This topic covers modern AI models used for drug discovery and molecular design, centering on latent‑space generative approaches (exemplified by “Latent‑X2”) and the alternative model families that are widely adopted in 2025. Latent models map molecules into continuous representations that enable conditioned generation, interpolation and optimization; alternatives include graph‑based VAEs/flow models, equivariant diffusion models that respect 3D geometry, and language‑to‑chemistry transformers tuned for multiobjective scoring. Practical pipelines now combine these generators with 3D conformer and docking tools, physics‑informed scoring, and synthetic‑accessibility filters to produce actionable candidates. Relevance and timing: by late 2025 the field has moved from proof‑of‑concept demonstrations to integrated discovery workflows. Key trends are 3D‑aware generative models, latent diffusion methods for efficient sampling, stronger emphasis on multiobjective optimization (potency, ADMET, synthesizability), and rising expectations around provenance, benchmarking and reproducibility. These shifts make model choice and systems engineering—how models are chained, tested and governed—critical for adoption in industrial and regulated settings. Tooling and ecosystem roles: engineering and orchestration platforms (e.g., LangChain) are used to build, debug and deploy multi‑step model chains and evaluation loops; enterprise assistants (IBM watsonx Assistant) provide no‑code and developer workflows, governance checkpoints and multi‑agent orchestration; autonomous agent platforms (AutoGPT) can automate iterative design‑evaluate cycles and run large experiment workflows; and code models (Salesforce CodeT5) accelerate pipeline development, test generation and reproducibility. Categories such as GenAI test automation, 3D model generation tools, and AI data platforms are central to validating models, producing structure‑aware inputs, and managing datasets and audit trails. Taken together, evaluating Latent‑X2 and its alternatives requires both model‑level comparison (architecture, 3D awareness, objective handling) and systems‑level consideration (tooling, automation, data governance) to move from candidate generation to reliable discovery outcomes.