Results 11 to 20 of about 1,617,247 (300)

Predicting Antimicrobial Peptide Activity: A Machine Learning-Based Quantitative Structure–Activity Relationship Approach [PDF]

Pharmaceutics
Background: Peptides are a class of molecules that can be presented as good antimicrobials and with mechanisms that avoid resistance, and the design of peptides with good activity can be complex and laborious.
Eliezer I. Bonifacio-Velez de Villa, María E. Montoya-Alfaro, Luisa P. Negrón-Ballarte, Christian Solis-Calero   +3 more
doaj   +2 more sources

AdapTor: Adaptive Topological Regression for quantitative structure–activity relationship modeling [PDF]

Journal of Cheminformatics
Quantitative structure–activity relationship (QSAR) modeling has become a critical tool in drug design. Recently proposed Topological Regression (TR), a computationally efficient and highly interpretable QSAR model that maps distances in the chemical ...
Yixiang Mao, Souparno Ghosh, Ranadip Pal
doaj   +2 more sources

Efficiency and Quantitative Structure-Activity Relationship of Monoaromatics Oxidation by Quinone-Activated Persulfate [PDF]

Frontiers in Chemistry, 2021
Quinones and quinone-containing organics have potential of activating persulfate to produce sulfate radical. In this work, the optimal condition for quinone activation of persulfate was investigated.
Jiaqi Shi, Tao Long, Yuxuan Zhou, Lei Wang, Cuiping Jiang, Dongguo Pan, Xin Zhu   +6 more
doaj   +2 more sources

Platinum(IV) compounds as potential drugs: a quantitative structure-activity relationship study [PDF]

BioImpacts, 2023
Introduction: Machine learning methods, coupled with a tremendous increase in computer power in recent years, are promising tools in modern drug design and drug repurposing.
Jurica Novak, Alena R. Zykova, Vladimir A. Potemkin, Vladimir V. Sharutin, Olga K. Sharutina   +4 more
doaj   +2 more sources

Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models [PDF]

iScience, 2021
Summary: Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features.
Jiashun Mao, Javed Akhtar, Xiao Zhang, Liang Sun, Shenghui Guan, Xinyu Li, Guangming Chen, Jiaxin Liu, Hyeon-Nae Jeon, Min Sung Kim, Kyoung Tai No, Guanyu Wang   +11 more
doaj   +2 more sources

Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads

Molecules, 2021
In the present endeavor, for the dataset of 219 in vitro MDA-MB-231 TNBC cell antagonists, a (QSAR) quantitative structure–activity relationships model has been carried out.
Ajaykumar Gandhi, Vijay Masand, Magdi E. A. Zaki, Sami A. Al-Hussain, Anis Ben Ghorbal, Archana Chapolikar   +5 more
doaj   +1 more source

Calculation of Quantitative Structure-Activity Relationship Descriptors of Artemisinin Derivatives [PDF]

Mongolian Journal of Biological Sciences, 2008
Quantitative structure-activity relationships are based on the construction of predictive models using a set of known molecules and associated activity value.
Jambalsuren Bayarmaa, Vladimir Frecer
doaj   +1 more source

Synthesis, Characterization, and Antifungal Evaluation of Thiolactomycin Derivatives

Engineering, 2020
5-Substituted benzylidene 3-acylthiotetronic acids are antifungal. A series of 3-acylthiotetronic acid derivatives with varying substitutions at the 5-position were designed, synthesized, and characterized, based on the binding pose of 3-acyl thiolactone
Pei Lv, Yiliang Chen, Dawei Wang, Xiangwei Wu, Qing X. Li, Rimao Hua   +5 more
doaj   +1 more source

Application of Hansch’s Model to Capsaicinoids and Capsinoids: A Study Using the Quantitative Structure−Activity Relationship. A Novel Method for the Synthesis of Capsinoids [PDF]

, 2010
We describe a synthetic approach for two families of compounds, the capsaicinoids and capsinoids, as part of a study of the quantitative relationship between structure and ...
Appendino G., Barbero G. F., Barbero G. F., Bennett D. J., Careaga M., Chou J., Constant H. L., Gannett P. M., Hancock C. R., Henderson D. E., Iwai K., Kaga H., Kagan I. A., Kim C. S., Kobata K., Kobata K., Lipinski C. A., Macho A., Macias F. A., Mannhold R., Morris G. C., Nitsch J. P., Ohnuki K., Ohnuki K., Perucka I., Rekker R. F., Rosa A., Sancho R., Surh Y. J., Sutoh K., Torregiani E., Unnikrishnan M. C., Zewdie Y.   +32 more
core   +3 more sources

Dataset of 2-(2-(4-aryloxybenzylidene) hydrazinyl) benzothiazole derivatives for GQSAR of antitubercular agents

Data in Brief, 2017
Fragment based Quantitative structure activity relationship (QSAR) analysis on reported 25 2-(2-(4-aryloxybenzylidene) hydrazinyl) benzothiazole dataset as antitubercular agents were carried out.
Amit S. Tapkir, Sohan S. Chitlange, Ritesh P. Bhole   +2 more
doaj   +1 more source