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Quantitative structure-activity relationship and machine learning
Nierostek, Jakub ; Uhlík, Filip (advisor) ; Svozil, Daniel (referee)
Quantitative structure-activity relationship (QSAR) computational methods allow us to examine the relationship between the chemical structure of molecules and their chemical or biological properties. For QSAR calculations, widely used machine learning methods, such as deep learning models, can be used. In this work, we construct a pipeline for training QSAR machine-learning models that can predict molecular toxicity. Furthermore, we investigate the effect of molecular representation on model performance. Both our deep learning mod- els and traditional machine learning models are employed on Tox21 and Ames Mutagenicity datasets. Their performance is evaluated against recently published models for toxicity prediction using the AUC-ROC metric and, regarding certain toxicity targets, shows improvement over these models. Keywords: QSAR, machine learning, deep learning, molecular descriptors 1
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DEBRA - Derscriptor Barn
Le, Khanh Chuong ; Hoksza, David (advisor) ; Jančík, Pavel (referee)
One of the key tasks of cheminformatics is representation of chemical compounds. In this thesis we develop a client server software that can generate large amount of such representations. The server allows adding plugins which exploit third party applications. These applications are launched via a command line interface, or calling methods of a web service. The client displays the possible representation types. The user will choose a set of representations and an input set of molecules and this query sends to the server. The server then calls plugins and return a result. Powered by TCPDF (www.tcpdf.org)
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DEBRA - Derscriptor Barn
Le, Khanh Chuong ; Hoksza, David (advisor) ; Jančík, Pavel (referee)
One of the key tasks of cheminformatics is representation of chemical compounds. In this thesis we develop a client server software that can generate large amount of such representations. The server allows adding plugins which exploit third party applications. These applications are launched via a command line interface, or calling methods of a web service. The client displays the possible representation types. The user will choose a set of representations and an input set of molecules and this query sends to the server. The server then calls plugins and return a result. Powered by TCPDF (www.tcpdf.org)
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