Machine learning techniques applied to chemotaxonomy and ligand-based virtual screening
Year: 2007
Type: dissertation
Abstract: This work demonstrated the applicability of different machine learning techniques for extracting knowledge from chemical databases of different size. Two different fields – chemotaxonomy and ligand-based virtual screening were studied. The former demonstrated how a relatively small chemical data set can be coupled with different machine learning techniques in a way, which allows us to better our understanding of the relationships between plants’ secondary metabolism and their taxonomic classification. The ligand-based virtual screening demonstrated how the large amount of chemical data stored across different large chemical databases can be used in a knowledge-driven way for the discovery of new potential drugs. Chapter 2 presented the application of different classification techniques to the assignment of sesquiterpene lactones – important secondary metabolites in the plant family Asteraceae – to the Asteraceae tribe from which they have been isolated. The performance of different machine learning techniques was investigated. Good agreement with the taxonomic division proposed by Bremer was obtained. In addition, the problem of the applicability domain of the built models was investigated and some practical guidance was given. Chapter 3 extended the study presented in Chapter 2. The simultaneous occurrence of secondary metabolites in different taxa was taken into account. A machine learning area, known as multi-labeled classification, was introduced in an attempt to model the reality as closely as possible. With this approach interesting relationships between the studied Asteraceae tribes were discovered. In addition, the practical application of the built classification models to targeted collection of plants with the aim of finding natural products with desired properties was shown. Chapter 4 demonstrated how machine learning techniques can help in the navigation of large chemical spaces. A new approach to ligand-based similarity searching based on a machine learning technique known as novelty detection was described. Its applicability for the knowledge-driven selection of chemical compounds with potential biological activity has been demonstrated comparative to the most common ligand-based virtual screening approach – similarity searching. Chapter 5 extended the work described in Chapter 4 to more concrete practical scenarios. Four such scenarios: prioritizing compounds for a subsequent high-throughput screening experiment; selecting compounds for a subsequent lead-optimization, assessing the probability that a given structure will exhibit a particular biological activity, and the identification of the most active structure were examined. The applicability of different ligand-based virtual screening methods and chemical structure representations in each scenario was tested. Different measures for the success of the virtual screening experiment in each scenario were presented and discussed. The optimal size of the training set, the difference in the chemical spaces covered by two large databases of biologically active compounds, the bias introduced by the training set selection, the differences in the compounds recovered by different methods or/and descriptors were discussed and the best method-descriptor combination was identified for each scenario. The findings of this work can be used as guidance for future studies, including investigations in both chemotaxonomy and ligand-based virtual screening, as well as in other chemistry related areas. Concerning chemotaxonomy, a comparative study using the existing different taxonomic divisions of Asteraceae will no doubt discover new interesting relationships between Asteraceae plant species. With regards to ligand-based virtual screening the investigation of other novelty detection techniques and a study, which accounts for the conformational flexibility of the ligands, will be valuable. On the other hand, the definition of the applicability domain of a machine learning model, discussed in Chapter 2, is of benefit for any machine learning method which is used with predictive purposes. The multi-labeled classification, presented in Chapter 3, may benefit other chemoinformatics fields – like, for example, predicting multi-target drugs. The novelty detection technique presented and discussed in Chapter 4 and Chapter 5 offers an alternative for any case where information about only one of the possible states of a given (chemical) system is known. As such, it may help in discovering knowledge from data in various situations where the classic classification algorithms are not applicable. The studies presented in this work have shown the applicability of machine learning techniques to different chemistry related problems. We have demonstrated how, with the help of different machine learning techniques, knowledge can be gathered from both small and large chemical databases. This knowledge is of great value in the modern, data-rich world.
Author: Dimitar Hristozov
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2189471110