AI platforms for drug discovery and computational chemistry bring together model engineering, data management, agent orchestration, and specialized inference infrastructure to accelerate molecular design, target identification, and simulation workflows. As of 2026, the field emphasizes integrated data platforms and tool marketplaces that connect provenance-aware datasets, multimodal models (including LLMs), and reproducible pipelines for chemistry and biology teams. Key building blocks include engineering frameworks and agent runtimes (e.g., LangChain) to compose stateful, agentic workflows; enterprise assistants (IBM watsonx Assistant) for no-code and developer-driven orchestration of lab and desk workflows; and web-grounded research engines (Perplexity AI) for rapid literature and patent triage. Developer tooling — GitHub Copilot, Windsurf (formerly Codeium), Tabnine, and Tabby — speeds model development, dataset curation, and automation of simulation code. Marketplaces and deployment platforms (Agentverse, Vellum showcases) make it easier to package, share, and monitor autonomous agents and domain-specific tools. On the infrastructure side, consolidation and specialization in inference and optimization (signals such as the Deci → NVIDIA transition and vendors like Rebellions.ai) highlight a push toward energy-efficient, high-throughput model serving for compute-heavy molecular simulations and generative chemistry. Practically, these components address common bottlenecks: integrating heterogeneous experimental and simulated data, maintaining model provenance and governance, enabling reproducible ML-driven experiments, and scaling inference cost-effectively. For organizations evaluating offerings, priority areas in 2026 are data integration and governance, agent orchestration for end-to-end workflows, validated model/tool marketplaces, and access to optimized inference hardware and software stacks.