Special Libraries, July 1978

Volume 69, Issue 7https://scholarworks.sjsu.edu/sla_sl_1978/1005/thumbnail.jp

The evolution of an on-line chemical search system for an industrial research unit.

The objectives of this study were to design an information system, using modern computer technology, to meet a research chemist's need for chemical structural information, to quantify the effects of increasing degrees of computer technology on the use made of the facilities, and to relate the use of the service back to the individual chemist, his performance and background. A computer system was developed based on Wiswesser Line Notation and molecular formula as the chemical structure descriptors. Systems design and analysis were performed so that access to the information could be obtained directly for individual compounds and more generally for classes of compounds. As the system was being developed, its use by information staff was monitored by constant interaction with the people concerned. Where appropriate, the system was modifiea to meet information staff's requirements, but a number of precautions had to be introduced to prevent mis-use. The research chemists' use of the information services was studied retrospectively over a two-year period. In addition to the use made, several other factors were observed for each chemist. These included performance measures and background information on the chemists' research role. The data showed a steady increase in the demand for the services by the research chemist as the degree of computerisation increased. The use made of the services related closely to the number of compounds prepared by each chemist, but there was no significant correlation between a chemist's success in preparing biologically active compounds and his information use. The very individual way in which chemists conduct their research was highlighted by the wide range of use of the information facilities and the low correlation with background factors. This makes the design of on-line systems for use by chemists themselves complex and justifies the existence of the information scientist as an interface

Storing the wisdom: chemical concepts and chemoinformatics

The purpose of the paper is to examine the nature of chemical concepts, and the ways in which they are applied in chemoinformatics systems. An account of concepts in philosophy and in the information sciences leads to an analysis of chemical concepts, and their representation. The way in which concepts are applied in systems for information retrieval and for structure–property correlation are reviewed, and some issues noted. Attention is focused on the basic concepts or substance, reaction and property, on the organising concepts of chemical structure, structural similarity, periodicity, and on more specific concepts, including two- and three-dimensional structural patterns, reaction types, and property concepts. It is concluded that chemical concepts, despite (or perhaps because of) their vague and mutable nature, have considerable and continuing value in chemoinformatics, and that an increased formal treatment of concepts may have value in the future

Chemoinformatics approaches for new drugs discovery

Chemoinformatics uses computational methods and technologies to solve chemical problems. It works on molecular structures, their representations, properties and related data. The first and most important phase in this field is the translation of interconnected atomic systems into in-silico models, ensuring complete and correct chemical information transfer. In the last 20 years the chemical databases evolved from the state of molecular repositories to research tools for new drugs identification, while the modern high-throughput technologies allow for continuous chemical libraries size increase as highlighted by publicly available repository like PubChem [http://pubchem.ncbi.nlm.nih.gov/], ZINC [http://zinc.docking.org/], ChemSpider[http://www.chemspider. com/]. Chemical libraries fundamental requirements are molecular uniqueness, absence of ambiguity, chemical correctness (related to atoms, bonds, chemical orthography), standardized storage and registration formats. The aim of this work is the development of chemoinformatics tools and data for drug discovery process. The first part of the research project was focused on accessible commercial chemical space analysis; looking for molecular redundancy and in-silico models correctness in order to identify a unique and univocal molecular descriptor for chemical libraries indexing. This allows for the 0%-redundancy achievement on a 42 millions compounds library. The protocol was implemented as MMsDusty, a web based tool for molecular databases cleaning. The major protocol developed is MMsINC, a chemoinformatics platform based on a starting number of 4 millions non-redundant high-quality annotated and biomedically relevant chemical structures; the library is now being expanded up to 460 millions compounds. MMsINC is able to perform various types of queries, like substructure or similarity search and descriptors filtering. MMsINC is interfaced with PDB(Protein Data Bank)[http://www.rcsb.org/pdb/home/home.do] and related to approved drugs. The second developed protocol is called pepMMsMIMIC, a peptidomimetic screening tool based on multiconformational chemical libraries; the screening process uses pharmacophoric fingerprints similarity to identify small molecules able to geometrically and chemically mimic endogenous peptides or proteins. The last part of this project lead to the implementation of an optimized and exhaustive conformational space analysis protocol for small molecules libraries; this is crucial for high quality 3D molecular models prediction as requested in chemoinformatics applications. The torsional exploration was optimized in the range of most frequent dihedral angles seen in X-ray solved small molecules structures of CSD(Cambridge Structural Database); by appling this on a 89 millions structures library was generated a library of 2.6 x 10 exp 7 high quality conformers. Tools, protocols and platforms developed in this work allow for chemoinformatics analysis and screening on large size chemical libraries achieving high quality, correct and unique chemical data and in-silico model

