QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIP METHODS: PERSPECTIVES ON DRUG DISCOVERY AND TOXICOLOGY

2003 ◽  
Vol 22 (8) ◽  
pp. 1666 ◽  
Author(s):  
Roger Perkins ◽  
Hong Fang ◽  
Weida Tong ◽  
William J. Welsh
Keyword(s):  
Drug Discovery ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity

scholarly journals Imputation versus prediction: applications in machine learning for drug discovery

2020 ◽  
Vol 2 (2) ◽  
pp. FDD38 ◽  
Author(s):  
Benedict W J Irwin ◽  
Samar Mahmoud ◽  
Thomas M Whitehead ◽  
Gareth J Conduit ◽  
Matthew D Segall
Keyword(s):  
Drug Discovery ◽  
Missing Values ◽  
Value Added ◽  
Predictive Modelling ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Modelling Techniques

Imputation is a powerful statistical method that is distinct from the predictive modelling techniques more commonly used in drug discovery. Imputation uses sparse experimental data in an incomplete dataset to predict missing values by leveraging correlations between experimental assays. This contrasts with quantitative structure–activity relationship methods that use only descriptor – assay correlations. We summarize three recent imputation strategies – heterogeneous deep imputation, assay profile methods and matrix factorization – and compare these with quantitative structure–activity relationship methods, including deep learning, in drug discovery settings. We comment on the value added by imputation methods when used in an ongoing project and find that imputation produces stronger models, earlier in the project, over activity and absorption, distribution, metabolism and elimination end points.

Cloud 3D-QSAR: a web tool for the development of quantitative structure–activity relationship models in drug discovery

2020 ◽  
Author(s):  
Yu-Liang Wang ◽  
Fan Wang ◽  
Xing-Xing Shi ◽  
Chen-Yang Jia ◽  
Feng-Xu Wu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Activity Prediction ◽  
Structure Generation ◽  
Structure Activity ◽  
Prediction Functions

Abstract Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure–Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/.

scholarly journals Quantitative Structure Activity Relationship for Drug Discovery

2019 ◽  
Vol 8 (9S) ◽  
pp. 607-610
Keyword(s):  
Drug Discovery ◽  
Biological Activities ◽  
Principal Component ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Qsar Models ◽  
Pharmaceutical Industries

Quantitative structure-activity relationship (QSAR), gives useful information for drug design and medicinal chemistry. QSAR is a method used to anticipate the organic reaction of a molecule by developing equations which use descriptors calculated from its compounds. The molecular descriptors vary in complexity. A time consuming and expensive process for pharmaceutical industries is drug discovery. An inspiration driving these QSAR models is to help revive the revelation of molecular drug candidates through minimized test work and to bring a drug to market faster. To obtain sorted features principal component analysis is used. The biological activities of the test set are determined by training the neural network using training set. By predicting the activities it can be known whether the drug is close to the target or not.

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