A Systematic Analysis of Multi-Property Prediction Using Deep Learning In The Field Of Drug Development

In the rapidly changing field of pharmaceutical development, it is crucial to speed up clinical trials in order to introduce effective drugs in a timely manner. Conventional methods of forecasting molecular characteristics are often slow and time consuming, causing difficulties in the drug discovery process. However, Advancements in deep learning have streamlined pharmaceutical testing, enabling precise prediction of key drug properties like solubility, toxicity, reactivity, and bioactivity before synthesis. By employing different Simplified Molecular Input Line Entry System (SMILES) representations and training them with various deep learning algorithms like Graph Neural Networks (GNN) and Graph Convolution Networks (GCN). In this, a comparative study done on various technologies used in predicting molecular properties that helps to develop faster clinical trials in pharmaceutical industry. By identifying the limitations in the existing technologies, proposed an improved GCN which helps in better estimation of molecular properties.