Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures

By Pedro Hermosilla, Marco Schäfer, Matěj Lang, Gloria Fackelmann, Pere-Pau Vázquez, Barbora Kozliková, Michael Krone, Tobias Ritschel, Timo Ropinski. 2021

124 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Zuobai Zhang
2. Minghao Xu
3. Arian Jamasb
4. Jian Tang
5. Vijil Chenthamarakshan
6. Aurelie C. Lozano
7. Payel Das
8. Andreas Krause

Citations in the corpus

• Multi-Scale Representation Learning on Proteins (2022, methodology): “The baseline models [Rao et al., 2019, Hermosilla et al., 2021] take about 40 hours for running 300 epochs, while the remaining baseline models train in under 24 hours.”
• Protein Representation Learning by Geometric Structure Pretraining (2022, methodology): “…variety of tasks such as protein design (Ingraham et al., 2019; Strokach et al., 2020; Cao et al., 2021; Jing et al., 2021), structure classification (Hermosilla et al., 2021), model quality assessment (Baldassarre et al., 2021; Derevyanko et al., 2018), and function prediction (Gligorijevi´c et…”