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Scaffold hopping-based exploration of chemical space
Mikeš, Marek ; Hoksza, David (advisor) ; Krivák, Radoslav (referee)
This work is based on the Molpher SW project, which is client-server application aiding exploration of chemical space between two input molecules. Aim of master thesis was modify the current version of program to manage scaffold hopping technique. This technique represents molecule in a simplified way. The simpler molecule is called scaffold. First of all there was need to define seve- ral levels of granularity and for each level define morphing operators. Server was modified with respect for parallelization. Experimental exploration of chemical space with and without the new feature is part of this work too. Powered by TCPDF (www.tcpdf.org)
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Modeling of fragment-based molecular similarity
Lamprecht, Matyáš ; Škoda, Petr (advisor) ; Mráz, František (referee)
Virtual screening is a part of computer-aided drug design, which aims to identify biologically active molecules. The ligand-based virtual screening employs known bio- logically active molecules and similarity search. A common approach to computation of molecular similarity is to utilize molecular fingerprints. Hashed structural molecular fingerprints hash fragments (subgraphs) of molecular graphs into a bit string reducing the problem of molecular similarity to the bit string similarity. Due to the hashing two distinct fragments may collide, which causes information loss. For this reason collisions are considered unwanted and they are generally believed to decrease a performance. Our goal was, contrary to the general believe, test whether collisions can have positive impact on the performance. For this purpose we designed several similarity models based on fragments. In order to make testing and evaluation easy we implemented testing environ- ment. Results of our experiments prove that some collisions can outperform commonly used methods. Moreover some collisions in a specific model can lead to a performance of AUC over 0.99. 1
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Utilization of latent semantic analysis in virtual screening
Kolář, Jiří ; Hoksza, David (advisor) ; Škoda, Petr (referee)
Title: Utilization of latent semantic analysis in virtual screening Author: Jiří Kolář Department: Department of Software Engineering Supervisor: RNDr. David Hoksza, Ph.D., Department of Software Engineering Abstract: Aim of this thesis is to investigate utilisation of latent semantic in- dexing in Virtual screening. We have examined existing VS method called lat- ent semantic structural indexing (LaSSI) and compared performance of different structural fingerprints. Additionally, we have developed a new model that com- pare fragments of molecules by usage of latent semantic indexing. Fragments are characterized by formula based counts and descriptors describing the physi- cochemical properties. Results of our methods are compared to VS techniques using directly standard fingerprints. Keywords: virtual screening cheminformatics ligand-based fingerprints ECFP TT latent semantic analysis LaSSI iii
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Využití simulovaného žíhání pro optimalizaci molekulárních otisků ve virtuálním screeningu
Filandr, Adam ; Hoksza, David (advisor) ; Kratochvíl, Miroslav (referee)
Ligand based virtual screening can be realised with various molecular rep- resentations. Fragment-feature representation represents the molecules as a set of fragments, where each fragment receives a set of descriptors. First goal of this thesis is to find suitable similarity function for such represen- tation. This representation can also be improved by assigning a weight for each descriptor, which gives it a priority in a given similarity function. The second goal of this thesis is to examine simulated annealing as an algorithm used to find the weights. We experimentally analysed the influence of various fragment types, descriptor types, similarity functions, correlated descriptors, fragment noise and parameters of simulated annealing. Because the experi- ments are computationally demanding, we also created a tool for large scale computations. 1
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Scaffold hopping-based exploration of chemical space
Mikeš, Marek ; Hoksza, David (advisor) ; Krivák, Radoslav (referee)
This work is based on the Molpher SW project, which is client-server application aiding exploration of chemical space between two input molecules. Aim of master thesis was modify the current version of program to manage scaffold hopping technique. This technique represents molecule in a simplified way. The simpler molecule is called scaffold. First of all there was need to define seve- ral levels of granularity and for each level define morphing operators. Server was modified with respect for parallelization. Experimental exploration of chemical space with and without the new feature is part of this work too. Powered by TCPDF (www.tcpdf.org)
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Machine learning-based identification of separating features in molecular fragments
Ravi, Aakash ; Hoksza, David (advisor) ; Škoda, Petr (referee)
Chosen molecular representation is one of the key parameters of virtual screening campaigns where one is searching in-silico for active molecules with respect to given macromolecular target. Most campaigns employ a molecular representation in which a molecule is represented by the presence or absence of a predefined set of topological fragments. Often, this information is enriched by physiochemical features of these fragments: i.e. the representation distinguishes fragments with identical topology, but different features. Given molecular representation, however, most approaches always use the same static set of features irrespective of the specific target. The goal of this thesis is, given a set of known active and inactive molecules with respect to a target, to study the possibilities of parameterization of a fragment-based molecular representation with feature weights dependent on the given target. In this setting, we are given a very general molecular representation, with targets represented by sets of known active and inactive molecules. We subsequently propose a machine-learning approach that would identify which of the features are relevant for the given target. This will be done using a multi-stage pipeline that includes data preprocessing using statistical imputation and dimensionality...
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