Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM

Cheng, Zhengjun; Zhang, Yuntao; Zhou, Changhong; Zhang, Wenjun; Gao, Shibo

doi:10.3390/ijms10083316

Cited by 9 publications

(9 citation statements)

References 54 publications

(60 reference statements)

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“…These descriptors cover topological and conformational spaces properties of compounds. Moreover, these descriptors have been successfully used in various QSAR/QSPR researches (21–24). In the prereduction step, the calculated descriptors are searched for constant values for all molecules and those detected descriptors are removed, and other calculated descriptors would be used as original variable set.…”

Section: Methodsmentioning

confidence: 99%

QSAR Models for Phosphoramidate Prodrugs of 2′‐Methylcytidine as Inhibitors of Hepatitis C Virus Based on PSO Boosting

2011

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In the current study, boosting regression has been proposed to model the activities of a series of phosphoramidate prodrugs of 2'-methylcytidine as inhibitors of hepatitis C virus. The stepwise multiple linear regression and particle swarm optimization strategies are used to select descriptors which are responsible for the inhibitory activity of these compounds. As comparisons to the boosting regression method, the multiple linear regression, back-propagation neural networks, and support vector machine have also been investigated. Experimental results have shown that the boosting can drastically enhance the generalization performance of individual multiple linear regression model and the particle swarm optimization-boosting method is a well-performing technique in quantitative structure-activity relationship studies superior to support vector machine. The squared correlation coefficient and standard deviation of the best model are 0.744 and 0.438 for the training set and 0.710 and 0.748 for the test set.

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Section: Methodsmentioning

confidence: 99%

QSAR Models for Phosphoramidate Prodrugs of 2′‐Methylcytidine as Inhibitors of Hepatitis C Virus Based on PSO Boosting

2011

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“…In another study that focused on the classification of serotonin subtype 1 receptor (5-HT 1A ) ligands, an Adaboost-SVM model containing seven descriptors was reported with 100% prediction accuracy. In the same study, other SVM models were developed with prediction accuracies ranging from 77.5 to 99.1% [243].…”

Section: Covid-19mentioning

confidence: 99%

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

Arabi

2021

Future Drug. Discov.

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The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

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“…Intracellular packaging and secretion of BDNF is altered by a common functional non-synonymous SNP within the 5′ region of the gene ( BDNF val66met). Links between this SNP and ASD are limited to small association and candidate gene studies with mixed results (Philippe et al, 2002; Nishimura et al, 2007; Cheng et al, 2009). Evidence of altered peripheral BDNF levels in ASD, and increased forebrain BDNF levels in autistic versus control brain, on post-mortem examination, have been found (Perry et al, 2001).…”

Section: Imaging-genetics Review In Asdmentioning

confidence: 99%

Imaging-Genetics in Autism Spectrum Disorder: Advances, Translational Impact, and Future Directions

Ameis¹,

Szatmári²

2012

Front. Psychiatry

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Autism Spectrum Disorder (ASD) refers to a group of heterogeneous neurodevelopmental disorders that are unified by impairments in reciprocal social communication and a pattern of inflexible behaviors. Recent genetic advances have resolved some of the complexity of the genetic architecture underlying ASD by identifying several genetic variants that contribute to the disorder. Different etiological pathways associated with ASD may converge through effects on common molecular mechanisms, such as synaptogenesis, neuronal motility, and axonal guidance. Recently, with more sophisticated techniques, neuroimaging, and neuropathological studies have provided some consistency of evidence that altered structure, activity, and connectivity within complex neural networks is present in ASD, compared to typically developing children. The imaging-genetics approach promises to help bridge the gap between genetic variation, resultant biological effects on the brain, and production of complex neuropsychiatric symptoms. Here, we review recent findings from the developing field of imaging-genetics applied to ASD. Studies to date have indicated that relevant risk genes are associated with alterations in circuits that mediate socio-emotional, visuo-spatial, and language processing. Longitudinal studies ideally focused on early development, in conjunction with investigation for gene–gene, and gene–environment interactions may move the promise of imaging-genetics in ASD closer to the clinical domain.

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Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM

Cited by 9 publications

References 54 publications

QSAR Models for Phosphoramidate Prodrugs of 2′‐Methylcytidine as Inhibitors of Hepatitis C Virus Based on PSO Boosting

QSAR Models for Phosphoramidate Prodrugs of 2′‐Methylcytidine as Inhibitors of Hepatitis C Virus Based on PSO Boosting

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

Imaging-Genetics in Autism Spectrum Disorder: Advances, Translational Impact, and Future Directions

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