DeepHDAC3i: Leveraging an Interpretable Deep Learning-Based Framework for the Accelerated Discovery of HDAC3 Inhibitors

Epigenetics entails reversible modifications that regulate gene activity without altering DNA. Non-coding RNA interactions and DNA methylation direct expression in response to signaling and environmental cues. Histone acetylation, controlled by deacetylases (HDACs) and acetyltransferases (HATs), is disrupted by aberrant HDAC upregulation. While HDAC inhibitors are used therapeutically, their lack of specificity underscores the need for highly selective alternatives. Machine learning (ML)-driven methods are recognized as rapid and cost-efficient tools in drug discovery and development, capable of identifying inhibitors from SMILES notation, without requiring 3D ligand structure. Here, we present a novel and interpretable deep learning-based framework, DeepHDAC3i, for accurate in silico identification of HDAC3i using only the SMILES notation. Firstly, we employed five molecular encoding methods to extract the biological and structural information in HDAC3i. These molecular representations were then fused to generate multi-view features. Secondly, elastic net was employed to determine the optimal feature subset and enhance prediction performance. Thirdly, a one-dimensional convolutional neural network (1D-CNN) coupled with the optimal feature set was chosen for the construction of the final model. Finally, our framework leveraged the Shapley Additive exPlanation algorithm to disclose the most important features for identifying HDAC3i. On the independent test dataset, DeepHDAC3i achieved an accuracy of 0.965, MCC of 0.930, and AUC of 0.985, which were significantly higher than several conventional machine learning and deep learning models. In addition, upon comparison with the existing methods, DeepHDAC3i secured the best performance with improvements of approximately 4.80, 4.70, 6.50, and 9.50% in accuracy, F1, AUC, and MCC, respectively.