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Found 4 papers, 1 papers with code
Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions
Predicting the bioactivity of a ligand is one of the hardest and most important challenges in computer-aided drug discovery.
3D pride without 2D prejudice: Bias-controlled multi-level generative models for structure-based ligand design
Generative models for structure-based molecular design hold significant promise for drug discovery, with the potential to speed up the hit-to-lead development cycle, while improving the quality of drug candidates and reducing costs.
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings
Current meta-learning approaches focus on learning functional representations of relationships between variables, i. e. on estimating conditional expectations in regression.
Hyperparameter Learning via Distributional Transfer
Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved.
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