Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search

Abstract

AI-based computer-aided synthesis planning (CASP) systems are in demand as components of AI-driven drug discovery workflows. However, the high latency of such CASP systems limits their utility for high-throughput synthesizability screening in de novo drug design. We propose a transformer-based single-step retrosynthesis model with reduced inference latency based on speculative beam search combined with a scalable drafting strategy called Medusa. Replacing the standard transformer and beam search with our approach accelerates the expansion stage of the planning algorithm, leading to higher solvability in CASP when planning under stringent time limits, and saves hours of computation when synthesis is constrained by the number of iterations. Our method brings AI-based CASP systems closer to meeting the stringent latency requirements of high-throughput synthesizability screening and improving the overall user experience.