HALO: Interactive Co-abductive Reasoning in Scientific Hypothesis Generation

Scientific discovery, particularly in drug discovery, is often hindered by the difficulty of navigating vast chemical search spaces. While generative AI can suggest new molecular candidates, it often fails to support the iterative reasoning process required to turn these candidates into viable, high-quality hypotheses. Researchers must manually manage complex trade-offs between competing pharmacological properties, such as solubility and toxicity, which often leads to surface-level exploration and missed opportunities for innovation.

The authors introduce a framework called co-abduction, which formalizes the human-AI collaboration process as an abductive reasoning loop. This framework is implemented in HALO, an interactive system designed to assist medicinal chemists through three distinct stages:

1. Improved-candidate Clustering: The system generates molecular candidates and groups them based on their specific property-improvement profiles.
2. Strategy Identification: The AI explains patterns within these clusters and suggests intra-cluster strategies to improve specific, lagging properties.
3. Multi-strategy Synthesis: The AI proposes combinations of strategies across different clusters, allowing researchers to iteratively synthesize and refine molecules that balance multiple, often conflicting, pharmacological requirements.

In an expert study involving 10 medicinal chemists, HALO was compared against a baseline system that provided AI-generated candidates without the structured abductive reasoning support. Participants using HALO reported significantly higher satisfaction and demonstrated improved performance in observation, strategy identification, and multi-strategy composition. Notably, the study observed that once users experienced an "abductive leap"—a moment of sudden insight—they became more proactive, shifting from relying on the AI for suggestions to using it as a tool for validating their own manual refinements. This suggests that effective AI tools for science should adapt their level of intervention based on the user's cognitive stage in the discovery process.