This topic covers automated laboratory and scientific automation platforms as they are applied to materials and drug discovery, contrasting research-driven systems (such as DeepMind’s automated-lab initiatives) with modular commercial automation stacks. As of 2025-12-11, the field is defined by convergence between cloud ML platforms, multimodal generative models, and robotic orchestration: platforms that design experiments, control instruments, capture provenance, and close the loop between hypothesis generation and high-throughput execution. Key platform roles and tools include: Vertex AI and Google Gemini for building, fine-tuning and serving multimodal models that propose experiments and analyze complex datasets; Claude, Cohere, Mistral and IBM watsonx for conversational assistants, private/custom models, and governance-aware LLMs used in protocol generation, ELN/LIMS querying and decision support; Anakin.ai and Microsoft 365 Copilot for no-code workflow automation and productivity integrations that lower the barrier to orchestrating routine tasks; PDF.ai and similar document-centric tools to extract protocols, methods and literature insights into structured knowledge. Typical trade-offs are visible: research labs often prototype tightly integrated, closed-loop systems emphasizing rapid scientific iteration, while commercial stacks prioritize modularity, regulatory compliance, vendor interoperability and enterprise-grade deployment. Important trends include stronger emphasis on provenance, data privacy and model governance; wider adoption of multimodal models for instrument-aware control and imagery; and growth of no-code orchestration to democratize automation. This overview helps teams evaluate when to adopt research-style integrated automation versus configurable commercial stacks based on speed-to-discovery, reproducibility, compliance and scaling considerations.