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

Understand

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 method for accelerating multi-step synthesis planning systems that rely on SMILES-to-SMILES transformers as single-step retrosynthesis models.
• Our approach reduces the latency of SMILES-to-SMILES transformers powering multi-step synthesis planning in AiZynthFinder through speculative beam search combined with a scalable drafting strategy called Medusa.