Abstract
Over the past few years, a number of Graph Neural Network (GNN) architectures have been effectively employed for molecular analysis. However, generating annotated molecular data usually requires molecular dynamics or quantum chemistry calculations, which can be extremely time-consuming. To address this challenge, we introduce a predictive equivariant self-supervision technique that is founded on perturbing the 3D positions of the atoms. This method is ideal for 3D molecular data and allows the network to initially learn general structural information before fine-tuning it for specific tasks. We demonstrate that these pre-training procedures can also be utilized to fine-tune the network for learning molecular properties on a different dataset. Our pre-training method is demonstrated to surpass previously proposed solutions via extensive experiments on different standard molecular datasets.
Original language | English |
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Title of host publication | Proceedings of the 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing, MLSP 2023 |
Editors | Danilo Comminiello, Michele Scarpiniti |
Publisher | IEEE Computer Society |
ISBN (Electronic) | 9798350324112 |
DOIs | |
State | Published - 2023 |
Event | 33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023 - Rome, Italy Duration: 17 Sep 2023 → 20 Sep 2023 |
Publication series
Name | IEEE International Workshop on Machine Learning for Signal Processing, MLSP |
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Volume | 2023-September |
ISSN (Print) | 2161-0363 |
ISSN (Electronic) | 2161-0371 |
Conference
Conference | 33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023 |
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Country/Territory | Italy |
City | Rome |
Period | 17/09/23 → 20/09/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- 3D Molecular Analysis
- Equivariance
- Graph Neural Networks
- pre-training