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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.
Meaningful machine learning models and machine-learned pharmacophores from fragment screening campaigns
In this study we derive ML models from over 50 fragment-screening campaigns to introduce two important elements that we believe are absent in most -- if not all -- ML studies of this type reported to date: First, alongside the observed hits we use to train our models, we incorporate true misses and show that these experimentally validated negative data are of significant importance to the quality of the derived models.
