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

Intellectual Property Management in Health and Agricultural Innovation: A Handbook of Best Practices, Vol. 1

Prepared by and for policy-makers, leaders of public sector research establishments, technology transfer professionals, licensing executives, and scientists, this online resource offers up-to-date information and strategies for utilizing the power of both intellectual property and the public domain. Emphasis is placed on advancing innovation in health and agriculture, though many of the principles outlined here are broadly applicable across technology fields. Eschewing ideological debates and general proclamations, the authors always keep their eye on the practical side of IP management. The site is based on a comprehensive Handbook and Executive Guide that provide substantive discussions and analysis of the opportunities awaiting anyone in the field who wants to put intellectual property to work. This multi-volume work contains 153 chapters on a full range of IP topics and over 50 case studies, composed by over 200 authors from North, South, East, and West. If you are a policymaker, a senior administrator, a technology transfer manager, or a scientist, we invite you to use the companion site guide available at http://www.iphandbook.org/index.html The site guide distills the key points of each IP topic covered by the Handbook into simple language and places it in the context of evolving best practices specific to your professional role within the overall picture of IP management

Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures

This paper compares several published methods for clustering chemical structures, using both graph- and fingerprint-based similarity measures. The clusterings from each method were compared to determine the degree of cluster overlap. Each method was also evaluated on how well it grouped structures into clusters possessing a non-trivial substructural commonality. The methods which employ adjustable parameters were tested to determine the stability of each parameter for datasets of varying size and composition. Our experiments suggest that both graph- and fingerprint-based similarity measures can be used effectively for generating chemical clusterings; it is also suggested that the CAST and Yin–Chen methods, suggested recently for the clustering of gene expression patterns, may also prove effective for the clustering of 2D chemical structures

Superior economic performance in a small state: the pharmaceutical industry in Malta

Various academic disciplines have attempted to explain the factors underpinning superior economic performance. Generally they neglect the realities of small states. The literature fails to clearly define a small state . Mainstream theories associate smallness with sub-optimality . Small states studies tend to be conditioned by a vulnerability complex. Yet, a good number of small states have an economic track record which is the envy of much larger states. This thesis adopts an interdisciplinary approach to investigate the theoretical explanations of superior economic performance, at both the state and firm level. Resource-advantage theory, which claims to be a general theory of competition, offers valuable insights in understanding the superior economic performance of small states. The field research follows Porter (1998) in studying the performance of particular industries to understand the competitiveness of nations. A qualitative, case study approach, involving both primary and secondary investigation, explores the performance of the pharmaceutical industry in Malta following the country s decision to join the EU. This work perceives a small state as an organisation with well-defined, but permeable, boundaries. This open system is characterised by both a lack of market power and a small population. Through the secondary field research a small number of higher-order resources, competencies and dynamic capabilities (RCDCs) are identified. The field research s findings affirm the relevance of these arch-RCDCs in creating competitive advantage for the pharmaceutical industry in Malta. It also elucidates the key role played by an external catalyst, foreign direct investment, to circumvent domestic limitations. The study finds that it is still relevant to study small states and that achieving a strategic fit between the resource base and international market opportunities is essential if small states are to enhance their market power and achieve a superior economic performance

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)

The 2P-K Framework: A Personal Knowledge Measurement Framework for the Pharmaceutical Industry

