Rhizome OS-1: Rhizome's Semi-Autonomous Operating System for Small Molecule Drug Discovery

TL;DR

Rhizome OS-1 is a semi-autonomous AI-driven operating system that integrates multi-modal agents for small molecule drug discovery, enabling rapid, adaptive, and scalable inverse design with medicinal chemistry reasoning.

Contribution

The paper introduces Rhizome OS-1, a novel semi-autonomous system combining AI agents and graph-native generative models for efficient early-stage drug discovery.

Findings

Generated 5,231 novel molecules in two oncology campaigns.

91.9% of scaffolds are novel compared to ChEMBL.

Binding affinity predictions achieved high ROC AUC of 0.88-0.93.

Abstract

We present Rhizome OS-1, a semi-autonomous operating system for small molecule drug discovery in which multi-modal AI agents operate as a full multidisciplinary discovery team. These agents function as computational chemists, medicinal chemists, and patent agents: they write and execute analysis code (fingerprint clustering, R-group decomposition, substructure search), visually triage molecular grids using vision capabilities, formulate explicit medicinal chemistry hypotheses across three strategy tiers, assess patent freedom-to-operate, and dynamically adapt generation strategies based on empirical screening feedback. Powered by r1 - a 246M-parameter graph diffusion model trained on 800 million molecular graphs - the system generates novel chemical matter directly on molecular graphs using fragment masking, scaffold decoration, linker design, and graph editing primitives. In two oncology campaigns (BCL6 BTB domain and EZH2 SET domain), the agent team executed 26 seeds and produced 5,231 novel molecules. Across both targets, 91.9% of generated Murcko scaffolds are absent from ChEMBL, with median Tanimoto similarity of 0.56-0.69 to the nearest known active. Boltz-2 binding affinity predictions, calibrated against ChEMBL data, achieved Spearman correlations of -0.53 to -0.64 and ROC AUC values of 0.88-0.93. These results demonstrate that semi-autonomous agent systems, equipped with graph-native generative tools and physics-informed scoring, enable a new paradigm for early-stage drug discovery: scaled, rapid, and adaptive inverse design with embedded medicinal chemistry reasoning.