Logos: An evolvable reasoning engine for rational molecular design

TL;DR

Logos is a compact, interpretable molecular reasoning model that combines logical reasoning with chemical validity, achieving high accuracy and reliability in molecular design tasks while enabling human inspection of its reasoning process.

Contribution

The paper introduces Logos, a novel molecular reasoning engine that integrates multi-step logical reasoning with chemical constraints, improving interpretability and validity in molecular design.

Findings

Achieves high structural accuracy and chemical validity on benchmark datasets.

Operates with fewer parameters than larger language models.

Demonstrates stable performance in multi-constraint molecular optimization.

Abstract

The discovery and design of functional molecules remain central challenges across chemistry,biology, and materials science. While recent advances in machine learning have accelerated molecular property prediction and candidate generation, existing models tend to excel either in physical fidelity without transparent reasoning, or in flexible reasoning without guarantees of chemical validity. This imbalance limits the reliability of artificial intelligence systems in real scientific design workflows.Here we present Logos, a compact molecular reasoning model that integrates multi-step logical reasoning with strict chemical consistency. Logos is trained using a staged strategy that first exposes the model to explicit reasoning examples linking molecular descriptions to structural decisions, and then progressively aligns these reasoning patterns with molecular representations. In a final training phase, chemical rules and invariants are incorporated directly into the optimization objective, guiding the model toward chemically valid outputs. Across multiple benchmark datasets, Logos achieves strong performance in both structural accuracy and chemical validity, matching or surpassing substantially larger general-purpose language models while operating with a fraction of their parameters. Beyond benchmark evaluation, the model exhibits stable behaviour in molecular optimization tasks involving multiple, potentially conflicting constraints. By explicitly exposing intermediate reasoning steps, Logos enables human inspection and assessment of the design logic underlying each generated structure. These results indicate that jointly optimizing for reasoning structure and physical consistency offers a practical pathway toward reliable and interpretable AI systems for molecular science, supporting closer integration of artificial intelligence into scientific discovery processes.