most citedMeaningful machine learning models and machine-learned pharmacophores from fragment screening campaigns

6 citations · 10 across the 2 of their papers we have counts for

collaborators

2 papers

stat.ML2022★ 4 cited

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…

q-bio.BM2022★ 6 cited

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…