Improving Molecular Pretraining with Complementary Featurizations

Understand

Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery.

• Recently, prosperous progress has been made in molecular pretraining with different molecular featurizations, including 1D SMILES strings, 2D graphs, and 3D geometries.
• However, the role of molecular featurizations with their corresponding neural architectures in molecular pretraining remains largely unexamined.
• In this paper, through two case studies -- chirality classification and aromatic ring counting -- we first demonstrate that different featurization techniques convey chemical information differently.