scholarly journals Bioactivity Profile Similarities to Expand the Repertoire of COVID-19 Drugs

2020 ◽  
Author(s):  
Miquel Duran-Frigola ◽  
Martino Bertoni ◽  
Eduardo Pauls ◽  
Victor Alcalde ◽  
Gemma Turon ◽  
...  
Keyword(s):  
Language Processing ◽  
Source Code ◽  
Bioactive Molecules ◽  
Level Of Evidence ◽  
Drug Candidates ◽  
Drug Molecules ◽  
Online Resource ◽  
Functional Features ◽  
Computational Platform ◽  
Bioactivity Profile

We present an online resource, based on small-molecule bioactivity signatures and natural language processing, to expand the portfolio of compounds with potential to treat COVID-19. By comparing the set of drugs reported to be potentially active against SARS-CoV-2 to a universe of 1M bioactive molecules, we identify compounds that display analogous chemical and functional features to the current COVID-19 candidates. Searches can be filtered by level of evidence and mechanism of action, and results can be restricted to drug molecules or include the much broader space of bioactive compounds. Moreover, we allow users to contribute COVID-19 drug candidates, which are automatically incorporated to the pipeline once per day. The computational platform, as well as the source code, is available at https://sbnb.irbbarcelona.org/covid19.

scholarly journals Bioactivity Profile Similarities to Expand the Repertoire of COVID-19 Drugs

2020 ◽  
Author(s):  
Miquel Duran-Frigola ◽  
Martino Bertoni ◽  
Eduardo Pauls ◽  
Victor Alcalde ◽  
Gemma Turon ◽  
...  
Keyword(s):  
Language Processing ◽  
Source Code ◽  
Bioactive Molecules ◽  
Level Of Evidence ◽  
Drug Candidates ◽  
Drug Molecules ◽  
Online Resource ◽  
Functional Features ◽  
Computational Platform ◽  
Bioactivity Profile

We present an online resource, based on small-molecule bioactivity signatures and natural language processing, to expand the portfolio of compounds with potential to treat COVID-19. By comparing the set of drugs reported to be potentially active against SARS-CoV-2 to a universe of 1M bioactive molecules, we identify compounds that display analogous chemical and functional features to the current COVID-19 candidates. Searches can be filtered by level of evidence and mechanism of action, and results can be restricted to drug molecules or include the much broader space of bioactive compounds. Moreover, we allow users to contribute COVID-19 drug candidates, which are automatically incorporated to the pipeline once per day. The computational platform, as well as the source code, is available at https://sbnb.irbbarcelona.org/covid19.

scholarly journals Bioactivity Profile Similarities to Expand the Repertoire of COVID-19 Drugs

2020 ◽  
Author(s):  
Miquel Duran-Frigola ◽  
Martino Bertoni ◽  
Roi Blanco ◽  
Victor Martinez ◽  
Eduardo Pauls ◽  
...  
Keyword(s):  
Language Processing ◽  
Source Code ◽  
Bioactive Molecules ◽  
Level Of Evidence ◽  
Drug Candidates ◽  
Drug Molecules ◽  
Online Resource ◽  
Functional Features ◽  
Computational Platform ◽  
Bioactivity Profile

We present an online resource, based on small-molecule bioactivity signatures and natural language processing, to expand the portfolio of compounds with potential to treat COVID-19. By comparing the set of drugs reported to be potentially active against SARS-CoV-2 to a universe of 1M bioactive molecules, we identify compounds that display analogous chemical and functional features to the current COVID-19 candidates. Searches can be filtered by level of evidence and mechanism of action, and results can be restricted to drug molecules or include the much broader space of bioactive compounds. Moreover, we allow users to contribute COVID-19 drug candidates, which are automatically incorporated to the pipeline once per day. The computational platform, as well as the source code, is available at https://sbnb.irbbarcelona.org/covid19.

scholarly journals Linguistic feature of anorexia nervosa: a prospective case–control pilot study

2021 ◽  
Author(s):  
Vittoria Cuteri ◽  
Giulia Minori ◽  
Gloria Gagliardi ◽  
Fabio Tamburini ◽  
Elisabetta Malaspina ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Language Processing ◽  
Statistical Significance ◽  
Case Control ◽  
Pathological Process ◽  
Linguistic Features ◽  
Level Of Evidence ◽  
Linguistic Feature ◽  
Linguistic Markers ◽  
Clinical Conditions

