MolecureAI: an agentic AI platform for autonomous drug discovery.

Accelerating the identification of viable drug candidates across therapeutic areas demands autonomous systems for molecular generation that enforce chemical correctness by structural invariants rather than model capability. We present MolecureAI, an autonomous AI agent architecture for closed-loop drug discovery with cost-ordered cascade evaluation that addresses four capability gaps unresolved across the field: autonomous design strategy selection, persistent cross-campaign structural memory, in-loop ADMET safety gating, and literature-scale evidence integration into the generative loop. A cost-ordered cascade concentrates full structure-based evaluation on a small pre-filtered survivor set, achieving a 2.5 × reduction in candidates advanced relative to those generated. An in-loop metabolic gate enforces Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) safety by writing flagged reactive substructures to a per-target learned state, suppressing identified liabilities from all subsequent generation rounds. A persistent campaign memory influences generation trajectories by embedding prior structural success and failure patterns directly into the design context. We validate these architectural invariants across three structurally diverse benchmark targets (EGFR, KRAS G 12 C , and BRD4) through a five-condition component ablation and a controlled memory ablation, and confirm generator agnosticism across four language-model backends via a deterministic recalibration layer that overrides backend self-evaluation bias. Without explicit chemotype instruction, the pipeline computationally converged toward candidates bearing the 4-anilinoquinazoline scaffold characteristic of the approved inhibitors erlotinib and gefitinib, from structural feedback alone. Persistent campaign memory showed directional but non-significant trends toward lower inter-round score variance and higher late-stage advance rate ( n ≈ 90 per condition; all p > 0.20 ). The component ablation reveals that optimizing for a single objective collapses performance on others; only full orchestration resolves this tension. Advancement rates above 79% across all backends confirm that the validated properties are generator-agnostic, enforced by architectural invariants rather than model capability. All reported outcomes are properties of the computational generation process; prospective biochemical validation of the advanced candidates remains the primary direction for future work.