Message-ID: <1129825205.143.1632427496755.JavaMail.bigchem@cpu>
Subject: Exported From Confluence
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OCHEM allows creating multiple models with different configurati=
on options. Hundreds of models can be created with a few clicks.
The u=
tility for multiple models creation and management is referred to as "=
comprehensive modeling" utility.
Creation of multiple models
To access the comprehensive modelling dialogue, select the menu "Mo=
dels > Create multiple models".
Select the necessary training and validation sets.
Select the predefined configuration templates for:
- molecular descriptors
- machine learning methods
- descriptors filtering
- validation protocols
Each combination of the selected templates will be used, therefore, if y=
ou select 2 types of descriptors, 3 machine learning methods, 1 descriptor =
filtering options and 2 validation options, then 2x3x1x2 =3D 12 mod=
els will be created.

Wait until the models have been started:

M=
ultiple models overview
The multiple models overview page will display the=
status of all the models, both running and completed:

When the models are ready, we fetch the statistics by clicking the appro=
priate lick and overview the predictive performance of the models.
The=
multiple models overview allows to compare models by:
- RMSE
- R2
- Mean absolute error (MAE)
It is possible to compare the models by their performances for both trai=
ning and validation sets.
The screenshot below displays RMSE values for 9 models developed for BCF=
(bio-concentration factor) endpoint based on the "BCF train" basket.

Comprehensive modeling can be a very powerful feature. Which descriptors=
are the best? How do the models evolve when outliers are excluded? Which t=
raining method performs best for this property? All this questions require =
deep analysis made possible using comprehensive modeling.
The screenshot below shows an intermediate result of a real on-going stu=
dy =E2=80=93 prediction of melting point based on more tha=
n 30,000 experimental measurements. More than 150 =
models have been built. Using the comprehensive modeling feature, =
it was possible to identify the best methods and to gradually improve model=
s by removing outliers and reducing noise.

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