Gotta be SAFE: A New Framework for Molecular Design

1 code implementation • 16 Oct 2023 • Emmanuel Noutahi, Cristian Gabellini, Michael Craig, Jonathan S. C Lim, Prudencio Tossou

Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of molecular substructures.

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

1 code implementation • 6 Oct 2023 • Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michał Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew Fitzgibbon, Błażej Banaszewski, Chad Martin, Dominic Masters

Recently, pre-trained foundation models have enabled significant advancements in multiple fields.

Reconstruction of Long-Term Historical Demand Data

no code implementations • 10 Sep 2022 • Reshmi Ghosh, Michael Craig, H. Scott Matthews, Constantine Samaras, Laure Berti-Equille

Long-term planning of a robust power system requires the understanding of changing demand patterns.

Sparse hierarchical representation learning on molecular graphs

no code implementations • 6 Aug 2019 • Matthias Bal, Hagen Triendl, Mariana Assmann, Michael Craig, Lawrence Phillips, Jarvist Moore Frost, Usman Bashir, Noor Shaker, Vid Stojevic

Architectures for sparse hierarchical representation learning have recently been proposed for graph-structured data, but so far assume the absence of edge features in the graph.

Drug Discovery Representation Learning