UC Irvine

One of the widely discussed problems in the feld of chemoinformatics is the prediction of molecular properties. These properties can range from physical, chemical, or biological properties of molecules to the behaviour or molecules under certain chemical conditions.Traditionally, these properties were calculated using chemical experiments. But with the increase in computational capabilities, various machine learning methods like neural networks and kernel methods have also been tried. These approaches have been successful to a certain extent. But with recent development in data, deep machine learning techniques have been developed, which matches or exceed the performance of state-of-the-art techniques. One such approach is to consider the molecular graphs of the molecules and use them for prediction. Here, I discuss this approach in great details and provide its application on two problems: predicting aqueous solubility and melting point.