DDEL-15. EXPLORING THE USE OF MACHINE LEARNING TO PREDICT BLOOD BRAIN BARRIER PERMEABILITY OF DRUG MOLECULES

Abstract

Abstract The blood-brain barrier (BBB) is a selective boundary of the central nervous system (CNS) that plays a critical role in protecting the brain microenvironment by allowing passage of certain molecules. Clinical experiments accurately determine which chemotherapy drugs effectively cross the BBB to reach the tumor site but are also time consuming and labor intensive. Machine learning offers the ability to rapidly screen large datasets of drug molecules and assess their potential for CNS therapeutic effect. The goal of this study is to compare the performance across machine learning, deep learning, and transfer learning methods in predicting BBB permeability on public drug datasets. The dataset used for training and validation was composed of 7,807 compounds compiled from 50 published resources. The machine and deep learning methods used in this study included support vector machines (SVMs), deep neural networks (DNNs), and graph convolutional neural networks (GCNNs). For transfer learning, we first trained DNN models to a single quantum chemical property before appending neural network layers and retraining to the new task of BBB permeability. The prediction accuracies on the validation set for SVM, DNN, GCNN, transfer learning of polarizability, and transfer learning of dipole moment were 82.33%, 83.09%, 87.14%, 76.89%, and 70.23%, respectively. Overall, the results indicate that GCNN was the best performing model. This highlights GCNNs ability to learn the molecular features most relevant to the predictive task in the first stage of the algorithm. Future work entails expanding the set of input features to include key chemical and structural information such as the presence or absence of certain functional groups as well as training more transfer learning models to other quantum chemical properties. This study further motivates the predictive capability of machine learning methods in identifying drug compounds with potential CNS-activity.