Abstract Purpose Attention has recently been paid to Clinical Linguistics for the detection and support of clinical conditions. Many works have been published on the “linguistic profile” of various clinical populations, but very few papers have been devoted to linguistic changes in patients with eating disorders. Patients with Anorexia Nervosa (AN) share similar psychological features such as disturbances in self-perceived body image, inflexible and obsessive thinking and anxious or depressive traits. We hypothesize that these characteristics can result in altered linguistic patterns and be detected using the Natural Language Processing tools. Methods We enrolled 51 young participants from December 2019 to February 2020 (age range: 14–18): 17 girls with a clinical diagnosis of AN, and 34 normal-weighted peers, matched by gender, age and educational level. Participants in each group were asked to produce three written texts (around 10–15 lines long). A rich set of linguistic features was extracted from the text samples and the statistical significance in pinpointing the pathological process was measured. Results Comparison between the two groups showed several linguistics indexes as statistically significant, with syntactic reduction as the most relevant trait of AN productions. In particular, the following features emerge as statistically significant in distinguishing AN girls and their normal-weighted peers: the length of the sentences, the complexity of the noun phrase, and the global syntactic complexity. This peculiar pattern of linguistic erosion may be due to the severe metabolic impairment also affecting the central nervous system in AN. Conclusion These preliminary data showed the existence of linguistic parameters as probable linguistic markers of AN. However, the analysis of a bigger cohort, still ongoing, is needed to consolidate this assumption. Level of evidence III Evidence obtained from case–control analytic studies.

scholarly journals Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rakesh David ◽  
Rhys-Joshua D. Menezes ◽  
Jan De Klerk ◽  
Ian R. Castleden ◽  
Cornelia M. Hooper ◽  
...  
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Data Dissemination ◽  
Short Term Memory ◽  
Biological Data ◽  
Experimental Methodology ◽  
Subcellular Localisation ◽  
Crop Species ◽  
Deep Recurrent Neural Network ◽  
Functional Features

AbstractThe increased diversity and scale of published biological data has to led to a growing appreciation for the applications of machine learning and statistical methodologies to gain new insights. Key to achieving this aim is solving the Relationship Extraction problem which specifies the semantic interaction between two or more biological entities in a published study. Here, we employed two deep neural network natural language processing (NLP) methods, namely: the continuous bag of words (CBOW), and the bi-directional long short-term memory (bi-LSTM). These methods were employed to predict relations between entities that describe protein subcellular localisation in plants. We applied our system to 1700 published Arabidopsis protein subcellular studies from the SUBA manually curated dataset. The system combines pre-processing of full-text articles in a machine-readable format with relevant sentence extraction for downstream NLP analysis. Using the SUBA corpus, the neural network classifier predicted interactions between protein name, subcellular localisation and experimental methodology with an average precision, recall rate, accuracy and F1 scores of 95.1%, 82.8%, 89.3% and 88.4% respectively (n = 30). Comparable scoring metrics were obtained using the CropPAL database as an independent testing dataset that stores protein subcellular localisation in crop species, demonstrating wide applicability of prediction model. We provide a framework for extracting protein functional features from unstructured text in the literature with high accuracy, improving data dissemination and unlocking the potential of big data text analytics for generating new hypotheses.

scholarly journals Organophotocatalytic selective deuterodehalogenation of aryl or alkyl chlorides

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yanjun Li ◽  
Ziqi Ye ◽  
Yu-Mei Lin ◽  
Yan Liu ◽  
Yumeng Zhang ◽  
...  
Keyword(s):  
Room Temperature ◽  
Sodium Formate ◽  
Bioactive Molecules ◽  
Co Catalyst ◽  
Aryl Chlorides ◽  
Aryl Amine ◽  
Drug Molecules ◽  
Photocatalytic System ◽  
Site Selective ◽  
Environmentally Friendly Method

AbstractDevelopment of practical deuteration reactions is highly valuable for organic synthesis, analytic chemistry and pharmaceutic chemistry. Deuterodehalogenation of organic chlorides tends to be an attractive strategy but remains a challenging task. We here develop a photocatalytic system consisting of an aryl-amine photocatalyst and a disulfide co-catalyst in the presence of sodium formate as an electron and hydrogen donor. Accordingly, many aryl chlorides, alkyl chlorides, and other halides are converted to deuterated products at room temperature in air (>90 examples, up to 99% D-incorporation). The mechanistic studies reveal that the aryl amine serves as reducing photoredox catalyst to initiate cleavage of the C-Cl bond, at the same time as energy transfer catalyst to induce homolysis of the disulfide for consequent deuterium transfer process. This economic and environmentally-friendly method can be used for site-selective D-labeling of a number of bioactive molecules and direct H/D exchange of some drug molecules.

