Prediction of novel and selective TNF-alpha converting enzyme (TACE) inhibitors and characterization of correlative molecular descriptors by machine learning approaches

Ye, Cong; Yang, Xue-Gang; Lv, Wei; Yang, Xue

doi:10.1016/j.jmgm.2009.08.001

Cited by 20 publications

(12 citation statements)

References 69 publications

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“…[78][79][80] The robustness of a derived model is extremely important while validating the utility of descriptors in deducing the pharmacophoric qualities of the inhibitors. [83][84][85][86][87] It is a supervised learning method, and support vectors are used with suitable kernel functions. A brief account of these methods is presented herewith.…”

Section: Molecular Structural Data Setmentioning

confidence: 99%

In silico de novo design of novel NNRTIs: a bio-molecular modelling approach

Jain¹,

Gupta

Sapre³

et al. 2015

RSC Adv.

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Six novel non-nucleoside reverse transcriptase inhibitors exhibiting high efficacy are designed using in silico mathematical modelling techniques and the results are validated using a docking technique. An in silico assessment of interaction potential and structural requirements of 5-alkyl-2-alkylamino-6-(2,6difluorophenylalkyl)-3,4-dihydropyrimidin-4(3H)-one (DABO) analogues in the non-nucleoside inhibitor binding pocket is also performed. Efficient use of 3D-pharamacophoric (S ALL , HD ALL , HA ALL and R ALL ) and 3D-averaged alignment (C log P and dipole moment) descriptors is made in this study. The chemometric analyses, using support vector machine, back propagation neural network and multiple linear regression, are performed. The relative potentials of these chemometric methods is also assessed and the results,indicates that SVM describes the relationship between the descriptors and inhibitory activity in a better manner. The results also suggest that there is a non-linear relationship between the descriptors and inhibitory activity. The study further suggests that isopropyl/propenyl groups as R and R 0 , oxobutyl group as X and di or tri-substitution as R 00 are the best suited substituents for exhibiting better inhibitory activity.View Article Online a pEC 50 ¼ Àlog EC 50 (where EC 50 is the effective concentration of a compound required to activate 50% protection of MT-4 cell against the cytopathic effect of HIV-1). The data points a represent the test set and b as Outliers.This journal is

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Section: Molecular Structural Data Setmentioning

confidence: 99%

In silico de novo design of novel NNRTIs: a bio-molecular modelling approach

Jain¹,

Gupta

Sapre³

et al. 2015

RSC Adv.

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“…Cong et al firstly develop four machine learning models, including SVM, k -nearest neighbor ( k -NN), back-propagation neural network and C4.5 decision tree, for screening the inhibitory efficiency of various candidate compounds on TACE. After that, the authors evaluated the reliability of the established model by using two separate methods: 5-fold cross-validation and independent evaluation ( Cong et al, 2009 ). We expect that the above updated multiple mechanical learning models can guide the discovery of more candidate compounds or pharmacodynamic groups acting on RA related biomarkers in the future, and provide an efficient and non-blind pre-screening for the development of RA treatment drugs.…”

Section: Introductionmentioning

confidence: 99%

Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis

Wang

Hou

et al. 2021

Front. Pharmacol.

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Rheumatoid arthritis (RA), an autoimmune disease of unknown etiology, is a serious threat to the health of middle-aged and elderly people. Although western medicine, traditional medicine such as traditional Chinese medicine, Tibetan medicine and other ethnic medicine have shown certain advantages in the diagnosis and treatment of RA, there are still some practical shortcomings, such as delayed diagnosis, improper treatment scheme and unclear drug mechanism. At present, the applications of artificial intelligence (AI)-based deep learning and cloud computing has aroused wide attention in the medical and health field, especially in screening potential active ingredients, targets and action pathways of single drugs or prescriptions in traditional medicine and optimizing disease diagnosis and treatment models. Integrated information and analysis of RA patients based on AI and medical big data will unquestionably benefit more RA patients worldwide. In this review, we mainly elaborated the application status and prospect of AI-assisted deep learning and cloud computation-oriented western medicine and traditional medicine on the diagnosis and treatment of RA in different stages. It can be predicted that with the help of AI, more pharmacological mechanisms of effective ethnic drugs against RA will be elucidated and more accurate solutions will be provided for the treatment and diagnosis of RA in the future.

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“…However, these molecular computation approaches were generally preprogrammed, rulebased, or used logic gates for simple forms of computations which may not exceed the ability of reflex action from the perspective of intelligence. Such as in the work of [15,16] where a perceptron algorithm was designed with a weighted sum operation and [17] where a feedforward and recurrent neural network was constructed with cascading nodes using DNA hybridization; although these studies realized pre-defined perceptrons, the idea of learning, where computational weight parameters were updated to train the model was lacking.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithm for DNA-Based Molecular Ligands

Baek¹,

Lee²,

Lee³

et al. 2019

Preprint

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Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability that may be essential in developing molecular systems that function in dynamic environments. Here, we introduce an in vitro molecular algorithm that ‘learns’ molecular models from training data, opening the possibility of ‘machine learning’ in wet molecular systems. Our algorithm enables enzymatic weight update by targeting internal loop structures in DNA and ensemble learning, based on the hypernetwork model. This novel approach allows massively parallel processing of DNA with enzymes for specific structural selection for learning in an iterative manner. We also introduce an intuitive method of DNA data construction to dramatically reduce the number of unique DNA sequences needed to cover the large search space of feature sets. By combining molecular computing and machine learning the proposed algorithm makes a step closer to developing molecular computing technologies for future access to more intelligent molecular systems.

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Prediction of novel and selective TNF-alpha converting enzyme (TACE) inhibitors and characterization of correlative molecular descriptors by machine learning approaches

Cited by 20 publications

References 69 publications

In silico de novo design of novel NNRTIs: a bio-molecular modelling approach

In silico de novo design of novel NNRTIs: a bio-molecular modelling approach

Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis

Machine Learning Algorithm for DNA-Based Molecular Ligands

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