85 citations · 192 across the 14 of their papers we have counts for
3 papers · 1 filter
Role of Structural and Conformational Diversity for Machine Learning Potentials
Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi +3
In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically conformational and structural diversity,…
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini, Shenyang Huang, Joao Alex Cunha +32
Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and…
Gotta be SAFE: A New Framework for Molecular Design
Emmanuel Noutahi, Cristian Gabellini, Michael Craig +2
Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of molecular substructures.…