AI-driven drug discovery platforms bundle generative modelling, data pipelines, lab informatics and orchestration to accelerate target selection, molecule design and preclinical prioritization. Isomorphic Labs’ IsoDDE represents one such integrated environment; competing approaches split responsibilities across foundation-model providers, compute/cloud accelerators, knowledge platforms and domain-specific tooling. This topic surveys how those pieces fit together and how vendors differ by focus: foundational-model vendors and production stacks (e.g., Mistral AI) supply efficient, enterprise-grade models and governance; cloud acceleration and training services (e.g., Together AI) provide scalable GPU training, fine-tuning and serverless inference; enterprise assistants and orchestration layers (e.g., IBM watsonx Assistant) enable no-code automation, multi-agent workflows and experiment orchestration; and specialist search, knowledge and collaboration tools (DeeperMind.ai, PDF.ai, Aurora AI, Notion, Gumnut, Asana) close the loop on data access, documentation, team collaboration and experimental tracking. Relevance and timing: by early 2026 the field is consolidating around multimodal and domain-specialized models, MLOps for regulated R&D, and tighter integrations between model outputs and lab execution. Key decision factors are model provenance and governance, data platform interoperability (omics, screening, ELN/LIMS), compute cost and latency for iterative design–test cycles, and collaboration/CI tooling that preserves reproducibility and IP. Practical deployments therefore mix specialty discovery platforms (like IsoDDE) with foundational-model suppliers, scalable training/inference clouds, and enterprise knowledge/collaboration layers to manage workflows and regulatory traceability. Evaluations should focus on end-to-end data integration, model customization and validation, auditability, vendor lock-in risk, and how easily model hypotheses translate into executable experiments.