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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Vydáno v:Journal of cheminformatics Ročník 15; číslo 1; s. 4 - 11
Hlavní autoři: Tamura, Shunsuke, Miyao, Tomoyuki, Bajorath, Jürgen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 07.01.2023
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
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
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Shrnutí: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 predictions over the past decade. Different from typical compound classification tasks, AC predictions must be carried out at the level of compound pairs representing ACs or nonACs. Most AC predictions reported so far have focused on individual methods or comparisons of two or three approaches and only investigated a few compound activity classes (from 2 to 10). Although promising prediction accuracy has been reported in most cases, different system set-ups, AC definitions, methods, and calculation conditions were used, precluding direct comparisons of these studies. Therefore, we have carried out a large-scale AC prediction campaign across 100 activity classes comparing machine learning methods of greatly varying complexity, ranging from pair-based nearest neighbor classifiers and decision tree or kernel methods to deep neural networks. The results of our systematic predictions revealed the level of accuracy that can be expected for AC predictions across many different compound classes. In addition, prediction accuracy did not scale with methodological complexity but was significantly influenced by memorization of compounds shared by different ACs or nonACs. In many instances, limited training data were sufficient for building accurate models using different methods and there was no detectable advantage of deep learning over simpler approaches for AC prediction. On a global scale, support vector machine models performed best, by only small margins compared to others including simple nearest neighbor classifiers. Graphical Abstract
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ISSN:1758-2946
1758-2946
DOI:10.1186/s13321-022-00676-7