Exploring QSAR models for activity-cliff prediction
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2023
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Online Access: | https://link.springer.com/article/10.1186/s13321-023-00708-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/8224 |
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oai:localhost:PNK-82242023-04-21T08:51:03Z Exploring QSAR models for activity-cliff prediction Markus, Dablander Thierry, Hanser Renaud, Lambiotte ACs QSAR models CC BY 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. 2023-04-21T08:51:03Z 2023-04-21T08:51:03Z 2023 Book https://link.springer.com/article/10.1186/s13321-023-00708-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/8224 en application/pdf Springer |
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Digital Phenikaa |
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Digital Phenikaa |
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English |
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ACs QSAR models |
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ACs QSAR models Markus, Dablander Thierry, Hanser Renaud, Lambiotte Exploring QSAR models for activity-cliff prediction |
description |
CC BY |
format |
Book |
author |
Markus, Dablander Thierry, Hanser Renaud, Lambiotte |
author_facet |
Markus, Dablander Thierry, Hanser Renaud, Lambiotte |
author_sort |
Markus, Dablander |
title |
Exploring QSAR models for activity-cliff prediction |
title_short |
Exploring QSAR models for activity-cliff prediction |
title_full |
Exploring QSAR models for activity-cliff prediction |
title_fullStr |
Exploring QSAR models for activity-cliff prediction |
title_full_unstemmed |
Exploring QSAR models for activity-cliff prediction |
title_sort |
exploring qsar models for activity-cliff prediction |
publisher |
Springer |
publishDate |
2023 |
url |
https://link.springer.com/article/10.1186/s13321-023-00708-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/8224 |
_version_ |
1763815060759642112 |
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8.891053 |