scholarly journals In Memoriam—Alexander Lawson: Visionary Pioneer in Cheminformatics

2021 ◽  
Vol 43 (1) ◽  
pp. 30-30
Keyword(s):  
Chemical Structure ◽  
Structural Information ◽  
Database Searching ◽  
Chemical Nomenclature ◽  
In Memoriam

Abstract Alexander Lawson (20 Oct 1944 - 23 Feb 2020), known to his friends and peers as “Sandy,” passed away in early 2020. He is recognized as a pioneer and far-sighted visionary in the fields of chemical structure handling, database searching, chemical nomenclature, reading machines, and the linking of text and structural information.

scholarly journals 3D Structural View of Pathogen Recognition by Mammalian Lectin Receptors

2021 ◽  
Vol 8 ◽  
Author(s):  
Noriyoshi Manabe ◽  
Yoshiki Yamaguchi
Keyword(s):  
Binding Sites ◽  
Chemical Structure ◽  
Structural Information ◽  
Current Knowledge ◽  
Adaptive Immune System ◽  
Pathogen Recognition ◽  
Lectin Receptors ◽  
Adaptive Immune ◽  
Species Specific ◽  
Mammalian Lectin

Humans and other mammals resist exogenous pathogens by recognizing them as non-self. How do they do this? The answer lies in the recognition by mammalian lectin receptors of glycans usually found on the surface of pathogens and whose chemical structure is species-specific. Some glycan components, such as galactofuranose, only occur in microbes, and is the principal means by which mammalian lectin receptors recognize non-self. Several lectins may function together as pattern recognition receptors to survey the infecting pathogen before the adaptive immune system is invoked. Most lectins have primary and secondary monosaccharide-binding sites which together determine the specificity of a receptor toward microbial glycans. There may also be a hydrophobic groove alongside the sugar binding sites that increases specificity. Another elaboration is through oligomerization of lectin domains with defined spacing and arrangement that creates high-affinity binding towards multiply-presented glycans on microbes. Microbe-specific polysaccharides may arise through unique sugar linkages. Specificity can come from mammalian receptors possessing a shallow binding site and binding only internal disaccharide units, as in the recognition of mannan by Dectin-2. The accumulation of 3D structural information on lectins receptors has allowed the recognition modes of microbe glycans to be classified into several groupings. This review is an introduction to our current knowledge on the mechanisms of pathogen recognition by representative mammalian lectin receptors.

scholarly journals In Silico Predictions of Endocrine Disruptors Properties

Endocrinology ◽  
2019 ◽  
Vol 160 (11) ◽  
pp. 2709-2716 ◽  
Author(s):  
Melanie Schneider ◽  
Jean-Luc Pons ◽  
Gilles Labesse ◽  
William Bourguet
Keyword(s):  
Biological Activity ◽  
Endocrine Disruptors ◽  
In Silico ◽  
Chemical Structure ◽  
Structural Information ◽  
Broad Class ◽  
Computer Assisted ◽  
Screening Methods

Abstract Endocrine-disrupting chemicals (EDCs) are a broad class of molecules present in our environment that are suspected to cause adverse effects in the endocrine system by interfering with the synthesis, transport, degradation, or action of endogenous ligands. The characterization of the harmful interaction between environmental compounds and their potential cellular targets and the development of robust in vivo, in vitro, and in silico screening methods are important for assessment of the toxic potential of large numbers of chemicals. In this context, computer-aided technologies that will allow for activity prediction of endocrine disruptors and environmental risk assessments are being developed. These technologies must be able to cope with diverse data and connect chemistry at the atomic level with the biological activity at the cellular, organ, and organism levels. Quantitative structure–activity relationship methods became popular for toxicity issues. They correlate the chemical structure of compounds with biological activity through a number of molecular descriptors (e.g., molecular weight and parameters to account for hydrophobicity, topology, or electronic properties). Chemical structure analysis is a first step; however, modeling intermolecular interactions and cellular behavior will also be essential. The increasing number of three-dimensional crystal structures of EDCs’ targets has provided a wealth of structural information that can be used to predict their interactions with EDCs using docking and scoring procedures. In the present review, we have described the various computer-assisted approaches that use ligands and targets properties to predict endocrine disruptor activities.

scholarly journals Leveraging high-throughput screening data and conditional generative adversarial networks to advance predictive toxicology

2020 ◽  
Author(s):  
Adrian J. Green ◽  
Martin J. Mohlenkamp ◽  
Jhuma Das ◽  
Meenal Chaudhari ◽  
Lisa Truong ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Chemical Structure ◽  
Structural Information ◽  
Generative Adversarial Networks ◽  
Toxicity Data ◽  
Generative Adversarial Network ◽  
Structure Information ◽  
Adversarial Network

