Abstract
Ligand-based predictive modeling is widely used to generate predictive models aiding decision making in e.g. drug discovery projects. With growing data sets and requirements on low modeling time comes the necessity to analyze data sets efficiently to support rapid and robust modeling. In this study we analyzed four data sets and studied the efficiency of machine learning methods on sparse data structures, utilizing Morgan fingerprints of different radii and hash sizes, and compared with molecular signatures descriptor of different height. We specifically evaluated the effect these parameters had on modeling time, predictive performance, and memory requirements using two implementations of random forest; Scikit-learn as well as FEST. We also compared with a support vector machine implementation. Our results showed that unhashed fingerprints yield significantly better accuracy than hashed fingerprints (p le 0.05), with no pronounced deterioration in modeling time and memory usage. Furthermore, the fast execution and low memory usage of the FEST algorithm suggest that it is a good alternative for large, high dimensional sparse data. Both support vector machines and random forest performed equally well but results indicate that the support vector machine was better at using the extra information from larger values of the Morgan fingerprint’s radius.
Highlights
Ligand-based modelling is a widely used method where the ligand’s activity for a biological target, usually a measurement obtained from a bioassay, can be correlated to certain features of the ligand
Machine learning (ML) algorithms is an important component in structure–activity modelling and analysis of compounds, and an example of a widely used method is support vector machines (SVMs)
There are mainly two parameters to be set for the generation of the Morgan fingerprint: (1) bit size—the length of the bit string for the molecular features to be contained in; (2) radius—the number of neighbours × bond lengths away to take into account when calculating the identifiers of the atoms
Summary
Ligand-based modelling is a widely used method where the ligand’s activity for a biological target, usually a measurement obtained from a bioassay, can be correlated to certain features of the ligand. Machine learning (ML) algorithms is an important component in structure–activity modelling and analysis of compounds, and an example of a widely used method is support vector machines (SVMs). This method has proven to be successful for correlating molecular structures to toxicity and activity of compounds [3, 4, 11, 12]. SVMs have shown to be useful for drug transport predictions [13] and to model and study interactions of antibiotic compounds [14] Another important and successful machine learning method is the random
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