Reduction of a biochemical network mathematical model by means of approximating activators and inhibitors as perfect inverse relationships [PDF]
arXiv, 2023 Models of biochemical networks are usually presented as connected graphs where vertices indicate proteins and edges are drawn to indicate activation or inhibition relationships. These diagrams are useful for drawing qualitative conclusions from the identification of topological features, for example positive and negative feedback loops.
arxiv
2D-Quantitative Structure-Activity Relationship and Molecular Docking study of triazole compounds against trichophytonrubrum [PDF]
, 2021 The antifungal agents are compounds having the ability to cure fungal infections which are caused by microscopic fungi and yeasts. There are several types of fungal infections, such as candida albicans, Cryptococcus neofomans, and trichophytonrubrum.
BOUAMRANE et al, Soukaina
core +2 more sources
Learning quantitative sequence-function relationships from massively parallel experiments [PDF]
, 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
Quantitative structure-activity relationship for antimalarial activity of artemisinin [PDF]
, 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
On the Relationship Between Explanation and Prediction: A Causal View [PDF]
arXiv, 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]
, 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]
arXiv, 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]
, 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]
, 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]
, 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