Storing the wisdom: chemical concepts and chemoinformatics

The purpose of the paper is to examine the nature of chemical concepts, and the ways in which they are applied in chemoinformatics systems. An account of concepts in philosophy and in the information sciences leads to an analysis of chemical concepts, and their representation. The way in which concepts are applied in systems for information retrieval and for structure–property correlation are reviewed, and some issues noted. Attention is focused on the basic concepts or substance, reaction and property, on the organising concepts of chemical structure, structural similarity, periodicity, and on more specific concepts, including two- and three-dimensional structural patterns, reaction types, and property concepts. It is concluded that chemical concepts, despite (or perhaps because of) their vague and mutable nature, have considerable and continuing value in chemoinformatics, and that an increased formal treatment of concepts may have value in the future

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)

Reconstruction of lossless molecular representations from fingerprints

The simplified molecular-input line-entry system (SMILES) is the most prevalent molecular representation used in AI-based chemical applications. However, there are innate limitations associated with the internal structure of SMILES representations. In this context, this study exploits the resolution and robustness of unique molecular representations, i.e., SMILES and SELFIES (SELF-referencIng Embedded strings), reconstructed from a set of structural fingerprints, which are proposed and used herein as vital representational tools for chemical and natural language processing (NLP) applications. This is achieved by restoring the connectivity information lost during fingerprint transformation with high accuracy. Notably, the results reveal that seemingly irreversible molecule-to-fingerprint conversion is feasible. More specifically, four structural fingerprints, extended connectivity, topological torsion, atom pairs, and atomic environments can be used as inputs and outputs of chemical NLP applications. Therefore, this comprehensive study addresses the major limitation of structural fingerprints that precludes their use in NLP models. Our findings will facilitate the development of text- or fingerprint-based chemoinformatic models for generative and translational tasks.This work was supported by National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (Nos. NRF-2019M3E5D4066898, NRF-2022R1C1C1005080 and NRF-2020M3A9G7103933 to I.A. and J.L.). This work was also supported by the Korea Environment Industry & Technology Institute (KEITI) through the Technology Development Project for Safety Management of Household Chemical Products, funded by the Korea Ministry of Environment (MOE) (KEITI:2020002960002 and NTIS:1485017120 to U.V.U. and J.L.)

Design and implementation of a platform for predicting pharmacological properties of molecules

Tese de mestrado, Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2019O processo de descoberta e desenvolvimento de novos medicamentos prolonga-se por vários anos e implica o gasto de imensos recursos monetários. Como tal, vários métodos in silico são aplicados com o intuito de dimiuir os custos e tornar o processo mais eficiente. Estes métodos incluem triagem virtual, um processo pelo qual vastas coleções de compostos são examinadas para encontrar potencial terapêutico. QSAR (Quantitative Structure Activity Relationship) é uma das tecnologias utilizada em triagem virtual e em optimização de potencial farmacológico, em que a informação estrutural de ligandos conhecidos do alvo terapêutico é utilizada para prever a actividade biológica de um novo composto para com o alvo. Vários investigadores desenvolvem modelos de aprendizagem automática de QSAR para múltiplos alvos terapêuticos. Mas o seu uso está dependente do acesso aos mesmos e da facilidade em ter os modelos funcionais, o que pode ser complexo quando existem várias dependências ou quando o ambiente de desenvolvimento difere bastante do ambiente em que é usado. A aplicação ao qual este documento se refere foi desenvolvida para lidar com esta questão. Esta é uma plataforma centralizada onde investigadores podem aceder a vários modelos de QSAR, podendo testar os seus datasets para uma multitude de alvos terapêuticos. A aplicação permite usar identificadores moleculares como SMILES e InChI, e gere a sua integração em descritores moleculares para usar como input nos modelos. A plataforma pode ser acedida através de uma aplicação web com interface gráfica desenvolvida com o pacote Shiny para R e directamente através de uma REST API desenvolvida com o pacote flask-restful para Python. Toda a aplicação está modularizada através de teconologia de “contentores”, especificamente o Docker. O objectivo desta plataforma é divulgar o acesso aos modelos criados pela comunidade, condensando-os num só local e removendo a necessidade do utilizador de instalar ou parametrizar qualquer tipo de software. Fomentando assim o desenvolvimento de conhecimento e facilitando o processo de investigação.The drug discovery and design process is expensive, time-consuming and resource-intensive. Various in silico methods are used to make the process more efficient and productive. Methods such as Virtual Screening often take advantage of QSAR machine learning models to more easily pinpoint the most promising drug candidates, from large pools of compounds. QSAR, which means Quantitative Structure Activity Relationship, is a ligand-based method where structural information of known ligands of a specific target is used to predict the biological activity of another molecule against that target. They are also used to improve upon an existing molecule’s pharmacologic potential by elucidating the structural composition with desirable properties. Several researchers create and develop QSAR machine learning models for a variety of different therapeutic targets. However, their use is limited by lack of access to said models. Beyond access, there are often difficulties in using published software given the need to manage dependencies and replicating the development environment. To address this issue, the application documented here was designed and developed. In this centralized platform, researchers can access several QSAR machine learning models and test their own datasets for interaction with various therapeutic targets. The platform allows the use of widespread molecule identifiers as input, such as SMILES and InChI, handling the necessary integration into the appropriate molecular descriptors to be used in the model. The platform can be accessed through a Web Application with a full graphical user interface developed with the R package Shiny and through a REST API developed with the Flask Restful package for Python. The complete application is packaged up in container technology, specifically Docker. The main goal of this platform is to grant widespread access to the QSAR models developed by the scientific community, by concentrating them in a single location and removing the user’s need to install or set up software unfamiliar to them. This intends to incite knowledge creation and facilitate the research process

