Procedural Pretraining for Molecular Property Prediction
It is found that procedural pretraining can improve molecular property prediction even after subsequent molecular pretraining, and procedural data can provide transferable structure for molecular learning and offer a complementary route to improving performance when labeled molecular data are limited.
M. Friedemann, Zachary Shinnick, Philip H. S. Torr et al.
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