no code implementations • 2 Dec 2022 • Saee Paliwal, Angus Brayne, Benedek Fabian, Maciej Wiatrak, Aaron Sim
In this paper we generalize single-relation pseudo-Riemannian graph embedding models to multi-relational networks, and show that the typical approach of encoding relations as manifold transformations translates from the Riemannian to the pseudo-Riemannian case.
2 code implementations • 26 Nov 2020 • Benedek Fabian, Thomas Edlich, Héléna Gaspar, Marwin Segler, Joshua Meyers, Marco Fiscato, Mohamed Ahmed
We apply a Transformer architecture, specifically BERT, to learn flexible and high quality molecular representations for drug discovery problems.