scholarly journals Using chemical ontologies to create molecular prediction systems for any molecular property

2020 ◽  
Author(s):  
Lutz Weber ◽  
Konstantin Kruse ◽  
Timo Böhme ◽  
Claudia Bobach ◽  
Stephen Boyer
Keyword(s):  
Molecular Property ◽  
Prediction Systems ◽  
Chemical Ontologies

scholarly journals Using chemical ontologies to create molecular prediction systems for any molecular property

2020 ◽  
Author(s):  
Lutz Weber ◽  
Konstantin Kruse ◽  
Timo Böhme ◽  
Claudia Bobach ◽  
Stephen Boyer
Keyword(s):  
Molecular Property ◽  
Prediction Systems ◽  
Chemical Ontologies

scholarly journals Using chemical ontologies to create molecular prediction systems for any molecular property

2020 ◽  
Author(s):  
Lutz Weber ◽  
Konstantin Kruse ◽  
Timo Böhme ◽  
Claudia Bobach ◽  
Stephen Boyer
Keyword(s):  
Molecular Property ◽  
Prediction Systems ◽  
Chemical Ontologies

scholarly journals Using chemical ontologies to create molecular prediction systems for any molecular property

2020 ◽  
Author(s):  
Lutz Weber ◽  
Konstantin Kruse ◽  
Timo Böhme ◽  
Claudia Bobach ◽  
Stephen Boyer
Keyword(s):  
Molecular Property ◽  
Prediction Systems ◽  
Chemical Ontologies

Design, Synthesis and Pharmacological Evaluation of New Thiazole Derivatives as Anthelmintic Agents

2020 ◽  
Vol 13 (5) ◽  
pp. 5075-5081
Author(s):  
Sirisha Kalam ◽  
Sai Krishn G ◽  
Kumara Swamy D ◽  
Sai Santhoshi K ◽  
Durga Prasad K
Keyword(s):  
Anthelmintic Activity ◽  
Docking Studies ◽  
Molecular Property ◽  
Thiazole Derivatives ◽  
Pharmacological Agents ◽  
Design Synthesis ◽  
Standard Drug ◽  
Lipinski’S Rule ◽  
Mass Spectral ◽  
Tubulin Protein

Pharmacological agents that kills parasites are essential drugs in some tropical countries. In this study, a series of 2-amino substituted 4-phenyl thiazole derivatives (4a-e) have been synthesized by the conventional method. The thiazole derivatives were synthesized by three steps. The obtained five derivatives were purified by recrystallization using methanol as a solvent or column chromatography. They were characterized by melting point, TLC, FTIR, 1H NMR and MASS spectral data. Compounds 4a-e were evaluated in silico by using different software’s (Lipinski’s Rule of 5, OSIRIS molecular property explorer, Molsoft molecular property explorer, and PASS & docking studies). These compounds were then evaluated for their possible anthelmintic activity against Indian adult earth worms (Pherituma postuma). All the compounds displayed significant anthelmintic activity. Compound 4c and 4e were more potent compounds when compared with the standard drug (mebendazole). Molecular docking studies guided and proved the biological activity against beta tubulin protein (1OJ0). In conclusions, these new molecules have promising potential as anthelmintic for treatment of parasites.   

scholarly journals Learning chemistry: exploring the suitability of machine learning for the task of structure-based chemical ontology classification

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Janna Hastings ◽  
Martin Glauer ◽  
Adel Memariani ◽  
Fabian Neuhaus ◽  
Till Mossakowski
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Short Term Memory ◽  
Chemical Space ◽  
Chemical Data ◽  
Learning Approaches ◽  
Class Prediction ◽  
Chemical Structures ◽  
Chemical Ontology ◽  
Chemical Ontologies

AbstractChemical data is increasingly openly available in databases such as PubChem, which contains approximately 110 million compound entries as of February 2021. With the availability of data at such scale, the burden has shifted to organisation, analysis and interpretation. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of the large chemical space. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts. However, ChEBI is manually maintained and as such cannot easily scale to the full scope of public chemical data. There is a need for tools that are able to automatically classify chemical data into chemical ontologies, which can be framed as a hierarchical multi-class classification problem. In this paper we evaluate machine learning approaches for this task, comparing different learning frameworks including logistic regression, decision trees and long short-term memory artificial neural networks, and different encoding approaches for the chemical structures, including cheminformatics fingerprints and character-based encoding from chemical line notation representations. We find that classical learning approaches such as logistic regression perform well with sets of relatively specific, disjoint chemical classes, while the neural network is able to handle larger sets of overlapping classes but needs more examples per class to learn from, and is not able to make a class prediction for every molecule. Future work will explore hybrid and ensemble approaches, as well as alternative network architectures including neuro-symbolic approaches.

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