727-731In the present article, k nearest neighbour molecular field analysis (kNN-MFA) method was used to develop a three dimensional quantitative structure activity relationship (3D-QSAR) model. In this study 37 derivatives of quinoxaline having antimalarial activity were used. Sphere exclusion (SE) algorithm was used to create the biological activity data set in to into training and test set. For model generation kNN-MFA method has coupled with stepwise, simulated annealing and genetic algorithm this method provides various models, in which the most significant model developed by stepwise backward-forward method with predictive internal q2=0.7589 and external predictivity (pred_r2 = 0.4752). In the presented model electrostatic descriptors play crucial role for activity. Electrostatic descriptor (E_137) indicates regions in which electron withdrawing groups are favourable and descriptor (E_939) represents electron rich or electron donating groups are advantageous in particular region. The counter map/ plot of this model further helps to understand the relationship of structural feature of derivative of quinoxaline and its biological activity this would be applied for designing of new potent antimalarial containing quinoxaline as lead

Jha, Arvind Kumar

Mishra, Achal

Vaishnav, Yogesh

Verma, Shekhar

NOPR

Indian Journal of Chemistry Vol. 60B, May 2021, pp.  727-731 3D QSAR analysis on quinoxaline derivatives as anti-malarial using K-nearest neighbour molecular field analysis Achal Mishra*a, Yogesh Vaishnava, Arvind Kumar Jhaa & Shekhar Vermab a Faculty of Pharmaceutical Sciences Shri Shankaracharya Technical Campus, Bhilai 491 001, India b University College of Pharmacy, Pt Deendayal Upadhyay Memorial Health Sciences and Ayush University, Raipur 493 661, India E-mail: achal.mishra03@gmail.comReceived 20 August 2020; accepted (revised) 3 March 2021 In the present article, k nearest neighbour molecular field analysis (kNN-MFA) method was used to develop a three dimensional quantitative structure activity relationship (3D-QSAR) model. In this study 37 derivatives of quinoxaline having antimalarial activity were used. Sphere exclusion (SE) algorithm was used to create the biological activity data set in to into training and test set. For model generation kNN-MFA method has coupled with stepwise, simulated annealing and genetic algorithm this method provides various models, in which the most significant model developed by stepwise backward-forward method with predictive internal q2=0.7589 and external predictivity (pred_r2 = 0.4752). In the presented model electrostatic descriptors play crucial role for activity. Electrostatic descriptor (E_137) indicates regions in which electron withdrawing groups are favourable and descriptor (E_939) represents electron rich or electron donating groups are advantageous in particular region. The counter map/ plot of this model further helps to understand the relationship of structural feature of derivative of quinoxaline and its biological activity this would be applied for designing of new potent antimalarial containing quinoxaline as lead. Keywords: 3D-QSAR, kNN-MFA, antimalarial, quinoxaline derivatives, sphere exclusion (SE) algorithm Malaria is a life intimidating ailment originated by means of parasites and later on transmitted to individuals through infected female anopheles mosquitoes bites. This infection has globally important as in many regions of the world it is a cause of mortality and morbidity and creates social and economic loss to those regions. WHO report of 2019 clearly shows that in 2018 around 228 million causes are reported worldwide and 405000 deaths from this. Approximately 47% P. vivax cases are reported in India in total 53% of globally. Most of the drugs present in the market to treat this infection now are less active or not active against most of the malarial parasites because of resistance developed by parasites, so there is an urgent need to develop new molecules to restrict the development and existence of this infection1. Quantitative structure-activity relationships (QSAR) is a hypothetical method which can helps to correlate the biological properties or activates with descriptors that should be mathematically calculated and this relationship is given by some mathematical equations2. Some quinoxaline derivatives show good activity against some malarial parasites although various QSAR studies were reported and gives various models for antimalarial drug designing but nitrogen containing quinoxaline heterocyclic compounds are not properly checked for QSAR model