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Found 5 papers, 1 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.
Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning
The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria.
Towards Interpretable Molecular Graph Representation Learning
Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks.
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.
