Synthetic tabular data lets organisations share, rebalance, and stress-test sensitive in-house tables-but only if the result is actually fit for the downstream task. Yet a table that matches the real distribution on aggregate statistics can still be unfit for its downstream task: it loses the rare cases that matter, is not optimised for the end task, and silently breaks the deterministic relations that domain data must obey. We present TabAutoSyn, an end-to-end, generator-agnostic pipeline that turns any base generator into deployment-ready synthetic data through four automated stages: generate, verify, augment, and curate. A multi-agent LLM module discovers functional dependencies and domain constraints from the real table and repairs or removes violating rows; a divergence-minimising stage rebuilds under-represented tails; and a curation stage provably never lowers a chosen downstream score. Across 22 public and proprietary datasets and eleven generators spanning five model families (GAN, VAE, diffusion, language-model, and graph), TabAutoSyn delivers the best train-on-synthetic utility and the highest validity while preserving privacy. Because it is generator-agnostic, it attains these results on a commodity GPU at a fraction of the cost of the strongest LLM-based baselines, and it needs no per-dataset tuning-making it practical to run on sensitive, in-house data.

Hit identification is a central challenge in early drug discovery, traditionally requiring substantial experimental resources. Recent advances in artificial intelligence, particularly large language models (LLMs), have enabled virtual screening methods that reduce costs and improve efficiency. However, the growing complexity of these tools has limited their accessibility to wet-lab researchers. Multi-agent systems offer a promising solution by combining the interpretability of LLMs with the precision of specialized models and tools. In this work, we present MADD, a multi-agent system that builds and executes customized hit identification pipelines from natural language queries. MADD employs four coordinated agents to handle key subtasks in de novo compound generation and screening. We evaluate MADD across seven drug discovery cases and demonstrate its superior performance compared to existing LLM-based solutions. Using MADD, we pioneer application of AI-first drug design to five biological targets and release the identified hit molecules. Finally, we introduce a new benchmark of query-molecule pairs and docking scores for over three million compounds to contribute to the agentic future of drug design.