Exploring QSAR models for activity-cliff prediction
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Springer
2023
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| Truy cập trực tuyến: | 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 |
| institution |
Digital Phenikaa |
| collection |
Digital Phenikaa |
| language |
English |
| topic |
ACs QSAR models |
| spellingShingle |
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 |
| score |
8.887836 |