Most approaches to structure-activity-relationship (SAR) prediction proceed in two steps. In the first step, a typically large set of fingerprints, or fragments of interest, is constructed (either by hand or by some recent data mining techniques). In the second step, machine learning techniques are applied to obtain a predictive model. The result is often not only a highly accurate but also hard to interpret model. In this paper, we demonstrate the capabilities of a novel SAR algorithm, SMIREP, which tightly integrates the fragment and model generation steps and which yields simple models in the form of a small set of IF-THEN rules. These rules contain SMILES fragments, which are easy to understand to the computational chemist. SMIREP combi...
Small organic molecules, by binding to different proteins, can be used to modulate (inhibit/activate...
The ever-increasing number of chemical compounds added every year has not been accompanied by a simi...
An area of ongoing concern in toxicology and chemical risk assessment is endocrine disrupting chemic...
Most approaches to structure-activity-relationship (SAR) prediction proceed in two steps. In the fir...
Graph mining approaches are extremely popular and effective in molecular databases. The vast majorit...
Recognizing substructures and their relations embedded in a molecular structure representation is a ...
Abstract. Graph mining approaches are extremely popular and effective in molecular databases. The va...
Structure Activity Relationship (SAR) modelling capitalises on techniques developed within the compu...
Understanding the structure–activity relationships (SARs) of small molecules is important for develo...
Toxicity testing of chemicals is currently undergoing its largest ever paradigm shift, moving toward...
Knowledge of mixtures' phase equilibria is crucial in nature and technical chemistry. Phase equilibr...
This paper explores the utility of data mining and machine learning algorithms for the induction of ...
There is a pressing need for accurate in silico methods to predict the toxicity of molecules that ar...
Recent availability of large publicly accessible databases of chemical compounds and their biologica...
Activity cliffs (ACs) are formed by structurally similar compounds with large differences in activit...
Small organic molecules, by binding to different proteins, can be used to modulate (inhibit/activate...
The ever-increasing number of chemical compounds added every year has not been accompanied by a simi...
An area of ongoing concern in toxicology and chemical risk assessment is endocrine disrupting chemic...
Most approaches to structure-activity-relationship (SAR) prediction proceed in two steps. In the fir...
Graph mining approaches are extremely popular and effective in molecular databases. The vast majorit...
Recognizing substructures and their relations embedded in a molecular structure representation is a ...
Abstract. Graph mining approaches are extremely popular and effective in molecular databases. The va...
Structure Activity Relationship (SAR) modelling capitalises on techniques developed within the compu...
Understanding the structure–activity relationships (SARs) of small molecules is important for develo...
Toxicity testing of chemicals is currently undergoing its largest ever paradigm shift, moving toward...
Knowledge of mixtures' phase equilibria is crucial in nature and technical chemistry. Phase equilibr...
This paper explores the utility of data mining and machine learning algorithms for the induction of ...
There is a pressing need for accurate in silico methods to predict the toxicity of molecules that ar...
Recent availability of large publicly accessible databases of chemical compounds and their biologica...
Activity cliffs (ACs) are formed by structurally similar compounds with large differences in activit...
Small organic molecules, by binding to different proteins, can be used to modulate (inhibit/activate...
The ever-increasing number of chemical compounds added every year has not been accompanied by a simi...
An area of ongoing concern in toxicology and chemical risk assessment is endocrine disrupting chemic...