Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity

Activity cliffs (AC) are formed by pairs of structural analogues that are active against the same target but have a large difference in potency. While much of our knowledge about ACs has originated from the analysis and comparison of compounds and activity data, several studies have reported AC pred...

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Veröffentlicht in:	Journal of cheminformatics Jg. 15; H. 1; S. 4 - 11
Hauptverfasser:	Tamura, Shunsuke, Miyao, Tomoyuki, Bajorath, Jürgen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 07.01.2023 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accuracy Activity cliff Artificial neural networks Chemistry Chemistry and Materials Science Classifiers Cliffs Comparative analysis Complexity Compound pair-based prediction Computational Biology/Bioinformatics Computer Applications in Chemistry Decision trees Deep learning Documentation and Information in Chemistry Kernel functions Large-scale analysis Learning algorithms Machine learning Memory Methods Neural networks Predictions Support vector machines Theoretical and Computational Chemistry Deep learning Activity cliff Large-scale analysis Compound pair-based prediction Machine learning
ISSN:	1758-2946, 1758-2946
Online-Zugang:	Volltext
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