Semi-autoregressive Your Bridge: Chemical Space Projecting with Discrete Markov Bridges
rongchao zhang, Yongzhi Cao, Hanpin Wang, Yu Huang
Molecular chemical space projecting (MCSP) is a critical technique in de novo molecular design, which aims to rectify molecules without synthesizability guarantee by converting them into synthetic postfix notations. However, the vast synthesizable chemical space and the discrete data modalities involved pose significant challenges to postfix notation conversion benchmarking, and existing MCSP frameworks suffering from error accumulation. In this paper, we exploit conditional probability transitions in discrete state space and introduce MoleBridge, a deep generative model built on the Markov bridge approach for designing postfix notations of molecular synthesis pathways. MoleBridge consists of two iterative optimizations: i) semi-autoregressive extending of reaction tokens from molecular graphs, and ii) generation of discrete reaction postfix notations from the entire approximate token sequence while allowing the latter to backward jump for error rectification. For the challenging second iteration, which demands sensitivity to incorrect generative probability paths within intricate chemical spaces, we employ a thinking and denoising separation approach to denoise when an error occurs at a certain step. Endowed by this, our uniquely modulated generative perspective allows the model to retain scalable search capability for the progressively increasing chemical space and prevents the accumulation of error. Empirically, we find that MoleBridge is capable of accurately predicting synthesis pathways while exhibiting excellent performance in a variety of application scenarios.