Towards foundational models for molecular learning on large-scale multi-task datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple
fields. In molecular machine learning, however, where datasets are often hand-curated, and …
fields. In molecular machine learning, however, where datasets are often hand-curated, and …
Floor plan reconstruction from sparse views: Combining graph neural network with constrained diffusion
We address the challenging problem of floor plan reconstruction from sparse views and a
room-connectivity graph. As a first stage, we construct a flexible graph-structure unifying the …
room-connectivity graph. As a first stage, we construct a flexible graph-structure unifying the …
Reducing the cost of quantum chemical data by backpropagating through density functional theory
A Mathiasen, H Helal, P Balanca, A Krzywaniak… - arXiv preprint arXiv …, 2024 - arxiv.org
Density Functional Theory (DFT) accurately predicts the quantum chemical properties of
molecules, but scales as $ O (N_ {\text {electrons}}^ 3) $. Sch\" utt et al.(2019) successfully …
molecules, but scales as $ O (N_ {\text {electrons}}^ 3) $. Sch\" utt et al.(2019) successfully …
Motif-driven molecular graph representation learning
R Wang, Y Ma, X Liu, Z Xing, Y Shen - Expert Systems with Applications, 2025 - Elsevier
Abstract Graph Neural Networks (GNNs) have emerged as powerful tools for molecular
graph analysis. Subgraph-based GNNs focus on learning high-level local patterns beyond …
graph analysis. Subgraph-based GNNs focus on learning high-level local patterns beyond …
: A Parameter-Efficient Foundation Model for Molecular Learning
K Kläser, B Banaszewski, S Maddrell-Mander… - arXiv preprint arXiv …, 2024 - arxiv.org
In biological tasks, data is rarely plentiful as it is generated from hard-to-gather
measurements. Therefore, pre-training foundation models on large quantities of available …
measurements. Therefore, pre-training foundation models on large quantities of available …
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval
Predicting molecular impact on cellular function is a core challenge in therapeutic design.
Phenomic experiments, designed to capture cellular morphology, utilize microscopy based …
Phenomic experiments, designed to capture cellular morphology, utilize microscopy based …
Minimol: A parameter-efficient foundation model for molecular learning
K Klaser, B Banaszewski… - ICML 2024 Workshop …, 2024 - openreview.net
We propose MiniMol, an open-source foundation model for molecular machine learning
which outperforms the best previous foundation model on 17/22 downstream tasks from the …
which outperforms the best previous foundation model on 17/22 downstream tasks from the …
MXene Property Prediction via Graph Contrastive Learning
EW Vertina, E Sutherland, NA Deskins… - 2024 IEEE 14th …, 2024 - ieeexplore.ieee.org
MXenes are an important class of 2-D materials with expected novel properties and myriad
applications, including efficient energy conversion in batteries and solar cells, environmental …
applications, including efficient energy conversion in batteries and solar cells, environmental …
Molphenix: A Multimodal Foundation Model for PhenoMolecular Retrieval
Predicting molecular impact on cellular function is a core challenge in therapeutic design.
Phenomic experiments, designed to capture cellular morphology, utilize microscopy based …
Phenomic experiments, designed to capture cellular morphology, utilize microscopy based …