A Simple Clustering Technique To Improve QSAR Model Selection and Predictivity:  Application to a Receptor Independent 4D-QSAR Analysis of Cyclic Urea Derived Inhibitors of HIV-1 Protease

2003 ◽  
Vol 43 (6) ◽  
pp. 2180-2193 ◽  
Author(s):  
Craig L. Senese ◽  
A. J. Hopfinger
Keyword(s):  
Model Selection ◽  
Qsar Model ◽  
Qsar Analysis ◽  
Clustering Technique ◽  
Hiv 1

scholarly journals Application of Substituent Electronic Descriptors QSAR Model of 2-amino-6-arylsulfonylbenzonitriles as HIV-1 Reverse Transcriptase Inhibitors based on the MOLMAP Approach

2018 ◽  
pp. 1-11
Author(s):  
Razieh Sabet ◽  
Maryam Sabet
Keyword(s):  
Reverse Transcriptase ◽  
Three Dimensional ◽  
Quantitative Relationship ◽  
Qsar Model ◽  
Partial Least Square ◽  
Least Square ◽  
Qsar Modeling ◽  
Qsar Analysis ◽  
Dimensional Array ◽  
Hiv 1

The HIV-1 reverse transcriptase (RT) is a major target for drug development. Inhibition of this enzyme has been one of the primary therapeutic strategies in suppressing the replication of HIV-1. A series of 2-amino-6-arylsulfonylbenzonitrile derivatives were subjected to quantitative structure-activity relationship (QSAR) analysis. Very recently, we proposed the use of substituent electronic descriptors (SED) instead of the electronic descriptors of whole molecules as new and expedite source of electronic descriptors. In this study, we used SED parameters in QSAR modeling of anti HIV-1 activity of 6-arylsulfonylbenzonitrile derivatives. In SED methodology produces a vector of electronic descriptors for each substituent and thus a matrix of SED is generated for each molecule. Consequently, a three-dimensional array is obtained by staking the data matrices of different molecules beside each other. As a novel multiway data analysis method, molecular maps of atom-level properties (MOLMAP) approach was also used to transfer a three-dimensional array of SED descriptors into new two-dimensional parameters using Kohonen network, following by genetic algorithm-based partial least square(GA-PLS) to connect a quantitative relationship between the Kohonen scores and biological activity.In unfolding data, HOMO1, HOMOB1, SOFB1 and EPHA4 represent the most important indices on QSAR equation derived by PLS analysis. Accurate QSAR models were obtained by both approaches. The resulted GA-PLS model of MOLMAP approach possessed high statistical quality r2= 0.83 and q2=0.70. It could explain and predict about 70% of variances in the anti-HIV1 inhibitory activity of the studied molecules. However, the superiority of three-way analysis of SED parameters based on MOLMAP approach with respect to simple unfolding was obtained.

QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

2018 ◽  
Vol 21 (3) ◽  
pp. 204-214 ◽  
Author(s):  
Vesna Rastija ◽  
Maja Molnar ◽  
Tena Siladi ◽  
Vijay Hariram Masand
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Activities ◽  
Dipicolinic Acid ◽  
External Validation ◽  
Qsar Model ◽  
Internal Validation ◽  
Qsar Analysis ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis

2019 ◽  
Vol 15 (6) ◽  
pp. 588-601 ◽  
Author(s):  
Mahmoud A. Al-Sha'er ◽  
Rua'a A. Al-Aqtash ◽  
Mutasem O. Taha
Keyword(s):  
Virtual Screening ◽  
Inflammatory Diseases ◽  
Qsar Model ◽  
Qsar Analysis ◽  
Pharmacophore Modelling ◽  
Physicochemical Descriptors ◽  
Qsar Modelling ◽  
Ic50 Values ◽  
Phosphoinositide 3 Kinase ◽  
Pharmacophore Models

<P>Background: PI3K&#948; is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3K&#948; inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3K&#948; binding. </P><P> Methods: Seventeen PI3K&#948; crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors). </P><P> Results: The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values. </P><P> Conclusion: Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3K&#948; inhibitors.</P>

scholarly journals Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 357
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Siddhartha Akasapu ◽  
Sumit O. Bajaj ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Simulation Analysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Main Protease

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

Receptor-Independent 4D-QSAR Analysis of a Set of Norstatine Derived Inhibitors HIV-1 Protease.

ChemInform ◽  
2003 ◽  
Vol 34 (42) ◽  
Author(s):  
Craig L. Senese ◽  
A. J. Hopfinger
Keyword(s):  
Qsar Analysis ◽  
Hiv 1

scholarly journals 2D QSAR Approach to Develop Newer Analogs as Melatonin Receptor Agonist

2016 ◽  
Vol 15 (1) ◽  
pp. 7-19
Author(s):  
Supriyo Saha ◽  
Mrityunjoy Acharya ◽  
Prinsa
Keyword(s):  
Mahalanobis Distance ◽  
Receptor Agonist ◽  
Statistical Parameter ◽  
Qsar Model ◽  
Randomization Test ◽  
Melatonin Receptor ◽  
Distance Method ◽  
Qsar Analysis ◽  
Acceptable Model ◽  
Data Points

QSAR analysis was performed using 20 MT1 agonist and 18 MT2 agonist. MODI was 0.6373 in case of MT1 agonist and 0.6299 in case of MT2 agonist. QSAR model for MT1 receptor agonist was pKd = 16.24793(+/- 0.93539) +1.0924(+/-0.18831) ALogP -0.11399(+/-0.01383) apol +0.59876(+/-0.16599) C2SP3 -10.29435(+/-2.81413) E3p and for MT2 receptor agonist was pKd = 6.38692(+/-0.91098) +0.87139(+/-0.20258) ALogP -0.0351(+/-0.00542) AMR +3.33079 (+/-0.80377) SpMin6_Bhm +146.76208(+/-28.14492) VE2_Dt with statistical parameter as Q^2:0.79167, r^2 :0.88878, |r0^2-r'0^2|:0.04633,k:1.03159, [(r^2-r0^2)/r^2]:0.01013, k':0.96695, [(r^2- '0^2)/r^2]:0.06226 and Q^2:0.81401, r^2:0.97384, |r0^2-r'0^2|:0.1039, k:0.98543, [(r^2-r0^2)/r^2]:0.08048, k':1.01351, [(r^2-r'0^2)/r^2]:0.18717 respectively; comply with the Golbraikh and Tropsha acceptable model criteria. The results from MLR Y Randomization test in case of MT1 agonist was cRp^2: 0.7665 and MT2 agonist was cRp^2: 0.7284. Applicability domain were identified by Euclidean and Mahalanobis Distance Method. Finally it was clear that all the predicted data are inside the area of observed data points and also some data are purely overlapped.Dhaka Univ. J. Pharm. Sci. 15(1): 7-19, 2016 (June)

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