Substructure-Aware Program Synthesis for Automated Chemical Reaction Network Discovery

Abstract

Chemical Reaction Networks (CRNs) are essential for understanding complex reactive processes, yet incomplete experimental data often leave many networks only partially known. Grammar-driven program synthesis offers an approach to completing partial CRNs, but atom-by-atom construction of molecular candidates causes a severe combinatorial explosion, and the baseline synthesiser lacks awareness of the structural context of target molecules. It is not yet known whether substructure-aware heuristics can improve the computational tractability of CRN discovery via program synthesis. To investigate this, BRICS (Breaking of Retrosynthetically Interesting Chemical Substructures) fragments were incorporated into the molecular context-free grammar, and Tanimoto similarity of Morgan2 fingerprints was used to guide reaction and network synthesis. The results show that BRICS fragmentation achieves a 13.8 percentage-point improvement in completing partial reactions on a dataset missing complex organic compounds by encoding large substructures as single grammar rules. Conversely, the enhanced grammar solves 27.4 percentage points fewer problems than the baseline on reactions missing small non-carbon species, as increased branching delays discovery of small molecules. Moreover, molecular similarity guidance does not improve performance in reaction rebalancing from SynRXN datasets, but it substantially reduces the search space in an example esterification CRN synthesis problem, requiring 2,565 fewer candidate reactions and 409 fewer candidate networks before discovering the targets. Thus, BRICS fragmentation and similarity-guided heuristics have distinct strengths. Future frameworks should split the candidate molecule pool between fragment-enhanced and atom-by-atom methods to successfully capture both large structural fragments and small, dissimilar species.