Introduction and methodology 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 cliffs (ACs). It has been hypothesised that QSAR models struggle to predict ACs and that ACs thus form a major source of prediction error. However, the AC-prediction power of modern QSAR methods and its quantitative relationship to general QSAR-prediction performance is still underexplored. We systematically construct nine distinct QSAR models by combining three molecular representation methods (extended-connectivity fingerprints, physicochemical-descriptor vectors and graph isomorphism networks) with three regression techniques (random forests, k-nearest neighbours and multilayer perceptrons); we then use each resulting model to classify pairs of similar compounds as ACs or non-ACs and to predict the activities of individual molecules in three case studies: dopamine receptor D2, factor Xa, and SARS-CoV-2 main protease. Results and conclusions Our results provide strong support for the hypothesis that indeed QSAR models frequently fail to predict ACs. We observe low AC-sensitivity amongst the evaluated models when the activities of both compounds are unknown, but a substantial increase in AC-sensitivity when the actual activity of one of the compounds is given. Graph isomorphism features are found to be competitive with or superior to classical molecular representations for AC-classification and can thus be employed as baseline AC-prediction models or simple compound-optimisation tools. For general QSAR-prediction, however, extended-connectivity fingerprints still consistently deliver the best performance amongs the tested input representations. A potential future pathway to improve QSAR-modelling performance might be the development of techniques to increase AC-sensitivity. Graphical Abstract

This report investigates various aspects of modelling a composite material using randomly packed cylinders in 3d space. In particular it considers: simulating configurations of non-intersecting cylinders; generating weighted networks from such simulations, including calculation of minimal cylinder separation distances; estimating the conductance or resistivity of such a network. This is based on work carried out during the ESGI165 Study Group at Durham University, April 2021.

This report investigates various aspects of modelling a composite material using randomly packed cylinders in 3d space. In particular it considers: simulating configurations of non-intersecting cylinders; generating weighted networks from such simulations, including calculation of minimal cylinder separation distances; estimating the conductance or resistivity of such a network. This is based on work carried out during the ESGI165 Study Group at Durham University, April 2021. Acknowledgement: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 764850 (SAGEX). This is paper SAGEX-21-25.