Generate and Test for Formulated Product Variants With Information Extraction and an In-Silico Model

The chemical industry is expanding its focus from process-centered products to product-centered products. Of these, consumer chemical products and other similar formulated products are especially ubiquitous. State of the art in the formulated product design relies heavily on experts and their expertise, leading to extended time to market and increased costs. The authors show that it is possible to construct a graph database of various details of products from textual sources, both offline and online. Similar to the “generate and test” approach, they propose that it is possible to generate feasible design variants of a given type of formulated product using the database so constructed. If they restrict the set of products that are applied to the skin, they propose to test the generated design variants using an in-silico model. Even though this chapter is an account of the work in progress, the authors believe the gains they can obtain from a readily accessible database and its integration with an in-silico model are substantial.

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Computer-Aided Discovery in Antimicrobial Research: In Silico Model for Virtual Screening of Potent and Safe Anti-Pseudomonas Agents

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207318666150305144249 ◽

2015 ◽

Vol 18 (3) ◽

pp. 305-314 ◽

Cited By ~ 13

Author(s):

Alejandro Speck-Planche ◽

Maria Cordeiro

Keyword(s):

Virtual Screening ◽

In Silico ◽

In Silico Model ◽

Computer Aided

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Usefulness of collaboration between mathematical models and cell engineering for elucidating complex disease mechanisms and discover effective drugs

European Heart Journal ◽

10.1093/ehjci/ehaa946.3600 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

H Kohjitani ◽

A Kashiwa ◽

T Makiyama ◽

F Toyoda ◽

Y Yamamoto ◽

...

Keyword(s):

Mathematical Model ◽

In Silico ◽

Complex Disease ◽

Ion Selectivity ◽

Public Institution ◽

Cell Engineering ◽

Funding Source ◽

Patch Clamp Technique ◽

In Silico Model

Abstract Background A missense mutation, CACNA1C-E1115K, located in the cardiac L-type calcium channel (LTCC), was recently reported to be associated with diverse arrhythmias. Several studies reported in-vivo and in-vitro modeling of this mutation, but actual mechanism and target drug of this disease has not been clarified due to its complex ion-mechanisms. Objective To reveal the mechanism of this diverse arrhythmogenic phenotype using combination of in-vitro and in-silico model. Methods and results Cell-Engineering Phase: We generated human induced pluripotent stem cell (hiPSC) from a patient carrying heterozygous CACNA1C-E1115K and differentiated into cardiomyocytes. Spontaneous APs were recorded from spontaneously beating single cardiomyocytes by using the perforated patch-clamp technique. Mathematical-Modeling Phase: We newly developed ICaL-mutation mathematical model, fitted into experimental data, including its impaired ion selectivity. Furthermore, we installed this mathematical model into hiPSC-CM simulation model. Collaboration Phase: Mutant in-silico model showed APD prolongation and frequent early afterdepolarization (EAD), which are same as in-vitro model. In-silico model revealed this EAD was mostly related to robust late-mode of sodium current occurred by Na+ overload and suggested that mexiletine is capable of reducing arrhythmia. Afterward, we applicated mexiletine onto hiPSC-CMs mutant model and found mexiletine suppress EADs. Conclusions Precise in-silico disease model can elucidate complicated ion currents and contribute predicting result of drug-testing. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Japan Society for the Promotion of Science, Grant-in-Aid for Young Scientists

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A corneal-PAMPA-based in silico model for predicting corneal permeability

Journal of Pharmaceutical and Biomedical Analysis ◽

10.1016/j.jpba.2021.114218 ◽

2021 ◽

pp. 114218

Author(s):

Anna Vincze ◽

Gergö Dargó ◽

Anita Rácz ◽

György T. Balogh

Keyword(s):

In Silico ◽

Corneal Permeability ◽

In Silico Model

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In Silico Hemostasis Modeling and Prediction

Hämostaseologie ◽

10.1055/a-1213-2117 ◽

2020 ◽

Vol 40 (04) ◽

pp. 524-535

Author(s):

Dmitry Y. Nechipurenko ◽

Aleksey M. Shibeko ◽

Anastasia N. Sveshnikova ◽

Mikhail A. Panteleev

Keyword(s):

In Silico ◽

Physiological Response ◽

Platelet Adhesion ◽

State Of The Art ◽

Thrombus Formation ◽

The State ◽

Modeling And Prediction ◽

Hemostatic System ◽

Single Enzyme

AbstractComputational physiology, i.e., reproduction of physiological (and, by extension, pathophysiological) processes in silico, could be considered one of the major goals in computational biology. One might use computers to simulate molecular interactions, enzyme kinetics, gene expression, or whole networks of biochemical reactions, but it is (patho)physiological meaning that is usually the meaningful goal of the research even when a single enzyme is its subject. Although exponential rise in the use of computational and mathematical models in the field of hemostasis and thrombosis began in the 1980s (first for blood coagulation, then for platelet adhesion, and finally for platelet signal transduction), the majority of their successful applications are still focused on simulating the elements of the hemostatic system rather than the total (patho)physiological response in situ. Here we discuss the state of the art, the state of the progress toward the efficient “virtual thrombus formation,” and what one can already get from the existing models.

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Supervised learning for the detection of negation and of its scope in French and Brazilian Portuguese biomedical corpora

Natural Language Engineering ◽

10.1017/s1351324920000352 ◽

2020 ◽

pp. 1-21 ◽

Cited By ~ 2

Author(s):

Clément Dalloux ◽

Vincent Claveau ◽

Natalia Grabar ◽

Lucas Emanuel Silva Oliveira ◽

Claudia Maria Cabral Moro ◽

...

Keyword(s):

Machine Learning ◽

Information Extraction ◽

State Of The Art ◽

Automatic Detection ◽

Brazilian Portuguese ◽

Supervised Machine Learning ◽

Biomedical Domain ◽

Learning Approaches ◽

Cross Domain ◽

Automatic Methods

Abstract Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.

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