Efficient and Programmable Exploration of Synthesizable Chemical Space

TL;DR

PrexSyn is a transformer-based model that efficiently explores synthesizable chemical space, enabling programmable molecular discovery and optimization with high speed and property-specific control.

Contribution

It introduces PrexSyn, a large-scale, decoder-only transformer trained on synthesizable pathways, capable of property-conditioned molecule generation and optimization.

Findings

Achieves near-perfect reconstruction of synthesizable chemical space.

Generates molecules satisfying complex property conditions.

Outperforms synthesis-agnostic baselines in sampling efficiency.

Abstract

The constrained nature of synthesizable chemical space poses a significant challenge for sampling molecules that are both synthetically accessible and possess desired properties. In this work, we present PrexSyn, an efficient and programmable model for molecular discovery within synthesizable chemical space. PrexSyn is based on a decoder-only transformer trained on a billion-scale datastream of synthesizable pathways paired with molecular properties, enabled by a real-time, high-throughput C++-based data generation engine. The large-scale training data allows PrexSyn to reconstruct the synthesizable chemical space nearly perfectly at a high inference speed and learn the association between properties and synthesizable molecules. Based on its learned property-pathway mappings, PrexSyn can generate synthesizable molecules that satisfy not only single-property conditions but also composite property queries joined by logical operators, thereby allowing users to ``program'' generation objectives. Moreover, by exploiting this property-based querying capability, PrexSyn can efficiently optimize molecules against black-box oracle functions via iterative query refinement, achieving higher sampling efficiency than even synthesis-agnostic baselines, making PrexSyn a powerful general-purpose molecular optimization tool. Overall, PrexSyn pushes the frontier of synthesizable molecular design by setting a new state of the art in synthesizable chemical space coverage, molecular sampling efficiency, and inference speed.