Publications by authors named "D Svozil"

The ability to correctly identify Potential Key Fish Habitats (PKFH) before undertaking development assessments or stream restoration projects is a critical step for determining appropriate management interventions and offset strategies to minimize damage to all life stages of fish. However, there are no rapid, low-cost tools that can be universally utilised to identify PKFH in perennial streams and ephemeral streams in particular. We propose a conceptual framework for developing a rapid field appraisal based on a range of physical fish habitat-supporting features.

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Computational exploration of chemical space is crucial in modern cheminformatics research for accelerating the discovery of new biologically active compounds. In this study, we present a detailed analysis of the chemical library of potential glucocorticoid receptor (GR) ligands generated by the molecular generator, Molpher. To generate the targeted GR library and construct the classification models, structures from the ChEMBL database as well as from the internal IMG library, which was experimentally screened for biological activity in the primary luciferase reporter cell assay, were utilized.

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Schizophrenia is a serious mental disorder without a fully understood pathomechanism, but which involves dysregulation of neurotransmitters and their receptors. The best option for the management of schizophrenia comprises so-called multi-target ligands, similar to the third generation of neuroleptics. Dopamine type 2 receptors (DRs) are the main target in the treatment of schizophrenia, in particular for mitigation of the positive symptoms.

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Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains one of the main routes for therapeutic antibody development. Traditionally, humanization is manual, laborious, and requires expert knowledge. Although automation efforts are advancing, existing methods are either demonstrated on a small scale or are entirely proprietary.

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Many contemporary cheminformatics methods, including computer-aided de novo drug design, hold promise to significantly accelerate and reduce the cost of drug discovery. Thanks to this attractive outlook, the field has thrived and in the past few years has seen an especially significant growth, mainly due to the emergence of novel methods based on deep neural networks. This growth is also apparent in the development of novel de novo drug design methods with many new generative algorithms now available.

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