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Found 5 papers, 2 papers with code
MAGNet: Motif-Agnostic Generation of Molecules from Shapes
Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions.
The power of motifs as inductive bias for learning molecular distributions
Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic molecules.
Uncertainty Quantification for Atlas-Level Cell Type Transfer
Here, for the first time, we introduce uncertainty quantification methods for cell type classification on single-cell reference atlases.
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells.
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
Our approach is based on a novel technique to defend against adversarial evidence (i. e. faulty evidence due to artefacts) by filtering gradients during optimization.
