code/low‑code deployment, lab automation). Practical implementations blend LangChain‑style stateful orchestration, no‑code deployment via MindStudio, and developer integrations (Windsurf/Tabnine/Copilot) to shorten the loop from idea to experiment. Evaluations should focus on model fidelity for chemistry tasks, data lineage and provenance, orchestration reliability, and compute/inference cost — especially where specialized accelerators and enterprise governance are involved.