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Improving Molecular Pretraining with Complementary Featurizations
Yanqiao Zhu · Dingshuo Chen · Yuanqi Du · Yingze Wang · Qiang Liu · Shu Wu

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. In light of this observation, we propose a simple and effective MOlecular pretraining framework with COmplementary featurizations (MOCO). MOCO comprehensively leverages multiple featurizations that complement each other and outperforms existing state-of-the-art models that solely relies on one or two featurizations on a wide range of molecular property prediction tasks.

Author Information

Yanqiao Zhu (University of California at Los Angeles)
Dingshuo Chen (University of Chinese Academy of Sciences)
Yuanqi Du (Cornell University)
Yingze Wang (University of California, Berkeley)
Qiang Liu (Institute of Automation, Chinese Academy of Sciences)
Shu Wu (Institute of automation, Chinese academy of science, Chinese Academy of Sciences)

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