6 citations · 13 across the 3 of their papers we have counts for
3 papers
Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions
Lucian Chan, Marcel Verdonk, Carl Poelking
Predicting the bioactivity of a ligand is one of the hardest and most important challenges in computer-aided drug discovery. Despite years of data collection and curation efforts b…
3D pride without 2D prejudice: Bias-controlled multi-level generative models for structure-based ligand design
Lucian Chan, Rajendra Kumar, Marcel Verdonk +1
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 improvin…
Meaningful machine learning models and machine-learned pharmacophores from fragment screening campaigns
Carl Poelking, Gianni Chessari, Christopher W. Murray +3
Machine learning (ML) is widely used in drug discovery to train models that predict protein-ligand binding. These models are of great value to medicinal chemists, in particular if…