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Maciej Sypetkowski

4 accepted papers

2024

How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval

Philip Fradkin, Puria Azadi Moghadam, Karush Suri, Frederik Wenkel, Ali Bashashati, Maciej Sypetkowski +1
NeurIPS 2024poster

Predicting molecular impact on cellular function is a core challenge in therapeutic design. Phenomic experiments, designed to capture cellular morphology, utilize microscopy based techniques and demonstrate a high throughput solution for uncovering molecular impact on the cell. In this work, we lear…

Cited by 2Source ↗PDF ↗Scholar ↗
2024

Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

Oren Kraus, Kian Kenyon-Dean, Saber Saberian, Maryam Fallah, Peter McLean, Jess Leung +11
CVPR 2024highlight

Featurizing microscopy images for use in biological research remains a significant challenge especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training wit…

Cited by 34Source ↗PDF ↗code ↗Scholar ↗
2024

On the Scalability of GNNs for Molecular Graphs

Maciej Sypetkowski, Frederik Wenkel, Farimah Poursafaei, Nia Dickson, Karush Suri, Philip Fradkin +1
NeurIPS 2024poster

Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation. Practitioners have observed a strong relationship between model size, dataset size, and performance. However, structure-based architectures such as Graph Neural Networks (GNNs) are ye…

Cited by 12Source ↗PDF ↗Scholar ↗
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 38Source ↗PDF ↗code ↗Scholar ↗