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Predicting the possibility of two newly isolated phenetheren ring containing compounds from Aristolochia manshuriensis as CDK2 inhibitors.

ABSTRACT: UNLABELLED:Aristolochia manshuriensis has been used for centuries in Chinese medicinal system for their versatile medicinal uses. Recent studies have revealed two new aristolactames (compound A and B) with ?-lactame ring fused with the phenentherene ring as potent inhibitors of human Cycline Dependent Kinase2 (CDK2). Studies on aristolactames and related compounds claim for their CDK2 inhibition without delineating the involved mechanism and structural basis of interaction. Molecular structural model was used to we propose a structural basis of CDK2 inhibition. We showed that these compounds (A and B) can successfully dock into the inhibitor binding pockets of human CDK2. Predicted binding affinities are comparable to known inhibitors of CDK2. Results were in agreement with the earlier biochemical studies. Hence, suggest that studied compounds A and B can be a promising scaffold for rational design of novel and potential drugs against cancer. ABBREVIATIONS:CDK2 - cyclin-dependent kinase 2, OLO - Olomoucine, NW1 - Cyclohexylmethyloxy-5-Nitroso-Pyrimidine- 2, 4-Diamine, CMG - 6-O-Cyclohexylmethyl Guanine.

SUBMITTER: Alshatwi AA

PROVIDER: S-EPMC3280487 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Predicting the possibility of two newly isolated phenetheren ring containing compounds from Aristolochia manshuriensis as CDK2 inhibitors.

Alshatwi Ali Abdullah AA Hasan Tarique Noorul TN Syed Naveed Ahmed NA Shafi Gowhar G

Bioinformation 20111210 7

<h4>Unlabelled</h4>Aristolochia manshuriensis has been used for centuries in Chinese medicinal system for their versatile medicinal uses. Recent studies have revealed two new aristolactames (compound A and B) with γ-lactame ring fused with the phenentherene ring as potent inhibitors of human Cycline Dependent Kinase2 (CDK2). Studies on aristolactames and related compounds claim for their CDK2 inhibition without delineating the involved mechanism and structural basis of interaction. Molecular str ...[more]

PMID: 22355233

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Project description:In recent years, the number of vulnerabilities discovered and publicly disclosed has shown a sharp upward trend. However, the value of exploitation of vulnerabilities varies for attackers, considering that only a small fraction of vulnerabilities are exploited. Therefore, the realization of quick exclusion of the non-exploitable vulnerabilities and optimal patch prioritization on limited resources has become imperative for organizations. Recent works using machine learning techniques predict exploited vulnerabilities by extracting features from open-source intelligence (OSINT). However, in the face of explosive growth of vulnerability information, there is room for improvement in the application of past methods to multiple threat intelligence. A more general method is needed to deal with various threat intelligence sources. Moreover, in previous methods, traditional text processing methods were used to deal with vulnerability related descriptions, which only grasped the static statistical characteristics but ignored the context and the meaning of the words of the text. To address these challenges, we propose an exploit prediction model, which is based on a combination of fastText and LightGBM algorithm and called fastEmbed. We replicate key portions of the state-of-the-art work of exploit prediction and use them as benchmark models. Our model outperforms the baseline model whether in terms of the generalization ability or the prediction ability without temporal intermixing with an average overall improvement of 6.283% by learning the embedding of vulnerability-related text on extremely imbalanced data sets. Besides, in terms of predicting the exploits in the wild, our model also outperforms the baseline model with an F1 measure of 0.586 on the minority class (33.577% improvement over the work using features from darkweb/deepweb). The results demonstrate that the model can improve the ability to describe the exploitability of vulnerabilities and predict exploits in the wild effectively.

Dataset Information

Predicting the possibility of two newly isolated phenetheren ring containing compounds from Aristolochia manshuriensis as CDK2 inhibitors.

Publications

Predicting the possibility of two newly isolated phenetheren ring containing compounds from Aristolochia manshuriensis as CDK2 inhibitors.

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