Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors. 2. Application of the Novel 3D Molecular Descriptors to QSAR/QSPR Studies

2002 ◽  
Vol 42 (3) ◽  
pp. 693-705 ◽  
Author(s):  
Viviana Consonni ◽  
Roberto Todeschini ◽  
Manuela Pavan ◽  
Paola Gramatica
Keyword(s):  
Molecular Descriptors ◽  
Diversity Analysis ◽  
The Novel ◽  
Structure Response ◽  
Getaway Descriptors

Towards Efficient Generation, Correction and Properties Control of Unique Drug-like Structures

2019 ◽  
Author(s):  
Maksym Druchok ◽  
Dzvenymyra Yarish ◽  
Oleksandr Gurbych ◽  
Mykola Maksymenko
Keyword(s):  
Deep Neural Network ◽  
Molecular Descriptors ◽  
Material Design ◽  
Molecular Structures ◽  
The Novel ◽  
Efficient Design ◽  
Continuous Space ◽  
Multi Stage ◽  
Synthetic Accessibility ◽  
Novel Structures

<div> <div> <div> <p>Efficient design and screening of the novel molecules is a major challenge in drug and material design. This report focuses on a multi-stage pipeline in which several deep neural network (DNN) models are combined to map discrete molecular representations into continuous vector space to later generate from it new molecular structures with desired properties. Here the Attention-based Sequence-to-Sequence model is added to “spellcheck” and correct generated structures while the oversampling in the continuous space allows generating candidate structures with desired distribution for properties and molecular descriptors even for small reference datasets. We further use computer simulation to validate the desired properties in the numerical experiment. With the focus on the drug design, such pipeline allows generating novel structures with control of SAS (Synthetic Accessibility Score) and a series of ADME metrics that assess the drug-likeliness. </p> </div> </div> </div>

Use of alignment-free molecular descriptors in diversity analysis and optimal sampling of molecular libraries

2000 ◽  
Vol 6 (2) ◽  
pp. 135-147 ◽  
Author(s):  
Fabien Fontaine ◽  
Manuel Pastor ◽  
Hugo Gutiérrez-de-Terán ◽  
Juan J. Lozano ◽  
Ferran Sanz
Keyword(s):  
Molecular Descriptors ◽  
Diversity Analysis ◽  
Optimal Sampling ◽  
Alignment Free ◽  
Molecular Libraries

Towards Efficient Generation, Correction and Properties Control of Unique Drug-like Structures

2019 ◽  
Author(s):  
Maksym Druchok ◽  
Dzvenymyra Yarish ◽  
Oleksandr Gurbych ◽  
Mykola Maksymenko
Keyword(s):  
Deep Neural Network ◽  
Molecular Descriptors ◽  
Material Design ◽  
Molecular Structures ◽  
The Novel ◽  
Efficient Design ◽  
Continuous Space ◽  
Multi Stage ◽  
Synthetic Accessibility ◽  
Novel Structures

<div> <div> <div> <p>Efficient design and screening of the novel molecules is a major challenge in drug and material design. This report focuses on a multi-stage pipeline in which several deep neural network (DNN) models are combined to map discrete molecular representations into continuous vector space to later generate from it new molecular structures with desired properties. Here the Attention-based Sequence-to-Sequence model is added to “spellcheck” and correct generated structures while the oversampling in the continuous space allows generating candidate structures with desired distribution for properties and molecular descriptors even for small reference datasets. We further use computer simulation to validate the desired properties in the numerical experiment. With the focus on the drug design, such pipeline allows generating novel structures with control of SAS (Synthetic Accessibility Score) and a series of ADME metrics that assess the drug-likeliness. </p> </div> </div> </div>

scholarly journals MLR-Based QSAR Models for Predicting Inhibitory Activity of Reverse Transcriptase by HEPT Derivatives using GETAWAY Descriptors

KIMIKA ◽  
2013 ◽  
Vol 24 (2) ◽  
pp. 2-17
Author(s):  
Alex A. Tardaguila ◽  
Jennifer C. Sy ◽  
Marielyn R. Omañada ◽  
Eric R. Punzalan
Keyword(s):  
Reverse Transcriptase ◽  
Correlation Coefficient ◽  
Predictive Power ◽  
Molecular Descriptors ◽  
External Validation ◽  
Residual Analysis ◽  
Training Set ◽  
Qsar Models ◽  
Getaway Descriptors ◽  
Hept Derivatives

In this study, quantitative structure-activity relationship (QSAR) models for non-nucleoside reverse transcriptase inhibitors based on 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine (HEPT) derivatives were generated. The structures of the compounds and their activities were obtained from the literature. The data set were divided into two sets: training set (N=91) and validating set (N=10). All 3-D structures of these inhibitors were optimized by semi-empirical method, AM1 prior to calculations of 3-D molecular descriptors, GETAWAY. Multiple linear regression (MLR) using stepwise method was applied to determined significant descriptors. Out of 197 GETAWAY descriptors, 4-14 molecular descriptors have significant relationships with the activities (expressed as log (1/EC50)) of HEPT. The MLR method generated 14 models. The predictive power of these models were evaluated internally by applying the following statistical parameters for the training set and test set: root-mean-square error for prediction (RMSE), correlation coefficient (R), squared correlation coefficient (R2), adjusted squared correlation coefficient (R2adj), difference between R2 and R2adj (R2 – R2adj), squared cross-validation correlation coefficient (Q2). External validation was performed by employing Golbraikh and Tropsha criteria. Moreover, residual analysis was performed. Internal validation of Model XX (N = 91) revealed that it has the highest predictive power (RMSE = 0.4288, R = 0.9393, R2 = 0.882, R2adj = 0.8620, R2 - R2adj = 0.0203, Q2 = 0.8317). However, external validation (using the validating set, N=10) showed that Model XII has the highest predictive power (R2 = 0.961, R20 = 0.9565, k = 0.8648, k’ = 0.9800, [R2 - R20] = 0.0066, [R2 - R20] /R2 = 0.0069, R2pred = 0.9481) based on Golbraikh and Tropsha criteria. Residual analysis confirmed that both models are valid.

The novel steroidal glycoside from the dried bulb of Allium Macrostemon Bunge inhibits platelet activation

2010 ◽  
Vol 34 (8) ◽  
pp. S33-S33
Author(s):  
Wenchao Ou ◽  
Haifeng Chen ◽  
Yun Zhong ◽  
Benrong Liu ◽  
Keji Chen
Keyword(s):  
Platelet Activation ◽  
The Novel ◽  
Steroidal Glycoside ◽  
Allium Macrostemon
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