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Accurate molecular property prediction is fundamental to drug discovery and is critically governed by molecular representations. While most existing approaches primarily focus on small molecules, extending reliable prediction to structurally complex macrocyclic compounds remains challenging due to their conformational flexibility and nonlocal interactions. To bridge this gap, we developed automated molecular property prediction (AutoMPP), an automated machine learning-based pipeline that automates model selection and systematically evaluates fingerprint combinations across 75 molecular property prediction tasks. The results demonstrate that multifingerprint fusion significantly improves predictive robustness, with a four-fingerprint combination achieving superior performance across diverse molecular tasks. Using this optimized representation strategy, AutoMPP outperforms leading models Uni-Mol and fingerprints and graph neural networks (FP-GNN), securing top performance on 68% (51/75) of tasks. Notably, AutoMPP generalizes effectively to macrocycles, such as cyclic peptide, attaining a Pearson correlation coefficient of 0.794, outperforming Uni-Mol (0.692) and FP-GNN (0.681). Furthermore, by integrating SHapley Additive exPlanations, the framework offers chemist-intelligible insights into the structural determinants. Together, these results establish AutoMPP as a robust and adaptable framework for molecular property prediction, capable of identifying task-specific optimal fingerprint combinations and learning architectures for both small molecules and complex macrocycles.
Qian et al. (Wed,) studied this question.