Symbiotic Message Passing Model for Transfer Learning between Anti-Fungal and Anti-Bacterial Domains
Ronen Taub, Tanya Wasserman, Yonatan Savir · 2023
Machine learning, and representation learning in particular, has the potential to facilitate drug discovery by screening billions of compounds. For example, a successful approach is representing the molecules as a graph and utilizing graph neural networks (GNN). Yet, these approaches still require experimental measurements of thousands of compounds to construct a proper training set. While in some domains it is easier to acquire experimental data, in others it might be more limited. For example, it is easier to test the compounds on bacteria than perform in vivo experiments. Thus, a key question is how to utilize information from a large available dataset together with a small subset of compounds where both domains are measured to predict compounds’ effect on the second, experimentally less available domain. Current transfer learning approaches for drug discovery, including training of pre-trained modules or meta-learning, have limited success. In this work, we develop a novel method, named Symbiotic Message Passing Neural Network (SMPNN), for merging graph neural network models from different domains. By routing new message passing lanes between them, our approach resolves some of the potential conflicts between the different domains, and implicit constraints induced by the larger datasets. With the use of public data and additional high-throughput experiments, we demonstrate the advantages of our approach by predicting anti-fungal activity from anti-bacterial activity. We compare our method to the standard transfer learning approach and show that the SMPNN provided better and less variable performances. Our approach is general and can be used to facilitate information transfer between any two domains such as different organisms, different organelles, or different environments.