Navigating Route Latent Space for Synthesizable Molecular Design

2026-10-05

AI Summary

This study addresses the practical unsynthesizability of generated molecules in goal-directed molecular design, alongside the low search efficiency and goal deviation inherent in existing discrete-space methods. To overcome these limitations, this work proposes RouteFlow, a novel framework that reformulates synthesizable molecular design as a search problem within a continuous route latent space. The method introduces a cycle-consistency mechanism to prevent optimization from drifting off the true synthetic manifold and employs a reward-guided flow matching sampler for efficient directed optimization. Experimental evaluations across 16 tasks demonstrate that RouteFlow achieves state-of-the-art sample efficiency, synthetic accessibility, and retrosynthetic success rates, enabling efficient and intrinsically synthesizable molecular discovery.

Abstract

Goal-directed molecular design has advanced rapidly, yet a substantial proportion of designed molecules remain difficult to synthesize in practice, limiting their real-world utility. Prior synthesizability-aware methods either project generated molecules back to synthesizable analogs that deviate from the intended target, or optimize directly in discrete synthesis spaces that lack a continuous landscape for efficient search. We argue that this limitation mainly comes from the search space rather than the optimizer. To address this, we propose RouteFlow, a framework that reformulates synthesizable molecular design as a search over a continuous route latent space, where each latent maps back to a complete synthesis route and synthesizability is inherently preserved. To navigate this space, we adopt reward-guided flow matching as an efficient sampler that steers toward high-property regions. Since reward optimization may push latents off the manifold of real synthesis routes, where decoding becomes unreliable, we further introduce a cycle-consistency mechanism to stabilize fine-tuning. Across 16 optimization tasks from Therapeutic Data Commons, RouteFlow achieves the best sample efficiency among synthesizability-aware baselines, with the best synthetic accessibility and the highest retrosynthesis success rate. Our results also confirm that the proposed cycle-consistency reliably keeps optimization on-manifold while improving target properties, supporting effective synthesizable molecular discovery.

Problem

• molecular design
• synthesizability
• route latent space
• goal-directed optimization
• retrosynthesis

Innovation

• Route Latent Space
• Flow Matching
• Cycle Consistency
• Synthesizable Molecular Design
• Retrosynthesis