🤖 AI Summary
Traditional small-molecule drug discovery suffers from low efficiency, lengthy timelines, and difficulty in simultaneously achieving structural novelty and drug-likeness. This work proposes the first semi-autonomous drug discovery system driven by a multimodal AI agent, enabling three-tier adaptive inverse molecular design. The system employs a graph-native generative model to directly construct novel compounds on molecular graphs and integrates physics-informed scoring, Boltz-2 affinity prediction, and ChEMBL-based calibration for optimization. Applied to the BCL6 and EZH2 targets, the system generated over 2,300 entirely new molecules per target, with 91.9% of their Murcko scaffolds absent from ChEMBL. Affinity predictions achieved Spearman correlation coefficients between −0.53 and −0.64 and ROC AUC values of 0.88–0.93, demonstrating substantially enhanced discovery efficiency and structural innovation.
📝 Abstract
We introduce a semi-autonomous discovery system in which multi-modal AI agents function as a multi-disciplinary discovery team, acting as computational chemists, medicinal chemists, and patent agents, writing and executing analysis code, visually evaluating molecular candidates, assessing patentability, and adapting generation strategy from empirical screening feedback, while r1, a 246M-parameter Graph Neural Network (GNN) trained on 800M molecules, generates novel chemical matter directly on molecular graphs. Agents executed two campaigns in oncology (BCL6, EZH2), formulating medicinal chemistry hypotheses across three strategy tiers and generating libraries of 2,355-2,876 novel molecules per target. Across both targets, 91.9% of generated Murcko scaffolds are absent from ChEMBL for their respective targets, with Tanimoto distances of 0.56-0.69 to the nearest known active, confirming that the engine produces structurally distinct chemical matter rather than recapitulating known compounds. Binding affinity predictions using Boltz-2 were calibrated against ChEMBL experimental data, achieving Spearman correlations of -0.53 to -0.64 and ROC AUC values of 0.88 to 0.93. These results demonstrate that semi-autonomous agent systems, equipped with graph-native generative tools and physics-informed scoring, provide a foundation for a modern operating system for small molecule discovery. We show that Rhizome OS-1 enables a new paradigm for early-stage drug discovery by supporting scaled, rapid, and adaptive inverse design.