389 citations · 673 across the 5 of their papers we have counts for
5 papers
PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences
Martin Buttenschoen, Garrett M. Morris, Charlotte M. Deane
The last few years have seen the development of numerous deep learning-based protein-ligand docking methods. They offer huge promise in terms of speed and accuracy. However, despit…
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
Leo Klarner, Tim G. J. Rudner, Michael Reutlinger +4
Accelerating the discovery of novel and more effective therapeutics is an important pharmaceutical problem in which deep learning is playing an increasingly significant role. Howev…
Exploring QSAR Models for Activity-Cliff Prediction
Markus Dablander, Thierry Hanser, Renaud Lambiotte +1
Pairs of similar compounds that only differ by a small structural modification but exhibit a large difference in their binding affinity for a given target are known as activity cli…
The prospects of quantum computing in computational molecular biology
Carlos Outeiral, Martin Strahm, Jiye Shi +3
Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computa…
Investigating the potential for a limited quantum speedup on protein lattice problems
Carlos Outeiral, Garrett M. Morris, Jiye Shi +3
Protein folding is a central challenge in computational biology, with important applications in molecular biology, drug discovery and catalyst design. As a hard combinatorial optim…