Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search

Computer-aided synthesis planning (CASP) aims to automatically learn organic reactivity from literature and perform retrosynthesis of unseen molecules. CASP systems must learn reactions sufficiently precisely to propose realistic disconnections, while avoiding overfitting to leave room for diverse options, and explore possible routes such as to allow short synthetic sequences to emerge. Herein we report an open-source CASP tool proposing original solutions to both challenges. First, we use a triple transformer loop (TTL) predicting starting materials (T1), reagents (T2), and products (T3) to explore various disconnection sites defined by combining systematic, template-based, and transformer-based tagging procedures. Second, we integrate TTL into a multistep tree search algorithm (TTLA) prioritizing sequences using a route penalty score (RPScore) considering the number of steps, their confidence score, and the simplicity of all intermediates along the route. Our approach favours short synthetic routes to commercial starting materials, as exemplified by retrosynthetic analyses of recently approved drugs. An efficient transformer-based retrosynthesis model, the triple-transformer loop algorithm (TTLA), is reported and proposes short routes from commercial building blocks for a variety of drugs.

Automated summary

This study introduces a new tool for computer-aided synthesis planning that can automatically learn organic reactivity and perform retrosynthesis. The tool combines a triple transformer loop and a multistep tree search algorithm to propose short synthetic routes based on commercial starting materials.