During the last decade non-linear machine-learning methods have gained popularity among QSAR modelers. The machine-learning algorithms generate highly accurate models at a cost of increased model complexity where simple interpretations, valid in the entire model domain, are rare. This thesis focuses on maximizing the amount of extracted knowledge from predictive QSAR models and data. This has been achieved by the development of a descriptor importance measure, a method for automated local optimization of compounds and a method for automated extraction of substructural alerts. Furthermore different QSAR modeling strategies have been evaluated with respect to predictivity, risks and information content. To test hypotheses and theories large sc...
Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis o...
We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study...
We explore two avenues where machine learning can help drug discovery: predictive models of in vivo ...
QSAR (quantitative structure-activity relationship) modeling is one of the well developed areas in d...
Summary: Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory ...
Quantitative Structure Activity Relationship (QSAR) is a very useful computa-tional method which has...
Quantitative Structure Activity Relationship (QSAR) is a very useful computa-tional method which has...
This paper presents work in progress from theINFUSIS project and contains initial experimentation, u...
Quantitative Structure-Activity Relationship (QSAR) is a method that relates the chemical compositio...
Quantitative Structure‐Activity Relationship (QSAR) models have been successfully applied to lead op...
Quantitative Structure‐Activity Relationship (QSAR) models have been successfully applied to lead op...
State-of-the-art quantitative structure–activity relationship (QSAR) models are often based on nonli...
Prediction of chemical bioactivity and physical properties has been one of the most important applic...
In silico bioactivity prediction studies are designed to complement in vivo and in vitro efforts to ...
The work described here is aimed at developing QSAR models capable of predicting in vitro human plas...
Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis o...
We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study...
We explore two avenues where machine learning can help drug discovery: predictive models of in vivo ...
QSAR (quantitative structure-activity relationship) modeling is one of the well developed areas in d...
Summary: Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory ...
Quantitative Structure Activity Relationship (QSAR) is a very useful computa-tional method which has...
Quantitative Structure Activity Relationship (QSAR) is a very useful computa-tional method which has...
This paper presents work in progress from theINFUSIS project and contains initial experimentation, u...
Quantitative Structure-Activity Relationship (QSAR) is a method that relates the chemical compositio...
Quantitative Structure‐Activity Relationship (QSAR) models have been successfully applied to lead op...
Quantitative Structure‐Activity Relationship (QSAR) models have been successfully applied to lead op...
State-of-the-art quantitative structure–activity relationship (QSAR) models are often based on nonli...
Prediction of chemical bioactivity and physical properties has been one of the most important applic...
In silico bioactivity prediction studies are designed to complement in vivo and in vitro efforts to ...
The work described here is aimed at developing QSAR models capable of predicting in vitro human plas...
Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis o...
We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study...
We explore two avenues where machine learning can help drug discovery: predictive models of in vivo ...