scholarly journals Pyrrolidine in Drug Discovery: A Versatile Scaffold for Novel Biologically Active Compounds

2021 ◽  
Vol 379 (5) ◽  
Author(s):  
Giovanna Li Petri ◽  
Maria Valeria Raimondi ◽  
Virginia Spanò ◽  
Ralph Holl ◽  
Paola Barraja ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Binding Mode ◽  
Biologically Active ◽  
Bioactive Molecules ◽  
Biological Profile ◽  
Pyrrolidine Ring ◽  
Reaction Conditions ◽  
Drug Candidates ◽  
Steric Factors ◽  
Proline Derivatives

AbstractThe five-membered pyrrolidine ring is one of the nitrogen heterocycles used widely by medicinal chemists to obtain compounds for the treatment of human diseases. The great interest in this saturated scaffold is enhanced by (1) the possibility to efficiently explore the pharmacophore space due to sp3-hybridization, (2) the contribution to the stereochemistry of the molecule, (3) and the increased three-dimensional (3D) coverage due to the non-planarity of the ring—a phenomenon called “pseudorotation”. In this review, we report bioactive molecules with target selectivity characterized by the pyrrolidine ring and its derivatives, including pyrrolizines, pyrrolidine-2-one, pyrrolidine-2,5-diones and prolinol described in the literature from 2015 to date. After a comparison of the physicochemical parameters of pyrrolidine with the parent aromatic pyrrole and cyclopentane, we investigate the influence of steric factors on biological activity, also describing the structure–activity relationship (SAR) of the studied compounds. To aid the reader’s approach to reading the manuscript, we have planned the review on the basis of the synthetic strategies used: (1) ring construction from different cyclic or acyclic precursors, reporting the synthesis and the reaction conditions, or (2) functionalization of preformed pyrrolidine rings, e.g., proline derivatives. Since one of the most significant features of the pyrrolidine ring is the stereogenicity of carbons, we highlight how the different stereoisomers and the spatial orientation of substituents can lead to a different biological profile of drug candidates, due to the different binding mode to enantioselective proteins. We believe that this work can guide medicinal chemists to the best approach in the design of new pyrrolidine compounds with different biological profiles.

The Position of ADME Predictions in Multi-Objective QSAR

2021 ◽  
Vol 6 (1) ◽  
pp. 1-8
Author(s):  
Anna Tsantili-Kakoulidou
Keyword(s):  
Drug Efficacy ◽  
Clinical Development ◽  
Efficacy And Safety ◽  
Huge Amount ◽  
Multi Objective ◽  
Drug Candidates ◽  
Drug Molecules ◽  
Qsar Models ◽  
Adme Properties ◽  
Single Objective

ADME properties and toxicity predictions play an essential role in prioritization and optimization of drug molecules. According to recent statistics, drug efficacy and safety are principal reasons for drug failure. In this perspective, the position of ADME predictions in the evolution of traditional QSAR from the single objective of biological activity to a multi-task concept is discussed. The essential features of ADME and toxicity QSAR models are highlighted. Since such models are applied to prioritize existing or virtual project compounds with already established or predicted target affinity, a mechanistic interpretation, although desirable, is not a primary goal. However, a broad applicability domain is crucial. A future challenge with multi-objective QSAR is to adapt to the realm of big data by integrating techniques for the exploitation of the continuously increasing number of ADME data and the huge amount of clinical development endpoints for the sake of efficacy and safety of new drug candidates.

scholarly journals Interactive use of online health resources: a comparison of consumer and professional questions

2016 ◽  
Vol 23 (4) ◽  
pp. 802-811 ◽  
Author(s):  
Kirk Roberts ◽  
Dina Demner-Fushman
Keyword(s):  
Language Processing ◽  
Information Access ◽  
Health Resources ◽  
Background Information ◽  
Special Focus ◽  
Health And Wellness ◽  
Medical Problems ◽  
Online Resource ◽  
The Individual ◽  
Processing Techniques

