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Found 6 papers, 2 papers with code
Autoregressive fragment-based diffusion for pocket-aware ligand design
In this work, we introduce AutoFragDiff, a fragment-based autoregressive diffusion model for generating 3D molecular structures conditioned on target protein structures.
Learning Melanocytic Cell Masks from Adjacent Stained Tissue
Melanoma is one of the most aggressive forms of skin cancer, causing a large proportion of skin cancer deaths.
A single-cell gene expression language model
Machine learning systems model natural language by explicitly learning context dependencies between words.
Robust Semantic Interpretability: Revisiting Concept Activation Vectors
Our proposed Robust Concept Activation Vectors (RCAV) quantifies the effects of semantic concepts on individual model predictions and on model behavior as a whole.
Attention-Based Learning on Molecular Ensembles
The three-dimensional shape and conformation of small-molecule ligands are critical for biomolecular recognition, yet encoding 3D geometry has not improved ligand-based virtual screening approaches.
Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias
In high-stakes applications of machine learning models, interpretability methods provide guarantees that models are right for the right reasons.