AbstractThere are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) methods are vital. As part of one such HTS effort, embryonic zebrafish were used to examine a suite of morphological and mortality endpoints at six concentrations from over 1,000 unique chemicals found in the ToxCast library (phase 1 and 2). We hypothesized that by using a conditional Generative Adversarial Network (cGAN) and leveraging this large set of toxicity data, plus chemical structure information, we could efficiently predict toxic outcomes of untested chemicals. CAS numbers for each chemical were used to generate textual files containing three-dimensional structural information for each chemical. Utilizing a novel method in this space, we converted the 3D structural information into a weighted set of points while retaining all information about the structure. In vivo toxicity and chemical data were used to train two neural network generators. The first used regression (Go-ZT) while the second utilized cGAN architecture (GAN-ZT) to train a generator to produce toxicity data. Our results showed that both Go-ZT and GAN-ZT models produce similar results, but the cGAN achieved a higher sensitivity (SE) value of 85.7% vs 71.4%. Conversely, Go-ZT attained higher specificity (SP), positive predictive value (PPV), and Kappa results of 67.3%, 23.4%, and 0.21 compared to 24.5%, 14.0%, and 0.03 for the cGAN, respectively. By combining both Go-ZT and GAN-ZT, our consensus model improved the SP, PPV, and Kappa, to 75.5%, 25.0%, and 0.211, respectively, resulting in an area under the receiver operating characteristic (AUROC) of 0.663. Considering their potential use as prescreening tools, these models could provide in vivo toxicity predictions and insight into untested areas of the chemical space to prioritize compounds for HT testing.SummaryA conditional Generative Adversarial Network (cGAN) can leverage a large chemical set of experimental toxicity data plus chemical structure information to predict the toxicity of untested compounds.

scholarly journals Integrating Cell Morphology with Gene Expression and Chemical Structure to Aid Mitochondrial Toxicity Detection

2022 ◽  
Author(s):  
Srijit Seal ◽  
Jordi Carreras-Puigvert ◽  
Maria-Anna Trapotsi ◽  
Hongbin Yang ◽  
Ola Spjuth ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Discovery ◽  
Chemical Structure ◽  
Structural Information ◽  
Chemical Space ◽  
Model Performance ◽  
In Vitro Assays ◽  
Mitochondrial Toxicity ◽  
Mitochondrial Membrane Depolarization

Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy and applicability domain to the chemical space of the training compounds. In this work, we aimed to utilize both -omics and chemical data to push beyond the state-of-the-art. We combined Cell Painting and Gene Expression data with chemical structural information from Morgan fingerprints for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. We observed that mitochondrial toxicants significantly differ from non-toxic compounds in morphological space and identified compound clusters having similar mechanisms of mitochondrial toxicity, thereby indicating that morphological space provides biological insights related to mechanisms of action of this endpoint. We further showed that models combining Cell Painting, Gene Expression features and Morgan fingerprints improved model performance on an external test set of 236 compounds by 60% (in terms of F1 score) and improved extrapolation to new chemical space. The performance of our combined models was comparable with dedicated in vitro assays for mitochondrial toxicity; and they were able to detect mitochondrial toxicity where Tox21 assays outcomes were inconclusive because of cytotoxicity. Our results suggest that combining chemical descriptors with different levels of biological readouts enhances the detection of mitochondrial toxicants, with practical implications for use in drug discovery.

scholarly journals A review of optical chemical structure recognition tools

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Kohulan Rajan ◽  
Henning Otto Brinkhaus ◽  
Achim Zielesny ◽  
Christoph Steinbeck
Keyword(s):  
Chemical Structure ◽  
Deep Neural Networks ◽  
Structural Information ◽  
Chemical Compounds ◽  
Research Field ◽  
Molecular Structures ◽  
Translation Process ◽  
Structure Recognition ◽  
Rule Based Approach ◽  
Machine Readable

Abstract Structural information about chemical compounds is typically conveyed as 2D images of molecular structures in scientific documents. Unfortunately, these depictions are not a machine-readable representation of the molecules. With a backlog of decades of chemical literature in printed form not properly represented in open-access databases, there is a high demand for the translation of graphical molecular depictions into machine-readable formats. This translation process is known as Optical Chemical Structure Recognition (OCSR). Today, we are looking back on nearly three decades of development in this demanding research field. Most OCSR methods follow a rule-based approach where the key step of vectorization of the depiction is followed by the interpretation of vectors and nodes as bonds and atoms. Opposed to that, some of the latest approaches are based on deep neural networks (DNN). This review provides an overview of all methods and tools that have been published in the field of OCSR. Additionally, a small benchmark study was performed with the available open-source OCSR tools in order to examine their performance.

scholarly journals FAIR-ifying the Exposome Journal: Templates for Chemical Structures and Transformations

Exposome ◽  
2021 ◽  
Author(s):  
Emma L Schymanski ◽  
Evan E Bolton
Keyword(s):  
Chemical Structure ◽  
Structural Information ◽  
Data File ◽  
Transformation Product ◽  
The Public ◽  
Chemical Structures ◽  
Biological Responses ◽  
Health And Disease ◽  
Definition Of ◽  
Diet Behaviour

Abstract The exposome, the totality of lifetime exposures, is a new and highly complex paradigm for health and disease. Tackling this challenge requires an effort well beyond single individuals or laboratories, where every piece of the puzzle will be vital. The launch of this new Exposome journal coincides with the evolution of the exposome through its teenage years and into a growing maturity in an increasingly open and FAIR (findable, accessible, interoperable, reusable) world. This letter discusses how both authors and the Exposome journal alike can help increase the FAIRness of the chemical structural information and the associated metadata in the journal, aiming to capture more details about the chemistry of exposomics. The proposed chemical structure template can serve as an interoperable supplementary format that is made accessible through the website and more findable by linking the DOI of this data file to the article DOI metadata, supporting further reuse. An additional Transformations template provides authors with a means to connect predecessor (parent, substrate) molecules to successor (transformation product, metabolite) molecules and thus provide FAIR connections between observed (i.e., experimental) chemical exposures and biological responses, to help improve the public knowledgebase on exposome-related transformations. These connections are vital to extend current biochemical knowledge and to fulfil the current Exposome definition of “the cumulative measure of environmental influences and associated biological responses throughout the lifespan including exposures from the environment, diet, behaviour, and endogenous processes”.

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