Abstract Objective To understand how consumer questions on online resources differ from questions asked by professionals, and how such consumer questions differ across resources. Materials and Methods Ten online question corpora, 5 consumer and 5 professional, with a combined total of over 40 000 questions, were analyzed using a variety of natural language processing techniques. These techniques analyze questions at the lexical, syntactic, and semantic levels, exposing differences in both form and content. Results Consumer questions tend to be longer than professional questions, more closely resemble open-domain language, and focus far more on medical problems. Consumers ask more sub-questions, provide far more background information, and ask different types of questions than professionals. Furthermore, there is substantial variance of these factors between the different consumer corpora. Discussion The form of consumer questions is highly dependent upon the individual online resource, especially in the amount of background information provided. Professionals, on the other hand, provide very little background information and often ask much shorter questions. The content of consumer questions is also highly dependent upon the resource. While professional questions commonly discuss treatments and tests, consumer questions focus disproportionately on symptoms and diseases. Further, consumers place far more emphasis on certain types of health problems (eg, sexual health). Conclusion Websites for consumers to submit health questions are a popular online resource filling important gaps in consumer health information. By analyzing how consumers write questions on these resources, we can better understand these gaps and create solutions for improving information access. This article is part of the Special Focus on Person-Generated Health and Wellness Data, which published in the May 2016 issue, Volume 23, Issue 3.

scholarly journals hPSC-Derived Enteric Ganglioids Model Human ENS Development and Function

2022 ◽  
Author(s):  
Homa Majd ◽  
Ryan M Samuel ◽  
Jonathan T Ramirez ◽  
Ali Kalantari ◽  
Kevin Barber ◽  
...  
Keyword(s):  
Ex Vivo ◽  
Xenograft Model ◽  
Developmental Relationships ◽  
Drug Candidates ◽  
Effective Strategies ◽  
Wide Range ◽  
Functional Features ◽  
And Function

The enteric nervous system (ENS) plays a central role in gut physiology and mediating the crosstalk between the gastrointestinal (GI) tract and other organs. The human ENS has remained elusive, highlighting the need for an in vitro modeling and mapping blueprint. Here we map out the developmental and functional features of the human ENS, by establishing robust and scalable 2D ENS cultures and 3D enteric ganglioids from human pluripotent stem cells (hPSCs). These models recapitulate the remarkable neuronal and glial diversity found in primary tissue and enable comprehensive molecular analyses that uncover functional and developmental relationships within these lineages. As a salient example of the power of this system, we performed in-depth characterization of enteric nitrergic neurons (NO neurons) which are implicated in a wide range of GI motility disorders. We conducted an unbiased screen and identified drug candidates that modulate the activity of NO neurons and demonstrated their potential in promoting motility in mouse colonic tissue ex vivo. We established a high-throughput strategy to define the developmental programs involved in NO neuron specification and discovered that PDGFR inhibition boosts the induction of NO neurons in enteric ganglioids. Transplantation of these ganglioids in the colon of NO neuron-deficient mice results in extensive tissue engraftment, providing a xenograft model for the study of human ENS in vivo and the development of cell-based therapies for neurodegenerative GI disorders. These studies provide a framework for deciphering fundamental features of the human ENS and designing effective strategies to treat enteric neuropathies.  

scholarly journals Potential Drug Candidates for SARS-CoV-2 Using Computational Screening and Enhanced Sampling Methods

2020 ◽  
Author(s):  
Sadanandam Namsani ◽  
Debabrata Pramanik ◽  
Mohd Aamir Khan ◽  
Sudip Roy ◽  
Jayant Singh
Keyword(s):  
Free Energy ◽  
High Performance ◽  
Search Space ◽  
Computational Method ◽  
Enhanced Sampling ◽  
Free Energy Surface ◽  
Drug Candidates ◽  
Drug Molecules ◽  
Computational Screening ◽  
Graphical Processing

<div><div><div><p>Here we report new chemical entities that are highly specific in binding towards the 3-chymotrypsin- like cysteine protease (3CLpro) protein present in the novel SARS-CoV2 virus. The viral 3CLpro</p><p>protein controls coronavirus replication. Therefore, 3CLpro is identified as a target for drug molecules. We have implemented an enhanced sampling method in combination with molecular dynamics and docking to bring down the computational screening search space to four molecules that could be synthesised and tested against COVID-19. Our computational method is much more robust than any other method available for drug screening e.g., docking, because of sampling of the free energy surface of the binding site of the protein (including the ligand) and use of explicit solvent. We have considered all possible interactions between all the atoms present in the protein, ligands, and water. Using high performance computing with graphical processing units we are able to perform large number of simulations within a month's time and converge to 4 most strongly bound ligands (by free energy and other scores) from a set of 17 ligands with lower docking scores. Based on our results and analysis, we claim with high confidence, that we have identified four potential ligands. Out of those, one particular ligand is the most promising candidate, based on free energy data, for further synthesis and testing against SARS-CoV-2 and might be effective for the cure of COVID-19.</p></div></div></div>

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