Results 51 to 60 of about 1,549,507 (219)

Learning quantitative sequence-function relationships from massively parallel experiments [PDF]

open access: yes, 2015
A fundamental aspect of biological information processing is the ubiquity of sequence-function relationships -- functions that map the sequence of DNA, RNA, or protein to a biochemically relevant activity. Most sequence-function relationships in biology are quantitative, but only recently have experimental techniques for effectively measuring these ...
arxiv   +1 more source

Assessing the Effects of Alloxydim Phototransformation Products by QSAR Models and a Phytotoxicity Study

open access: yesMolecules, 2018
Once applied, an herbicide first makes contact with leaves and soil. It is known that photolysis can be one of the most important processes of dissipation of herbicides in the field.
Juan J. Villaverde   +4 more
doaj   +1 more source

Quantitative structure-activity relationship for antimalarial activity of artemisinin [PDF]

open access: yes, 2010
The increase in resistance to older drugs and the emergence of new types of infection have created an urgent need for discovery and development of new compounds with antimalarial activity.
Hasan, Mohamed Noor   +1 more
core  

LIPOPHILICITY AND ANTIFUNGAL ACTIVITY OF SOME 2-SUBSTITUTED BENZIMIDAZOLE DERIVATIVES [PDF]

open access: yesChemical Industry and Chemical Engineering Quarterly, 2011
In the present paper, the antifungal activity of some 2-methyl and 2-amino-benzimidazole derivatives was evaluated against yeast Saccharomyces cere¬visiae. The tested compounds displayed in vitro antifungal activity and mini¬mum inhibitory concentration (
SANJA O. PODUNAVAC-KUZMANOVIĆ   +1 more
doaj  

On the Relationship Between Explanation and Prediction: A Causal View [PDF]

open access: yesarXiv, 2022
Being able to provide explanations for a model's decision has become a central requirement for the development, deployment, and adoption of machine learning models. However, we are yet to understand what explanation methods can and cannot do. How do upstream factors such as data, model prediction, hyperparameters, and random initialization influence ...
arxiv  

A practical overview of quantitative structure-activity relationship [PDF]

open access: yes, 2009
Quantitative structure-activity relationship (QSAR) modeling pertains to the construction of predictive models of biological activities as a function of structural and molecular information of a compound library.
Isarankura-Na-Ayudhya, Chartchalerm   +3 more
core  

On the Borel-Cantelli Lemmas, the Erdős-Rényi Theorem, and the Kochen-Stone Theorem [PDF]

open access: yesarXiv, 2020
In this paper we present a quantitative analysis of the first and second Borel-Cantelli Lemmas and of two of their generalisations: the Erd\H{o}s-R\'enyi Theorem, and the Kochen-Stone Theorem. We will see that the first three results have direct quantitative formulations, giving an explicit relationship between quantitative formulations of the ...
arxiv  

Second-generation nitazoxanide derivatives: thiazolides are effective inhibitors of the influenza A virus [PDF]

open access: yes, 2018
Aim: The only small molecule drugs currently available for treatment of influenza A virus (IAV) are M2 ion channel blockers and sialidase inhibitors. The prototype thiazolide, nitazoxanide, has successfully completed Phase III clinical trials against ...
Andrew V Stachulski   +17 more
core   +1 more source

Quantile Regression Neural Networks Based Prediction of Drug Activities [PDF]

open access: yes, 2014
QSAR (quantitative structure-activity relationship) modeling is one of the well developed areas in drug development through computational chemistry. Similar molecules with just a slight variation in their structure can have quit different biological ...
El-Telbany, Mohammed E.
core   +2 more sources

Pattern recognition system based on support vector machines: HIV-1 integrase inhibitors application [PDF]

open access: yes, 2013
Support Vector Machines (SVM) represent one of the most promising Machine Learning (ML) tools that can be applied to develop a predictive Quantitative Structure-Activity Relationship (QSAR) models using molecular descriptors.
Darnag, Rachid   +2 more
core   +1 more source

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