Biochemical complex data generation and integration in genome-scale metabolic models

Dissertação de mestrado em BioinformaticsThe (re-)construction of Genome-Scale Metabolic (GSM) models is highly dependent on biochemical databases. In fact, the biochemical data within these databases is limited, lacking, most of the times, in structurally defined compounds’ representations. In order to circumvent this limitation, compounds are frequently represented by their generic version. Lipids are paradigmatic cases: given that a multitude of lipid species can occur in nature, not only is their storage in databases hampered, but also their integration into GSM models. Accordingly, converting one lipid version, in GSM models, into another can be tricky, as these compounds possess side chains that are likely to be transferred all across their biosynthetic network. Hence, converting a lipid implies that all its precursors have to be converted as well, requiring information on lipid specificity and biosynthetic context. The present work represents a strategy to tackle this issue. Biochemical cOmplex data Integration in Metabolic Models at Genome scale (BOIMMG)’s pipeline encompasses the integration and processing of biochemical data from different sources, aiming at expanding the current knowledge in lipid biosynthesis, and its integration in GSM models. Generic reactions retrieved from MetaCyc were handled and transformed into reactions with structurally defined lipid species. More than 30 generic reactions were fully (and 27 partially) characterized, allowing to predict over 30000 new lipid structures and their biosynthetic context. The integration of BOIMMG’s data into GSM models was conducted for electron-transfer quinones, glycerolipids, and phospholipids metabolism. The validation accounted on the comparison of models with different versions of these metabolites. BOIMMG’s conversion modules were applied to Escherichia coli’s iJR904 model [1], generating 53 more matching lipids and 38 more matching reactions with iJR904 model’s iteration iAF1260b [2, 3], in which the conversion was performed and curated manually. To the best of our knowledge, BOIMMG’s database is the only with biosynthetic information regarding structurally defined lipids. Moreover, there is no other state-of-the-art tool capable of automatically generating complex lipid-specific networks.A reconstrução de modelos metabólicos à escala genómica (GSM na língua inglesa) depende grandemente da informaçãoo bioquímica presente em bases de dados. De facto, esta informação é muitas vezes limitada, podendo não conter representações de compostos estruturalmente definidos. Como tentativa de contornar esta limitação, os compostos químicos são frequentemente representados pela sua representação genérica. Os lípidos são casos paradigmáticos, dado que uma multitude de diferentes espécies químicas de lípidos ocorrem na natureza, dificultando o seu armazenamento em bases de dados, assim como a sua integração em modelos GSM. Desta forma, o processo de converter lípidos de uma versão genérica para uma versão estruturalmente definida não é trivial, dado que estes compostos possuem cadeias laterais que são transferidas ao longo das suas vias de biossíntese. Consequentemente, essa conversão implica que todos os precursores desses lípidos também sejam convertidos, requerendo haver informação relativa a lípidos específicos e às suas relações biossintéticas. O presente trabalho representa uma estratégia para resolver esse problema. A pipeline do software desenvolvido no âmbito deste trabalho, Biochemical cOmplex dataIntegration in Metabolic Models at Genome scale (BOIMMG), engloba a integração e processamento de dados bioquímicos de diferentes fontes, visando a expansão do conhecimento atual na biossíntese de lípidos, assim como a sua integração em modelos GSM. Relativamente à segunda fase, reações genéricas extraídas da base de dados MetaCyc foram processadas e transformadas em reações com lípidos estruturalmente definidos. Mais de 30 reações genéricas foram completamente (e 27 parcialmente) caracterizadas, permitindo prever mais de 30000 novas estruturas de lípidos, assim como os seus contextos biossintéticos. A integração dos dados nos modelos GSM foi conduzido para o metabolismo das quinonas transportadoras de eletrões, glicerolípidos e fosfolípidos. A validação teve em conta a comparação entre modelos com diferentes versões destes metabolitos. Os módulos de conversão do BOIMMG foram aplicados ao modelo iJR904 de Escherichia coli [1], gerando mais 53 lípidos e 38 reações que se encontram no modelo iAF1260b [2, 3], uma iteração do modelo iJR904 cuja conversão de lípidos se procedeu manualmente. A base de dados gerada pelo método BOIMMG é a única que contém informação biossintética relata a lípidos estruturalmente definidos. Adicionalmente, BOIMMG é uma ferramenta única que permite gerar redes complexas de lípidos automaticamente