MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization

TL;DR

MolClaw is an autonomous AI agent designed for drug molecule evaluation, screening, and optimization, utilizing a hierarchical skill architecture to orchestrate complex workflows and achieve state-of-the-art performance.

Contribution

The paper introduces MolClaw, a hierarchical skill-based framework that unifies over 30 domain resources for robust, long-term drug discovery workflows, and presents MolBench for benchmarking.

Findings

MolClaw outperforms existing methods across all metrics.

Structured workflow orchestration is key to AI performance in drug discovery.

Ablation studies show structured skills improve complex task handling.

Abstract

Computational drug discovery, particularly the complex workflows of drug molecule screening and optimization, requires orchestrating dozens of specialized tools in multi-step workflows, yet current AI agents struggle to maintain robust performance and consistently underperform in these high-complexity scenarios. Here we present MolClaw, an autonomous agent that leads drug molecule evaluation, screening, and optimization. It unifies over 30 specialized domain resources through a three-tier hierarchical skill architecture (70 skills in total) that facilitates agent long-term interaction at runtime: tool-level skills standardize atomic operations, workflow-level skills compose them into validated pipelines with quality check and reflection, and a discipline-level skill supplies scientific principles governing planning and verification across all scenarios in the field. Additionally, we introduce MolBench, a benchmark comprising molecular screening, optimization, and end-to-end discovery challenges spanning 8 to 50+ sequential tool calls. MolClaw achieves state-of-the-art performance across all metrics, and ablation studies confirm that gains concentrate on tasks that demand structured workflows while vanishing on those solvable with ad hoc scripting, establishing workflow orchestration competence as the primary capability bottleneck for AI-driven drug discovery.