781 citations · 881 across the 5 of their papers we have counts for
6 papers
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…
Chemical design rules for non-fullerene acceptors in organic solar cells
A. Markina, K. -H. Lin, W. Liu +14
Efficiencies of organic solar cells have practically doubled since the development of non-fullerene acceptors (NFAs). However, generic chemical design rules for donor-NFA combinati…
Investigating 3D Atomic Environments for Enhanced QSAR
William McCorkindale, Carl Poelking, Alpha A. Lee
Predicting bioactivity and physical properties of molecules is a longstanding challenge in drug design. Most approaches use molecular descriptors based on a 2D representation of mo…
Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filtering
Carl Poelking, Yehia Amar, Alexei Lapkin +1
Capturing the microscopic interactions that determine molecular reactivity poses a challenge across the physical sciences. Even a basic understanding of the underlying reaction mec…
Machine Learning Unifies the Modelling of Materials and Molecules
Albert P. Bartok, Sandip De, Carl Poelking +4
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modelling, underpinning our understanding of chemical and materials properties and trans…