Multi-task learning models for predicting active compounds

The computational drug discovery methods can find potential drug-target interactions more efficiently and have been widely studied over past few decades. Such methods explore the relationship between the structural properties of compounds and their biological activity with the assumption that similar compounds tend to share similar biological targets and vice versa. However, traditional Quantitative Structure - Activity Relationship (QSAR) methods often do not have desired accuracy due to insufficient data of compound activity. In this paper, we focus on building Multi-Task Learning (MTL)-based QSAR models by considering multiple similar biological targets together and make shared information transfer across from one task to another, thereby improving not only the learning efficiency, but also the prediction accuracy. This paper selects 6 assay groups with similar biological targets from PubChem and builds their QSAR models with MTL simultaneously. According to the experiment results, our MTL-based QSAR models have better performance over traditional prominent machine learning algorithms and the improvements are even more obvious when other baseline models have low accuracy. The superiority of our models is also proved by Student's t-test with level of significance 5%. Moreover, this paper also explores three different assumptions on the underlying pattern in the dataset and finds that the joint feature MTL models further improve the performance of the QSAR models and are more suitable for building QSAR models for multiple similar biological targets.