Improved Molecular Binding Affinity Prediction using Deep Learning through Integration of Chemical Structure

The prediction of molecular interactions plays a crucial role in drug discovery and computational biology. This paper introduced a new method employing advanced computer techniques, specifically a hybrid model utilizing the Improved Molecular Binding Affinity approach using deep learning. The approach integrated information from two distinct sources the structural representation of chemicals in a Simplified Molecular Input Line Entry System (SMILES) and the protein sequence which is the amino acid sequence. This approach made use of the different information provided by each data type. This involved a bioinformatics approach, combining a hybrid model that utilizes the Bidirectional Long Short-Term Memory (BiLSTM) to understand detailed patterns in both chemical and biological data. This network processed input data in both forward and backward directions, allowing it to capture connections in both past and future contexts. This bidirectional processing helped the network to better understand and represent sequential patterns and long-range relationships in the input data. The model was trained on a diverse and extensive dataset, with the help of GPU support, and its performance was evaluated against measurement standards for predictions. The binding affinity as a result showed that the hybrid model, considering both chemical and biological information, was better at predicting how molecules stick together. This study helped advance computer-based molecular biology and made a strong foundation for understanding how molecules interact.