Support vector machines are a popular machine learning method for many classification tasks in biology and chemistry. In addition, the support vector regression (SVR) variant is widely used for numerical property predictions. In chemoinformatics and pharmaceutical research, SVR has become the probably most popular approach for modeling of non-linear structure-activity relationships (SARs) and predicting compound potency values. Herein, we have systematically generated and analyzed SVR prediction models for a variety of compound data sets with different SAR characteristics. Although these SVR models were accurate on the basis of global prediction statistics and not prone to overfitting, they were found to consistently mispredict highly poten...
9-21<span style="font-size:11.0pt;font-family: " times="" new="" roman";mso-fareast-font-family:"ti...
Background: In drug discovery morphological profiles can be used to identify and establish a drug's ...
Background: In drug discovery morphological profiles can be used to identify and establish a drug's ...
In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among...
Support vector regression (SVR) is a premier approach for the prediction of compound potency. Given ...
In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among...
Activity cliffs (ACs) are formed by structurally similar compounds with large differences in activit...
Support vector machine (SVM) modeling is one of the most popular machine learning approaches in chem...
Active compounds can participate in different local structure–activity relationship (SAR) environmen...
Activity cliffs are formed by structurally similar or analogous compounds having large potency diffe...
In ligand-based drug design, quantitative structure–activity relationship (QSAR) models play an impo...
Structure Activity Relationship (SAR) modelling capitalises on techniques developed within the compu...
Activity landscapes (ALs) integrate structural and potency data of active compounds and provide grap...
Quantitative structure-activity relationship (QSAR) models are mathematical equations constructing a...
The use of machine learning methods for the prediction of reaction yield is an emerging area. We dem...
9-21<span style="font-size:11.0pt;font-family: " times="" new="" roman";mso-fareast-font-family:"ti...
Background: In drug discovery morphological profiles can be used to identify and establish a drug's ...
Background: In drug discovery morphological profiles can be used to identify and establish a drug's ...
In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among...
Support vector regression (SVR) is a premier approach for the prediction of compound potency. Given ...
In computational chemistry and chemoinformatics, the support vector machine (SVM) algorithm is among...
Activity cliffs (ACs) are formed by structurally similar compounds with large differences in activit...
Support vector machine (SVM) modeling is one of the most popular machine learning approaches in chem...
Active compounds can participate in different local structure–activity relationship (SAR) environmen...
Activity cliffs are formed by structurally similar or analogous compounds having large potency diffe...
In ligand-based drug design, quantitative structure–activity relationship (QSAR) models play an impo...
Structure Activity Relationship (SAR) modelling capitalises on techniques developed within the compu...
Activity landscapes (ALs) integrate structural and potency data of active compounds and provide grap...
Quantitative structure-activity relationship (QSAR) models are mathematical equations constructing a...
The use of machine learning methods for the prediction of reaction yield is an emerging area. We dem...
9-21<span style="font-size:11.0pt;font-family: " times="" new="" roman";mso-fareast-font-family:"ti...
Background: In drug discovery morphological profiles can be used to identify and establish a drug's ...
Background: In drug discovery morphological profiles can be used to identify and establish a drug's ...