# An effective self-supervised framework for learning expressive molecular global representations to drug discovery

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/an-effective-self-supervised-framework-for-learning-expressive-molecular-global/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Pengyong Li,Jun Wang,Yixuan Qiao,Hao Chen,Yihuan Yu,Xiaojun Yao,Peng Gao,Guotong Xie,Sen Song |
| Citations | 153 |
| DOI | 10.1093/bib/bbab109 |
| Fields | Biochemistry, Genetics and Molecular Biology,Computer Science,Materials Science |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3179111421 |
| PMID | 33940598 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Guotong Xie](https://scholariq.org/researchers/guotong-xie/)

## Paper primary topic

- [Computational Drug Discovery Methods](https://scholariq.org/topics/computational-drug-discovery-methods/)

## Paper topics

- [Computational Drug Discovery Methods](https://scholariq.org/topics/computational-drug-discovery-methods/)
- [Machine Learning in Materials Science](https://scholariq.org/topics/machine-learning-in-materials-science/)
- [Protein Structure and Dynamics](https://scholariq.org/topics/protein-structure-and-dynamics/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.