781 citations · 889 across the 8 of their papers we have counts for
8 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…
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…
BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale
Carl Poelking, Felix A. Faber, Bingqing Cheng
We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guid…
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…