In-silico Predictive Mutagenicity Model Generation Using Supervised Learning Approaches

With the advent of High Throughput Screening techniques, it is feasible to filter possible leads from a mammoth chemical space that can act against a particular target and inhibit its action. Virtual screening complements the in-vitro assays which are costly and time consuming. This process is used to sort biologically active molecules by utilizing the structural and chemical information of the compounds and the target proteins in order to screen potential hits. Various data mining and machine learning tools utilize Molecular Descriptors through the knowledge discovery process using classifier algorithms that classify the potentially active hits for the drug development process.
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Development of a Microfluidics-Based Screening Assay for the High-Throughput Directed Evolution of Artificial Metalloenzymes

The present PhD thesis summarizes the scientific work conducted in the research group of Prof. Dr. Thomas R. Ward during the years 2016–2021. Research in the Ward group is focused on the development and optimization of artificial metalloenzymes with non-natural activities. These hybrid catalysts, resulting from an incorporation of a metal–containing cofactor within a protein or DNA scaffold, and can be optimized by either chemical or genetic means. The main part of this thesis deals with the genetic optimization of such systems and the development of higher throughput screening assays to facilitate the process. First attempts dealt with the development of a selection-based assay relying on the Carroll rearrangement (Chapter 2.6). Following, more high-throughput assays such as screening of cells relying on a fluorescent reporter protein (Chapter 3) or the screening of activity by an agar plate screening assay were pursued (Chapter 4.2). The main part of the thesis focuses on the method development of an ultrahigh-throughput screening platform for the in vivo directed evolution of artificial metalloenzymes using droplet microfluidics. The combination of ArMs and droplet microfluidics, can be a powerful tool for propelling directed evolution-based research forward. Systematic and high-throughput screening of ArMs in vivo using double emulsions could allow the screening of a much bigger sequence space, which is, to date, challenging. Identifying cooperative effects to improve catalysis or even remodelling whole enzymes to achieve new-to-nature reactivities are only two potential examples. Reactions based on ArMs could ultimately provide aqueous, environmentally friendly reaction pathways for industrial applications. Additionally, such big data sets could also be used as an input for machine learning applications, to further study active site plasticity, reaction pathways, or even protein-folding mechanisms. The developed method was then applied to libraries of different types and sizes, and recent findings of these screenings are highlighted in the fourth chapter. During the time in the research group of Prof. Dr. Ward, a deeper knowledge in molecular biology, especially library design, high-throughput screening using different approaches, microfluidic method development and fluorescence activated cell sorting (FACS), and the use of different sequencing techniques was garnered

MMsPred: a bioactivity and toxicology predictive system

In the last decade, the development and use of new methods in combinatorial chemistry and high-throughput screening has dramatically increased the number of known biologically active compounds. Paradoxically, the number of drugs reaching the market has not followed the same trend, often because many of the candidate drugs present poor qualities in absorption, distribution, metabolism, excretion, and toxicological properties (ADME-Tox). The ability to recognize and discard bad candidates early in the drug discovery steps would save lost investments in time and money. Machine learning techniques could provide solutions to this problem.
The goal of my research is to develop classifiers that accurately discriminate between active and inactive molecules for a specific target. To this end, I am comparing the effectiveness of the application of different machine learning techniques to this problem.	As a source of data we have selected a set of PubChem's public BioAssays1. In addition, with the objective of realizing a real-time query service with our predictors, we aim to keep the features describing the chemical compounds relatively simple.
At the end of this process, we should better understand how to build statistical models that are able to recognize molecules active in a specific bioassay, including how to select the most appropriate classification technique, and how to describe compounds in such a way that is not excessively resource-consuming to generate, yet contains sufficient information for the classification. We see immediate applications of such technology to recognize compounds with high-risk of toxicity, and also to suggest likely metabolic pathways that would process it

Iterative focused screening with biological fingerprints identifies selective Asc-1 inhibitors distinct from traditional high throughput screening

N-methyl-d-aspartate receptors (NMDARs) mediate glutamatergic signaling that is critical to cognitive processes in the central nervous system, and NMDAR hypofunction is thought to contribute to cognitive impairment observed in both schizophrenia and Alzheimer’s disease. One approach to enhance the function of NMDAR is to increase the concentration of an NMDAR coagonist, such as glycine or d-serine, in the synaptic cleft. Inhibition of alanine–serine–cysteine transporter-1 (Asc-1), the primary transporter of d-serine, is attractive because the transporter is localized to neurons in brain regions critical to cognitive function, including the hippocampus and cortical layers III and IV, and is colocalized with d-serine and NMDARs. To identify novel Asc-1 inhibitors, two different screening approaches were performed with whole-cell amino acid uptake in heterologous cells stably expressing human Asc-1: (1) a high-throughput screen (HTS) of 3 M compounds measuring 35S l-cysteine uptake into cells attached to scintillation proximity assay beads in a 1536 well format and (2) an iterative focused screen (IFS) of a 45 000 compound diversity set using a 3H d-serine uptake assay with a liquid scintillation plate reader in a 384 well format. Critically important for both screening approaches was the implementation of counter screens to remove nonspecific inhibitors of radioactive amino acid uptake. Furthermore, a 15 000 compound expansion step incorporating both on- and off-target data into chemical and biological fingerprint-based models for selection of additional hits enabled the identification of novel Asc-1-selective chemical matter from the IFS that was not identified in the full-collection HTS

Visual and computational analysis of structure-activity relationships in high-throughput screening data

Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets