Kovats Index, a Rapid New Physical QSAR Predictor of Acute Toxicity of Benzene Derivatives to Fathead Minnows

Higher Educatio

Enhancing Reaction-based de novo Design using Machine Learning

De novo design is a branch of chemoinformatics that is concerned with the rational design of molecular structures with desired properties, which specifically aims at achieving suitable pharmacological and safety profiles when applied to drug design. Scoring, construction, and search methods are the main components that are exploited by de novo design programs to explore the chemical space to encourage the cost-effective design of new chemical entities. In particular, construction methods are concerned with providing strategies for compound generation to address issues such as drug-likeness and synthetic accessibility. Reaction-based de novo design consists of combining building blocks according to transformation rules that are extracted from collections of known reactions, intending to restrict the enumerated chemical space into a manageable number of synthetically accessible structures. The reaction vector is an example of a representation that encodes topological changes occurring in reactions, which has been integrated within a structure generation algorithm to increase the chances of generating molecules that are synthesisable. The general aim of this study was to enhance reaction-based de novo design by developing machine learning approaches that exploit publicly available data on reactions. A series of algorithms for reaction standardisation, fingerprinting, and reaction vector database validation were introduced and applied to generate new data on which the entirety of this work relies. First, these collections were applied to the validation of a new ligand-based design tool. The tool was then used in a case study to design compounds which were eventually synthesised using very similar procedures to those suggested by the structure generator. A reaction classification model and a novel hierarchical labelling system were then developed to introduce the possibility of applying transformations by class. The model was augmented with an algorithm for confidence estimation, and was used to classify two datasets from industry and the literature. Results from the classification suggest that the model can be used effectively to gain insights on the nature of reaction collections. Classified reactions were further processed to build a reaction class recommendation model capable of suggesting appropriate reaction classes to apply to molecules according to their fingerprints. The model was validated, then integrated within the reaction vector-based design framework, which was assessed on its performance against the baseline algorithm. Results from the de novo design experiments indicate that the use of the recommendation model leads to a higher synthetic accessibility and a more efficient management of computational resources

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Fragment based ligand discovery : library design and screening by thermal shift analysis

The central idea in Fragment Based Ligand Discovery (FBLD) is to identify small, low molecular weight compounds (MW < 250) that bind to a particular protein active site. Hits can be used to efficiently design larger compounds with the desired affinity and selectivity. Three approaches to FBLD are described in this thesis. The first topic is the development and assessment of different chemoinformatics procedures to select those fragments that maximally represent the chemical features of a larger compound library. Such a fragment library could be of great value in the so-called “SAR by Catalogue" approach, where the initial stage of fragment growth is by selecting existing compounds that contain sub-structures of the hit fragments. Five schemes implemented in the Pipeline Pilot software are described. The second project was to develop improved approaches to processing Thermal Shift Analysis (TSA) data. The shift in melting temperature can indicate that a ligand binds and thus stabilises a protein. A program, MTSA, has been written which allows more straightforward processing of the experimental data than existing available software. However, detailed analysis of fragment screening data highlighted difficulties in defining the melting temperature and suggest that TSA is not sufficiently reliable for routine screening use. Finally, a number of proteins were assessed experimentally for suitability for FBLD: N-myristoyl transferase (NMT), the bacterial homologue of a GlcNAcase enzyme (BtGH84) and the model system hen egg white lysozyme (HEWL). It was not possible to produce suitable NMT material due to the inherent instability of the protein produced in York. The screening results of HEWL with a new Surface Plasmon Resonance (SPR) assay, a cell based activity assay and TSA were inconsistent and difficult to interpret. However, BtGH84 was suitable for screening by both TSA and SPR. The resulting fragment hits are suitable starting points for further evolution.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Computer Aided Synthesis Prediction to Enable Augmented Chemical Discovery and Chemical Space Exploration

The drug-like chemical space is estimated to be 10 to the power of 60 molecules, and the largest generated database (GDB) obtained by the Reymond group is 165 billion molecules with up to 17 heavy atoms. Furthermore, deep learning techniques to explore regions of chemical space are becoming more popular. However, the key to realizing the generated structures experimentally lies in chemical synthesis. The application of which was previously limited to manual planning or slow computer assisted synthesis planning (CASP) models. Despite the 60-year history of CASP few synthesis planning tools have been open-sourced to the community. In this thesis I co-led the development of and investigated one of the only fully open-source synthesis planning tools called AiZynthFinder, trained on both public and proprietary datasets consisting of up to 17.5 million reactions. This enables synthesis guided exploration of the chemical space in a high throughput manner, to bridge the gap between compound generation and experimental realisation. I firstly investigate both public and proprietary reaction data, and their influence on route finding capability. Furthermore, I develop metrics for assessment of retrosynthetic prediction, single-step retrosynthesis models, and automated template extraction workflows. This is supplemented by a comparison of the underlying datasets and their corresponding models. Given the prevalence of ring systems in the GDB and wider medicinal chemistry domain, I developed ‘Ring Breaker’ - a data-driven approach to enable the prediction of ring-forming reactions. I demonstrate its utility on frequently found and unprecedented ring systems, in agreement with literature syntheses. Additionally, I highlight its potential for incorporation into CASP tools, and outline methodological improvements that result in the improvement of route-finding capability. To tackle the challenge of model throughput, I report a machine learning (ML) based classifier called the retrosynthetic accessibility score (RAscore), to assess the likelihood of finding a synthetic route using AiZynthFinder. The RAscore computes at least 4,500 times faster than AiZynthFinder. Thus, opens the possibility of pre-screening millions of virtual molecules from enumerated databases or generative models for synthesis informed compound prioritization. Finally, I combine chemical library visualization with synthetic route prediction to facilitate experimental engagement with synthetic chemists. I enable the navigation of chemical property space by using interactive visualization to deliver associated synthetic data as endpoints. This aids in the prioritization of compounds. The ability to view synthetic route information alongside structural descriptors facilitates a feedback mechanism for the improvement of CASP tools and enables rapid hypothesis testing. I demonstrate the workflow as applied to the GDB databases to augment compound prioritization and synthetic route design