Improving Molecular Property Prediction on Limited Data with Deep Multi-Label Learning

Abstract

Acquiring labeled data has been widely recognized as a major challenge in molecular property prediction. Since it generally requires a series of specialized biochemical experiments which are time-consuming, costly, as well as labor-intensive. The deficiency of labeled property data makes it difficult to learn a good prediction model. Here, we propose an RNN-based multi-label molecular property prediction method to alleviate the data scarcity issue in two stages: 1) utilize the abundant unlabeled SMILES data to pre-train a seq2seq model whose encoder learns to generate molecular fingerprint based on the given SMILES; and 2) finetune the pre-trained model on the labeled molecular property data. Since labeled data is limited, we train those properties with limited sample size jointly with other properties which contain relatively sufficient samples. This approach brings in the idea of multi-label training, which is able to pre-train and fine-tune the encoder network, as well as train the prediction network with a data augmentation strategy. Extensive experiments on molecular property prediction demonstrate that our proposed method has achieved superior performance compared with the state-of-the-art approaches on properties with limited sample size.