Functional Group and Substructure Searching as a Tool in Metabolomics

BACKGROUND: A direct link between the names and structures of compounds and the functional groups contained within them is important, not only because biochemists frequently rely on literature that uses a free-text format to describe functional groups, but also because metabolic models depend upon the connections between enzymes and substrates being known and appropriately stored in databases. METHODOLOGY: We have developed a database named "Biochemical Substructure Search Catalogue" (BiSSCat), which contains 489 functional groups, >200,000 compounds and >1,000,000 different computationally constructed substructures, to allow identification of chemical compounds of biological interest. CONCLUSIONS: This database and its associated web-based search program (http://bisscat.org/) can be used to find compounds containing selected combinations of substructures and functional groups. It can be used to determine possible additional substrates for known enzymes and for putative enzymes found in genome projects. Its applications to enzyme inhibitor design are also discussed

Information revolutions, the information society, and the future of the history of information science

This paper aims to discuss the future of information history by interrogating its past. It presents in outline an account of the conditions and the trajectory of events that have culminated in today’s “information revolution” and “information society.” It suggests that we have already passed through at least two information orders or revolutions as we transition, first, from the long era of print that began over five hundred years ago with Gutenberg and the printing press. We have then moved through a predigital era after World War II, finally to a new era characterized by the advent of the ubiquitous technologies that are considered to herald a new “digital revolution” and the creation of new kind of “information society.” It argues that it is possible to see that the past is now opening itself to new kinds of scrutiny as a result of the apparently transformative changes that are currently taking place. It suggests that the future of the history of information science is best thought of as part of a still unrealized convergence of diverse historical approaches to understanding how societies are constituted, sustained, reproduced, and changed in part by information and the infrastructures that emerge to manage information access and use. In conclusion it suggests that different bodies of historical knowledge and historical research methodologies have emerged as we move into the digital world that might be usefully brought together in the future to broaden and deepen explorations of important historical information phenomena from Gutenberg to Google.published or submitted for publicationOpe

A review of molecular representation in the age of machine learning

Funder: UCB; Id: http://dx.doi.org/10.13039/100011110Abstract: Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances in computing, machine learning, and artificial intelligence. Everyone working with molecules, whether chemist or not, needs an understanding of the representation of molecules in a machine‐readable format, as this is central to computational chemistry. Four classes of representations are introduced: string, connection table, feature‐based, and computer‐learned representations. Three of the most significant representations are simplified molecular‐input line‐entry system (SMILES), International Chemical Identifier (InChI), and the MDL molfile, of which SMILES was the first to successfully be used in conjunction with a variational autoencoder (VAE) to yield a continuous representation of molecules. This is noteworthy because a continuous representation allows for efficient navigation of the immensely large chemical space of possible molecules. Since 2018, when the first model of this type was published, considerable effort has been put into developing novel and improved methodologies. Most, if not all, researchers in the community make their work easily accessible on GitHub, though discussion of computation time and domain of applicability is often overlooked. Herein, we present questions for consideration in future work which we believe will make chemical VAEs even more accessible. This article is categorized under: Data Science > Chemoinformatic

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.Comment: 34 pages, 15 figures, comments and suggestions for additional references are welcome

Outlook Magazine, Summer 2000

https://digitalcommons.wustl.edu/outlook/1138/thumbnail.jp

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

INTERNATIONAL COOPERATIVE ENFORCEMENT AGREEMENTS AND ANTITRUST EXTRATERRITORIALITY IN THE 21st CENTURY

It is the focus of this thesis to critically evaluate the cooperative enforcement option proffered by the US authorities with a view to judging its attractiveness to other nations and its adequacy in solving problems posed by extraterritoriality in today\u27s highly liberalized economy. In this regard, we shall see that the various models of cooperative enforcement arrangements adopted within the United States have failed to result in productive bilateral cooperation. This is due in large part, to the commitment of individual countries to satisfying national interests over cooperative obligations arising under the agreements. Because of these insufficiencies, the thesis reiterates the need for the US to actively partake in the ongoing effort within the WTO to forge global competition law.This thesis is made up of five Parts. Part I traces the origin of extraterritoriality in US antitrust law by examining changing the judicial attitude towards the extension of the Sherman Act abroad. The first section of Part II briefly catalogs international reaction to the reach of the Sherman Act into foreign territories. Part III opens with a discussion on the Cooperative Enforcement Agreements as a solution. Without necessarily underestimating the relevance of cooperation amongst antitrust enforcement agencies, the thesis laments the cooperative framework of the United States antitrust law, which has little chance of resulting in actual intercountry cooperative enforcement. In Part IV, the future of extraterritoriality in the 21st Century would be considered. Part V contains the thesis\u27 conclusions, mainly, that the inability of cooperative enforcement framework in the US

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

CULTURAL HISTORICAL ACTIVITY THEORY: A FRAMEWORK FOR WRITING CENTER ANALYSES

From the recognized beginning of the laboratory movement in composition instruction, teachers have sought to employ new and more practical methods useful in developing student writing. Such trends continue today as new generations of students enter the academy and new challenges emerge. From such conditions, we might see how components within a system of activity work together to meet objectives and develop outcomes within the shared dialectic of an activity system. Individuals and groups increase the potential for contradiction identification, thus, opportunities for solutions increase through mediational activities. With this idea in mind, this dissertation reviews writing center-related scholarship from 1887 through today to trace emerging contradictions in laboratory teachings epochal movements. The end goal, then, is to define how resolutions to those contradictions have given rise to our modern conceptualization of the writing center. Using Cultural-Historical Activity Theory (CHAT), this dissertation interprets the development of writing centers from their earliest beginnings. Through the evaluation of textual artifacts, I present the development of current writing center praxes in stages: a Formative Period; an Interim or Clinical Period; a Modern period; a Theoretical Period, and an emerging Activist Period. As a result, I look to provide modern writing center practitioners with a thorough history of writing center practices: what shaped them, through what contradictions they arose, what precipitated those contradictions, what resolved them, and what lies ahead. As communities like writing centers re-create themselvesthrough pushing and pulling, conflict and resolution, tension and releasethey birth new conceptualizations of realities. In the end, this dissertation uses CHAT to present a narrative about the development of writing center work that continues to unfold in new and dynamic ways. As a result, what may be most useful through this historical analysis is the way in which writing center practitioners may use CHAT to chart a way forward using the very framework used as the basis of this projects analysis. Today, writing centers may offer new ways to address a pedagogical order designed to challenge racism, homophobia, and other injustices through ongoing reading groups, curricular revision, and other faculty development efforts. Through learning our history, I believe we may more adequately position ourselves to shape our futures