Knowledge is a dynamic human process to justify our personal belief in pursuit of the truth. The intellectual output of any organisation is reliant upon the individual people within that organisation. Despite the eminent role of personal knowledge in organisations, personal knowledge management and measurement have received little attention, particularly in pharmaceutical manufacturing. The pharmaceutical industry is one of the pillars of the global economy and a knowledge-intensive sector where knowledge is described as the second product after medicines. The need of measurement to achieve effective management is not a new concept in management literature. This study offers an explanatory framework for personal knowledge, its underlying constructs and observed measures in the pharmaceutical manufacturing context. Following a sequential mixed method research (MMR) design, the researcher developed a measurement framework based on the thematic analysis of fifteen semi-structured interviews with industry experts and considering the extant academic and regulatory literature. A survey of 190 practitioners from the pharmaceutical manufacturing sector enabled quantitative testing and validation of the proposed models utilising confirmatory factor analysis. The pharmaceutical personal knowledge framework was the fruit of a comprehensive study to explain and measure the manifestations of personal knowledge in pharmaceutical organisations. The proposed framework identifies 41 personal knowledge measures reflecting six latent factors and the underlying personal knowledge. The hypothesised factors include: regulatory awareness, performance, wisdom, organisational understanding, mastership of product and process besides communication and networking skills. In order to enhance the applicability and flexibility of the measurement framework, an abbreviated 15-item form of the original framework was developed. The abbreviated pharmaceutical personal knowledge (2P-K) framework demonstrated superior model fit, better accuracy and reliability. The research results reveal that over 80% of the participant pharmaceutical organisations had a form of structured KM system. However, less than 30% integrated KM with corporate strategies suggesting that KM is still in the early stages of development in the pharmaceutical industry. Also, personal knowledge measurement is still a subjective practice and predominately an informal process. The 2P-K framework offers researchers and scholars a theoretically grounded original model for measuring personal knowledge. Also, it offers a basis for a personal knowledge measurement scale (2P-K-S) in the pharmaceutical manufacturing context. Finally, the study had some limitations. The framework survey relied on self-ratings. This might pose a risk of social desirability bias and Dunning–Kruger effect. Consequently, a 360- degree survey was suggested to achieve accurate assessments. Also, the model was developed and tested in an industry-specific context. A comparative study in similar manufacturing industries (e.g. chemical industries) is recommended to assess the validity of the current model or a modified version of it in other industries

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

Learning the Language of Chemical Reactions – Atom by Atom. Linguistics-Inspired Machine Learning Methods for Chemical Reaction Tasks

Over the last hundred years, not much has changed how organic chemistry is conducted. In most laboratories, the current state is still trial-and-error experiments guided by human expertise acquired over decades. What if, given all the knowledge published, we could develop an artificial intelligence-based assistant to accelerate the discovery of novel molecules? Although many approaches were recently developed to generate novel molecules in silico, only a few studies complete the full design-make-test cycle, including the synthesis and the experimental assessment. One reason is that the synthesis part can be tedious, time-consuming, and requires years of experience to perform successfully. Hence, the synthesis is one of the critical limiting factors in molecular discovery. In this thesis, I take advantage of similarities between human language and organic chemistry to apply linguistic methods to chemical reactions, and develop artificial intelligence-based tools for accelerating chemical synthesis. First, I investigate reaction prediction models focusing on small data sets of challenging stereo- and regioselective carbohydrate reactions. Second, I develop a multi-step synthesis planning tool predicting reactants and suitable reagents (e.g. catalysts and solvents). Both forward prediction and retrosynthesis approaches use black-box models. Hence, I then study methods to provide more information about the models’ predictions. I develop a reaction classification model that labels chemical reaction and facilitates the communication of reaction concepts. As a side product of the classification models, I obtain reaction fingerprints that enable efficient similarity searches in chemical reaction space. Moreover, I study approaches for predicting reaction yields. Lastly, after I approached all chemical reaction tasks with atom-mapping independent models, I demonstrate the generation of accurate atom-mapping from the patterns my models have learned while being trained self-supervised on chemical reactions. My PhD thesis’s leitmotif is the use of the attention-based Transformer architecture to molecules and reactions represented with a text notation. It is like atoms are my letters, molecules my words, and reactions my sentences. With this analogy, I teach my neural network models the language of chemical reactions - atom by atom. While exploring the link between organic chemistry and language, I make an essential step towards the automation of chemical synthesis, which could significantly reduce the costs and time required to discover and create new molecules and materials

Washington University Record, August 10, 2000

https://digitalcommons.wustl.edu/record/1868/thumbnail.jp