%0 DATA
%A Lewis, Mervin
%A Avid M., Afzal
%A Ola, Engkvist
%A Andreas, Bender
%D 2020
%T A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions
%U https://chemrxiv.org/articles/A_Comparison_of_Scaling_Methods_to_Obtain_Calibrated_Probabilities_of_Activity_for_Ligand-Target_Predictions/11526132
%R 10.26434/chemrxiv.11526132.v1
%2 https://chemrxiv.org/ndownloader/files/20697927
%K in silico target prediction
%K chemoinformatics
%K cheminformatics
%K QSAR Modeling
%K probability threshold
%K probability
%K probability calibration
%K probability scaling
%K venn abers
%K venn predictors
%K isotonic regression
%K platt scaling
%X In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into reliable probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance of three such methods, namely Platt Scaling, Isotonic Regression and Venn-ABERS in calibrating prediction scores for ligand-target prediction comprising the Naïve Bayes, Support Vector Machines and Random Forest algorithms with bioactivity data available at AstraZeneca (40 million data points (compound-target pairs) across 2112 targets). Performance was assessed using Stratified Shuffle Split (SSS) and Leave 20% of Scaffolds Out (L20SO) validation.