Development of deep learning applications for the automated extraction of chemical information from scientific literature

This dissertation focuses on developing deep learning applications for extracting chemical information from scientific literature, particularly targeting the automated recognition of molecular structures in images. DECIMER Segmentation, a novel application, employs a Mask Region-based Convolutional Neural Network (MRCNN) model to segment chemical structures in documents, aided by a mask expansion algorithm, marking a significant advancement in processing chemical literature. The Optical Chemical Structure Recognition (OCSR) tool DECIMER Image Transformer uses an encoder-decoder architecture to convert chemical structure depictions into the machine-readable SMILES format. The model has been trained on over 450 million pairs of images and SMILES representations. Its ability to interpret various depiction styles, including hand-drawn structures, sets a new standard in OCSR. To artificially generate large and diverse OCSR training datasets using multiple cheminformatics toolkits, RanDepict was developed. The diversification of training data ensures robust model generalisation across different chemical structure depictions. A unique dataset of hand-drawn molecule images was created to evaluate the model's performance in interpreting these challenging depictions. This dataset further contributes to the understanding of automated structure recognition from diverse styles. The integration of these technologies led to the creation of DECIMER.ai, an open-source web application that combines segmentation and interpretation tools, allowing users to extract and process chemical information from literature efficiently. The work concludes with a discussion on the significance of open data in advancing molecular informatics, highlighting the potential to broader chemical research domains. By adhering to FAIR data standards and open-source principles, the tools developed for this dissertation are designed for adaptability and future development within the community

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

Chemical Information Bulletin

Created as a supplement for "the regular journals of the American Chemical Society," this publication contains annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Information (CINF)

Coverage-Disclosure Conundrum and Future of Species Patents in India

Innovations are mostly derived from already existing technologies that may or may not have been patented. What could one think of, about the patentability of a product, let’s say a pharma product that is made from the group of previously known compounds, some of which are already patented? The answer to this question lies in the very technical field under patent law known as ‘Selection Patents’ or ‘Genus-Species Patents’. Predominantly this concept of selection patent or species patent is seen mostly in the domain of chemical compounds or species, but certainly is not limited to that only, as the same can be applied in other technological areas, such as engineering, biotechnology, material science and telecommunications. Selection patents/inventions are said so as they overlap with the disclosures in the preexisting art. Such aforesaid disclosures generally do not hamper the novelty of the latter invention unless the latter one does not encompass a new embodiment of feature or property. But this isn’t as straight forward as it seems to be. The critical issue in this domain is how to determine the novelty and inventive step of the selection inventions which are entangled in the dichotomy of coverage and disclosure. Off late there have been chunk of cases in India deciphering the coverage-disclosure conundrum in the field of species patents. This paper will foray as to what is this coverage-disclosure conundrum in selection patents, what are the legal framework that are prevalent across other jurisdictions to deal this and what is the future of specie patents in India especially in light of recently filed Dapagliflozin Appeals

Text Mining for Chemical Compounds

Exploring the chemical and biological space covered by patent and journal publications is crucial in early- stage medicinal chemistry activities. The analysis provides understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents and journals through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. In this book, we addressed the lack of quality measurements for assessing the correctness of structural representation within and across chemical databases; lack of resources to build text-mining systems; lack of high performance systems to extract chemical compounds from journals and patents; and lack of automated systems to identify relevant compounds in patents. The consistency and ambiguity of chemical identifiers was analyzed within and between small- molecule databases in Chapter 2 and Chapter 3. In Chapter 4 and Chapter 7 we developed resources to enable the construction of chemical text-mining systems. In Chapter 5 and Chapter 6, we used community challenges (BioCreative V and BioCreative VI) and their corresponding resources to identify mentions of chemical compounds in journal abstracts and patents. In Chapter 7 we used our findings in previous chapters to extract chemical named entities from patent full text and to classify the relevancy of chemical compounds

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Chemical Information Bulletin

Periodic supplement for "the regular journals of the American Chemical Society," containing annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Literature

Revealing Chemical Trends: Insights from Data-Driven Visualisation and Patent Analysis in Exposomics Research

Understanding historical chemical usage is crucial for assessing current and past impacts on human health and the environment and informing future regulatory decisions. However, past monitoring data is often limited in scope and number of chemicals, while suitable sample types are not always available for remeasurement. Data-driven cheminformatics methods on patent and literature data offer several opportunities to fill this gap. The chemical stripes were developed as an interactive, open source tool for visualising patent and literature trends over time, inspired by the global warming and biodiversity stripes. This paper details the underlying code and datasets behind the visualisation, with a major focus on the patent data sourced from PubChem, including patent origins, uses, and countries. Overall trends and specific examples are investigated in greater detail to explore both the promise and caveats that such data offers in assessing the trends and patterns of chemical patents over time and across different geographic regions. Despite a number of potential artefacts associated with patent data extraction, the integration of cheminformatics, statistical analysis, and data visualisation tools can help generate valuable insights that can both illuminate the chemical past and potentially serve towards an early warning system for the future