Message-ID: <1129825205.143.1632427496755.JavaMail.bigchem@cpu> Subject: Exported From Confluence MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_Part_142_410164592.1632427496755" ------=_Part_142_410164592.1632427496755 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Content-Location: file:///C:/exported.html Comprehensive modeling with multiple models

Comprehensive modeling with multiple models

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:

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:

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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