Abstract
Accurate molecular property prediction is crucial for drug discovery and computational chemistry, facilitating the identification of promising compounds and accelerating therapeutic development. Traditional machine learning falters with high-dimensional data and manual feature engineering, while existing deep learning approaches may not capture complex molecular structures, leaving a research gap. We introduce Deep-CBN, a novel framework designed to enhance molecular property prediction by capturing intricate molecular representations directly from raw data, thus improving accuracy and efficiency. Our methodology combines convolutional neural networks (CNNs) with a BiFormer attention mechanism, employing both the forward-forward algorithm and backpropagation. The model operates in three stages: (1) feature learning, extracting local features from SMILES strings using CNNs; (2) attention refinement, capturing global context with a BiFormer module enhanced by the forward-forward algorithm; and (3) prediction subnetwork tuning, fine-tuning via backpropagation. Evaluations on benchmark datasets-including Tox21, BBBP, SIDER, ClinTox, BACE, HIV, and MUV-show that Deep-CBN achieves near-perfect ROC-AUC scores, significantly outperforming state-of-the-art methods. These findings demonstrate its effectiveness in capturing complex molecular patterns, offering a robust tool to accelerate drug discovery processes.