Molecular structure input on the web

A molecule editor, that is program for input and editing of molecules, is an indispensable part of every cheminformatics or molecular processing system. This review focuses on a special type of molecule editors, namely those that are used for molecule structure input on the web. Scientific computing is now moving more and more in the direction of web services and cloud computing, with servers scattered all around the Internet. Thus a web browser has become the universal scientific user interface, and a tool to edit molecules directly within the web browser is essential

FlaME: Flash Molecular Editor - a 2D structure input tool for the web

<p>Abstract</p> <p>Background</p> <p>So far, there have been no Flash-based web tools available for chemical structure input. The authors herein present a feasibility study, aiming at the development of a compact and easy-to-use 2D structure editor, using Adobe's Flash technology and its programming language, ActionScript. As a reference model application from the Java world, we selected the Java Molecular Editor (JME). In this feasibility study, we made an attempt to realize a subset of JME's functionality in the Flash Molecular Editor (FlaME) utility. These basic capabilities are: structure input, editing and depiction of single molecules, data import and export in molfile format.</p> <p>Implementation</p> <p>The result of molecular diagram sketching in FlaME is accessible in V2000 molfile format. By integrating the molecular editor into a web page, its communication with the HTML elements on this page is established using the two JavaScript functions, <it>getMol() </it>and <it>setMol()</it>. In addition, structures can be copied to the <it>system clipboard</it>.</p> <p>Conclusion</p> <p>A first attempt was made to create a compact single-file application for 2D molecular structure input/editing on the web, based on Flash technology. With the application examples presented in this article, it could be demonstrated that the Flash methods are principally well-suited to provide the requisite communication between the Flash object (application) and the HTML elements on a web page, using JavaScript functions.</p

CurlySMILES: a chemical language to customize and annotate encodings of molecular and nanodevice structures

CurlySMILES is a chemical line notation which extends SMILES with annotations for storage, retrieval and modeling of interlinked, coordinated, assembled and adsorbed molecules in supramolecular structures and nanodevices. Annotations are enclosed in curly braces and anchored to an atomic node or at the end of the molecular graph depending on the annotation type. CurlySMILES includes predefined annotations for stereogenicity, electron delocalization charges, extra-molecular interactions and connectivity, surface attachment, solutions, and crystal structures and allows extensions for domain-specific annotations. CurlySMILES provides a shorthand format to encode molecules with repetitive substructural parts or motifs such as monomer units in macromolecules and amino acids in peptide chains. CurlySMILES further accommodates special formats for non-molecular materials that are commonly denoted by composition of atoms or substructures rather than complete atom connectivity

The PubChem chemical structure sketcher

PubChem is an important public, Web-based information source for chemical and bioactivity information. In order to provide convenient structure search methods on compounds stored in this database, one mandatory component is a Web-based drawing tool for interactive sketching of chemical query structures. Web-enabled chemical structure sketchers are not new, being in existence for years; however, solutions available rely on complex technology like Java applets or platform-dependent plug-ins. Due to general policy and support incident rate considerations, Java-based or platform-specific sketchers cannot be deployed as a part of public NCBI Web services. Our solution: a chemical structure sketching tool based exclusively on CGI server processing, client-side JavaScript functions, and image sequence streaming. The PubChem structure editor does not require the presence of any specific runtime support libraries or browser configurations on the client. It is completely platform-independent and verified to work on all major Web browsers, including older ones without support for Web2.0 JavaScript objects

MyChemise: A 2D drawing program that uses morphing for visualisation purposes

MyChemise (My Chemical Structure Editor) is a new 2D structure editor. It is designed as a Java applet that enables the direct creation of structures in the Internet using a web browser. MyChemise saves files in a digital format (.cse) and the import and export of .mol files using the appropriate connection tables is also possible

Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates

Computational drug design based on artificial intelligence is an emerging research area. At the time of writing this paper, the world suffers from an outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus replication is via protease inhibition. We propose an evolutionary multi-objective algorithm (EMOA) to design potential protease inhibitors for SARS-CoV-2's main protease. Based on the SELFIES representation the EMOA maximizes the binding of candidate ligands to the protein using the docking tool QuickVina 2, while at the same time taking into account further objectives like drug-likeliness or the fulfillment of filter constraints. The experimental part analyzes the evolutionary process and discusses the inhibitor candidates.Comment: 15 pages, 7 figures, submitted to PPSN 202

Interpreting linear support vector machine models with heat map molecule coloring

<p>Abstract</p> <p>Background</p> <p>Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity.</p> <p>Results</p> <p>We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor.</p> <p>Conclusions</p> <p>In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor.</p

Self-organizing ontology of biochemically relevant small molecules

<p>Abstract</p> <p>Background</p> <p>The advent of high-throughput experimentation in biochemistry has led to the generation of vast amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-activity relationship research, while providing new challenges, the biggest being the curation, annotation, and classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently existing chemical classification and annotation systems are not amenable to automated classification, formal and transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the automation of this process, especially for novel chemical entities of biological interest.</p> <p>Results</p> <p>To address this, we present a formal framework based on Semantic Web technologies for the automatic design of chemical ontology which can be used for automated classification of novel entities. We demonstrate the automatic self-assembly of a structure-based chemical ontology based on 60 MeSH and 40 ChEBI chemical classes. This ontology is then used to classify 200 compounds with an accuracy of 92.7%. We extend these structure-based classes with molecular feature information and demonstrate the utility of our framework for classification of functionally relevant chemicals. Finally, we discuss an iterative approach that we envision for future biochemical ontology development.</p> <p>Conclusions</p> <p>We conclude that the proposed methodology can ease the burden of chemical data annotators and dramatically increase their productivity. We anticipate that the use of formal logic in our proposed framework will make chemical classification criteria more transparent to humans and machines alike and will thus facilitate predictive and integrative bioactivity model development.</p

Consumer satisfaction with primary care provider choice and associated trust

BACKGROUND: Development of managed care, characterized by limited provider choice, is believed to undermine trust. Provider choice has been identified as strongly associated with physician trust. Stakeholders in a competitive healthcare market have competing agendas related to choice. The purpose of this study is to analyze variables associated with consumer's satisfaction that they have enough choice when selecting their primary care provider (PCP), and to analyze the importance of these variables on provider trust. METHODS: A 1999 randomized national cross-sectional telephone survey conducted of United States residential households, who had a telephone, had seen a medical professional at least twice in the past two years, and aged ≥ 20 years was selected for secondary data analyses. Among 1,117 households interviewed, 564 were selected as the final sample. Subjects responded to a core set of questions related to provider trust, and a subset of questions related to trust in the insurer. A previously developed conceptual framework was adopted. Linear and logistic regressions were performed based on this framework. RESULTS: Results affirmed 'satisfaction with amount of PCP choice' was significantly (p < .001) associated with provider trust. 'PCP's care being extremely effective' was strongly associated with 'satisfaction with amount of PCP choice' and 'provider trust'. Having sought a second opinion(s) was associated with lower trust. 'Spoke to the PCP outside the medical office,' 'satisfaction with the insurer' and 'insurer charges less if PCP within network' were all variables associated with 'satisfaction with amount of PCP choice' (all p < .05). CONCLUSION: This study confirmed the association of 'satisfaction with amount of PCP choice' with provider trust. Results affirmed 'enough PCP choice' was a strong predictor of provider trust. 'Second opinion on PCP' may indicate distrust in the provider. Data such as 'trust in providers in general' and 'the role of provider performance information' in choice, though import in PCP choice, were not available for analysis and should be explored in future studies. Results have implications for rethinking the relationships among consumer choice, consumer behaviors in making trade-offs in PCP choice, and the role of healthcare experiences in 'satisfaction with amount of PCP choice' or 'provider trust.