Figure 1.

Illustration of the self-supervised learning platform. (a) Three public data sets are involved in the pretraining data sets module (blue rectangle). Set C only contains the ChEMBL data set. Set CP consists of ChEMBL and PubChem data sets, and Set CPZ contains ChEMBL, PubChem, and ZINC data sets. (b) Based on those three data sets, three pretrained models (green rectangle) are obtained by self-supervised learning, which is Model C, Model CP, and Model CPZ, respectively. c The data set analysis module (purple rectangle) contains the Wasserstein distance analysis module and decision module. It will point to the best pretrained model for a specific data set. (d) The fine-tune module (yellow rectangle) fine-tunes the pretrained model using a specific data set. Finally, the fingerprints are generated from the fine-tuned model and used as input for the downstream machine learning tasks. (e) The correlations between pretraining data sets and downstream data sets, including five classifications (Classif.) and five regressions (Regre.) data sets, and pretrained data sets C, CP, and CPZ. (f) Normalized predicted results of the fingerprints from pretrained model C for DPP4, ESOL, FreeSolv, Lipophilicity (Lipop), and LogS five regression data sets.