Toxicity Prediction using Locality-Sensitive Deep Learner

2021 ◽  
pp. 100210
Author(s):  
Xiu Huan Yap ◽  
Michael Raymer
Keyword(s):  
Toxicity Prediction

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

2019 ◽  
Author(s):  
Qiannan Duan ◽  
Jianchao Lee ◽  
Jinhong Gao ◽  
Jiayuan Chen ◽  
Yachao Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Toxicity ◽  
Model Building ◽  
Low Cost ◽  
Enzyme Reaction ◽  
Chemical Effect ◽  
Technological Innovations ◽  
Acute Toxicity Test ◽  
Toxicity Prediction ◽  
Traditional Understanding

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

Novel Thiazole-Based Thiazolidinones as Potent Anti-infective Agents: In silico PASS and Toxicity Prediction, Synthesis, Biological Evaluation and Molecular Modelling

2020 ◽  
Vol 23 (2) ◽  
pp. 126-140 ◽  
Author(s):  
Christophe Tratrat
Keyword(s):  
In Silico ◽  
Antimicrobial Agents ◽  
Target Identification ◽  
Biological Evaluation ◽  
Microbial Infection ◽  
Toxicity Prediction ◽  
Discovery Studio ◽  
Bacteria And Fungi ◽  
Multiple Drugs ◽  
Antibacterial And Antifungal

Aims and Objective: The infectious disease treatment remains a challenging concern owing to the increasing number of pathogenic microorganisms associated with resistance to multiple drugs. A promising approach for combating microbial infection is to combine two or more known bioactive heterocyclic pharmacophores in one molecular platform. Herein, the synthesis and biological evaluation of novel thiazole-thiazolidinone hybrids as potential antimicrobial agents were dissimilated. Materials and Methods: The preparation of the substituted 5-benzylidene-2-thiazolyimino-4- thiazolidinones was achieved in three steps from 2-amino-5-methylthiazoline. All the compounds have been screened in PASS antibacterial activity prediction and in a panel of bacteria and fungi strains. Minimum inhibitory concentration and minimum bacterial concentration were both determined by microdilution assays. Molecular modeling was conducted using Accelrys Discovery Studio 4.0 client. ToxPredict (OPEN TOX) and ProTox were used to estimate the toxicity of the title compounds. Results: PASS prediction revealed the potentiality antibacterial property of the designed thiazolethiazolidinone hybrids. All tested compounds were found to kill and to inhibit the growth of a vast variety of bacteria and fungi, and were more potent than the commercial drugs, streptomycin, ampicillin, bifomazole and ketoconazole. Further, in silico study was carried out for prospective molecular target identification and revealed favorable interaction with the target enzymes E. coli MurB and CYP51B of Aspergillus fumigatus. Toxicity prediction revealed that none of the active compounds was found toxic. Conclusion: Substituted 5-benzylidene-2-thiazolyimino-4-thiazolidinones, endowing remarkable antibacterial and antifungal properties, were identified as a novel class of antimicrobial agents and may find a potential therapeutic use to eradicate infectious diseases.

scholarly journals Quantitative Toxicity Prediction via Meta Ensembling of Multitask Deep Learning Models

ACS Omega ◽  
2021 ◽  
Author(s):  
Abdul Karim ◽  
Vahid Riahi ◽  
Avinash Mishra ◽  
M. A. Hakim Newton ◽  
Abdollah Dehzangi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Toxicity Prediction

Developing read-across for toxicity prediction within an AOP framework to support the transition from the analogue approach to local QSARs

2015 ◽  
Vol 238 (2) ◽  
pp. S166
Author(s):  
F.P. Steinmetz ◽  
D.J. Ebbrell ◽  
S.J. Enoch ◽  
J.C. Madden ◽  
M.D. Nelms ◽  
...  
Keyword(s):  
Toxicity Prediction

ToxDP2 Database: Toxicity Prediction of Dietary Polyphenols

2021 ◽  
pp. 131350
Author(s):  
Meetali Sinha ◽  
Deepak Kumar Sachan ◽  
Roshni Bhattacharya ◽  
Prakrity Singh ◽  
Ramakrishnan Parthasarathi
Keyword(s):  
Toxicity Prediction ◽  
Dietary Polyphenols

Artificial Intelligence and Data Mining for Toxicity Prediction

2006 ◽  
Vol 2 (2) ◽  
pp. 123-133 ◽  
Author(s):  
Christoph Helma ◽  
Jeroen Kazius
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Toxicity Prediction

In-vitro hematological toxicity prediction by colony-forming cell assays

2013 ◽  
Vol 5 (4) ◽  
pp. 169-176 ◽  
Author(s):  
Navneet Kumar Yadav ◽  
Pooja Shukla ◽  
Ankur Omer ◽  
Rama Kant Singh
Keyword(s):  
Hematological Toxicity ◽  
Toxicity Prediction ◽  
Cell Assays ◽  
Colony Forming Cell
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