Search Results for author:
Found 11 papers, 5 papers with code
Role of Structural and Conformational Diversity for Machine Learning Potentials
In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically conformational and structural diversity, and model generalization is critical in improving the quality of Quantum Mechanics (QM) data generation efforts.
Gotta be SAFE: A New Framework for Molecular Design
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
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
3D Infomax improves GNNs for Molecular Property Prediction
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.
3D Pre-training improves GNNs for Molecular Property Prediction
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.
Rethinking Graph Transformers with Spectral Attention
Here, we present the $\textit{Spectral Attention Network}$ (SAN), which uses a learned positional encoding (LPE) that can take advantage of the full Laplacian spectrum to learn the position of each node in a given graph.
Geodesics in fibered latent spaces: A geometric approach to learning correspondences between conditions
This work introduces a geometric framework and a novel network architecture for creating correspondences between samples of different conditions.
MODELLING BIOLOGICAL ASSAYS WITH ADAPTIVE DEEP KERNEL LEARNING
Due to the significant costs of data generation, many prediction tasks within drug discovery are by nature few-shot regression (FSR) problems, including accurate modelling of biological assays.
Towards Interpretable Molecular Graph Representation Learning
Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks.
Adaptive Deep Kernel Learning
Deep kernel learning provides an elegant and principled framework for combining the structural properties of deep learning algorithms with the flexibility of kernel methods.
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling
We benchmark LaPool on molecular graph prediction and understanding tasks and show that it outperforms recent GNNs.
