Cristian Gabellini — accepted AI-conference papers

2026

MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models

Kacper Kapuśniak, Cristian Gabellini, Michael M. Bronstein, Prudencio Tossou, Francesco Di Giovanni

ICLR 2026poster

Molecular Dynamics (MD) is a powerful computational microscope for probing protein functions. However, the need for fine-grained integration and the long timescales of biomolecular events make MD computationally expensive. To address this, several generative models have been proposed to generate sur…

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2024

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

Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov +28

ICLR 2024poster

Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, and codebases to manage those datasets, has hindered…

Cited by 47 Source ↗PDF ↗code ↗Scholar ↗