🤖 AI Summary
This study addresses the lack of systematic comparison among molecular encoding methods in terms of both predictive performance and interpretability for drug property prediction. The authors propose a hybrid model combining multilayer perceptrons and Transformer encoders (MLP+TL) to comprehensively evaluate topological fingerprints, substructure-based fingerprints (e.g., MACCS, PubChem), and string representations across seven molecular datasets and multiple biologically relevant classification tasks. Innovatively leveraging the model’s intrinsic attention weights—without relying on external interpretation tools—the work identifies critical chemical moieties, revealing mechanistic insights such as the influence of hydroxyl groups on blood–brain barrier permeability and Salmonella mutagenicity. The model achieves average AUC scores exceeding 0.9 in toxicity, mutagenicity, and side-effect prediction tasks, demonstrating both high predictive accuracy and inherent chemical interpretability.
📝 Abstract
Fundamental investigations into how different molecular encoding methods affect molecular property prediction remain relatively limited. In this study, we extensively examined the optimal molecular encoding methods for molecular properties prediction using two prevalent structure designs: a classical neural network model (MLP) and a Transformer encoder-based model (MLP+TL). For molecular encoding methods, we investigated several types of fingerprints, including traditional topological fingerprints, substructure-based fingerprints, and string-based representations. These two models were trained on seven well-known molecular datasets to evaluate different input molecular encoding methods based on evaluation metrics. On several biologically relevant classification tasks, including toxicity, mutagenicity, and side-effect prediction, our models consistently achieved average AUC values above 0.9. Rather than relying on external post-hoc explanation methods such as the local interpretable model-agnostic explanation (LIME) or the Deep SHapley Additive exPlanations (SHAP), we leveraged the model's intrinsic attention weights as an internal interpretability signal for identifying potentially important feature. The MLP+TL model using MACCS and PubChem as input can capture chemically interpretable groups that determined the major blood-brain barrier (BBB) permeability and mutagenicity in Salmonella typhimurium. In particular, a comparison between Morphine and Heroin highlighted the role of hydroxyl-related substructures in BBB permeability prediction, which was consistently reflected in the attention weights. Overall, our findings provide practical guidance for selecting effective molecular encoding methods and contribute to the development of interpretable molecular informatics approaches for drug discovery.