In silico drug discovery of Acetylcholinesterase and Butyrylcholinesterase enzymes inhibitors based on Quantitative Structure-Activity Relationship (QSAR) and drug-likeness evaluation

2020 ◽  
pp. 129845
Author(s):  
Nour-El-Houda Hammoudi ◽  
Widad Sobhi ◽  
Ayoub Attoui ◽  
Tarek Lemaoui ◽  
Alessandro Erto ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity

scholarly journals Imputation versus prediction: applications in machine learning for drug discovery

2020 ◽  
Vol 2 (2) ◽  
pp. FDD38 ◽  
Author(s):  
Benedict W J Irwin ◽  
Samar Mahmoud ◽  
Thomas M Whitehead ◽  
Gareth J Conduit ◽  
Matthew D Segall
Keyword(s):  
Drug Discovery ◽  
Missing Values ◽  
Value Added ◽  
Predictive Modelling ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Modelling Techniques

Imputation is a powerful statistical method that is distinct from the predictive modelling techniques more commonly used in drug discovery. Imputation uses sparse experimental data in an incomplete dataset to predict missing values by leveraging correlations between experimental assays. This contrasts with quantitative structure–activity relationship methods that use only descriptor – assay correlations. We summarize three recent imputation strategies – heterogeneous deep imputation, assay profile methods and matrix factorization – and compare these with quantitative structure–activity relationship methods, including deep learning, in drug discovery settings. We comment on the value added by imputation methods when used in an ongoing project and find that imputation produces stronger models, earlier in the project, over activity and absorption, distribution, metabolism and elimination end points.

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