Integrating convolutional layers and biformer network with forward-forward and backpropagation training.

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.