Fragment-level feature fusion method using retrosynthetic fragmentation algorithm for molecular property prediction

Abstract

Recent advancements in Artificial Intelligence (AI) and deep learning have had a significant impact on drug discovery. The prediction of molecular properties, such as toxicity and blood-brain barrier (BBB) permeability, is crucial for accelerating drug development. The accuracy of these predictions largely depends on the selection of molecular descriptors. Self-supervised learning (SSL) has gained prominence due to its strong generalization capabilities. Graph contrastive learning (GCL), a type of SSL, is particularly useful in this context. Current GCL methods for molecular graphs use various data augmentation techniques, which may potentially alter the inherent structure of molecules. Additionally, traditional single-perspective representations do not fully capture the complexity of molecules. We present RFA-FFM (Fragment-level Feature Fusion Method using Retrosynthetic Fragmentation Algorithm), which integrates molecular representations from multiple perspectives. This method employs two strategies: (1) contrasting chemical information from fragments generated by two retrosynthetic methods to provide detailed contrastive insights; (2) fusing chemical information at different levels of molecular hierarchy, including the entire molecule and its fragments. Experiments show that RFA-FFM enhances the performance of deep learning models in predicting molecular properties, improving ROC-AUC scores by 0.3 %–2.6 % compared to baselines across four classification benchmarks. Case studies on hepatitis B virus datasets demonstrate that RFA-FFM outperforms baselines by 7 %–11 %. When compared to BPE and CC-Single fragmentation algorithms, RFA-FFM shows a 2 %–4 % improvement in BBB permeability tasks, thus demonstrating its effectiveness in predicting molecular properties.