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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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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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