development. In the present work a 3D-QSAR has to be performed for QSAR model development and try to stabilize a good correlation between biological activities vs. descriptors. This will help to established new molecules with less or no resistance3-6 (Table I). Result and Discussion Model - 3D-RAN-70%-SWFB-kNN-trial 7 A total of ten trials were done to establish best 3D model. The above model was considered as best in view of q2 and predicted r2 values (Figure 1 and Figure 2). Both the values were in optimum range which made this model as best model and the results are shown in Table II. Model X was generated by means of random selection method followed by 70% in which the whole set of molecules were separated into training and test set. In this model, 25 molecules were considered as training set and rest of the 12 molecules were considered as test set. From the generated statistical equation, it was confirmed that the model INDIAN J. CHEM., SEC B, MAY 2021   728 has good projecting ability of 75%. The electrostatic descriptor plays a significant role in determining biotic activity. Two different electrostatic descriptors E_939 and E_137 with negative range of -1.0625 -0.8001 and positive range of 0.0073, 0.1361 respectively. The contribution of respective relative electrostatic fields  indicates  that  electrostatic  fields were predominant which was shown in Figure 3. The actual and predicted activities of all the compounds in the training and test set are in well agreement with their respective biological activities and shown in Table III and Table IV. The actual and predicted biotic activity of training and test set was depicted in Figure 4. The involvement plot of electrostatic field’s  Table I — Data set of 37 molecules of quinoxaline derivatives and their biological activities in the form of IC50 converted in –log IC50  S No ID  R2 W R6 R7 IC50 (µM) -log IC50 1 1 CN H H H 2.35± 0.04 8.6289 2 3 CN H H CH3 1.55±0.76 8.8096 3 4 CN H H OCH3 1.47±0.24 8.8326 4 5 CN Cl H H 1.01±0.37 8.9956 5 7 CN Cl H CH3 0.48±0.13 9.3187 6 8 CN Cl H OCH3 0.73±0.03 9.1366 7 9 CN CH3 H H 3.46±1.08 8.4609 8 11 CN CH3 H CH3 1.30±0.55 8.886 9 12 CN CH3 H OCH3 0.81±0.13 9.0915 10 13 CN COOCH3 H H 6.54±1.03 8.1844 11 15 CN COOCH3 H CH3 2.15±0.80 8.6675 12 16 CN COOCH3 H OCH3 3.16±0.45 8.5003 13 17 CN F H H 3.91±0.92 8.4078 14 18 CN F H Cl 5.83±0.60 8.2343 15 19 CN F H CH3 0.88±0.13 9.0555 16 20 CN F H OCH3 0.87±0.19 9.0604 17 22 CN F CH3 CH3 1.26±0.16 8.8996 18 23 CN OCH3 H H 3.14±0.44 8.503 19 25 CN OCH3 H CH3 2.77±0.65 8.5575 20 26 CN OCH3 H OCH3 2.29±0.12 8.6401 21 28 CN OCH3 CH3 CH3 3.02±0.31 8.5199 22 29 CN OCF3 H H 1.27±0.17 8.8961 23 30 CN OCF3 H Cl 0.71±0.34 9.1487 24 31 CN OCF3 H CH3 0.75±0.28 9.1249 25 32 CN OCF3 H OCH3 0.93±0.48 9.0315 26 36 CN F H F 5.78±0.20 8.238 27 37 CN Cl H F 4.21±0.57 8.3757 28 40 CN OCF3 H F 1.70±0.19 8.7695 29 42 CN H H CF3 0.33±0.12 9.4814 30 43 CN F H CF3 0.66±0.03 9.1804 31 44 CN Cl H CF3 1.56±0.19 8.8068 32 45 CN CH3 H CF3 0.72±0.09 9.1426 33 46 CN OCH3 H CF3 2.44±0.30 8.6126 34 47 CN OCF3 H CF3 1.93±0.31 8.7144 35 48 CN COOCH3 H CF3 2.03±0.62 8.6925 36 54 COOC2H5 H H Cl 3.22±0.34 8.4921 37 59 COOC2H5 H CH3 CH3 1.18±0.05 8.9281 MISHRA et al.: QUINOXALINE DERIVATIVES   729 interaction point out relative regions of the local fields around the aligned molecules and foremost to activity variation in the generated model. The blue colored ball represents the electrostatic field descriptor and the positive value range (E_137 0.0073 0.1361) indicates regions in which electron withdrawing groups are favourable whereas negative value range (E_939 -1.0625 -0.8001) represents electron rich or electron donating groups are advantageous in particular region. 3D QSAR model reveals that the electrostatic descriptors with positive as well as negative coefficient values are from R6 and R7 position of substituted aryl ring. The fitness plot of actual versus predicted biotic activity was depicted in Figure 5.  Materials and Methods The present work consist of 37 derivatives of quinoxaline as this were reported to have antiplasmodial activity and the IC50 [inhibition constant (μM)] values were converted to the pIC50   Figure 1 — Structure of template [1]    Figure 2 — Template based 3D-alignment of molecules  Table II — Results of 3D-QSAR analysis using kNN-MFA method (k nearest neighbour) by random selection 70% in connection with step wise forward backward (SWFB)  as variable selection method S No Trial No Test Set Molecules RAN-SWFB-kNN 01 07 2,10,13,23,24,26,27, 29,31,35,36,37 Optimum Components = 2 n = 25 Degree of freedom = 21 q2 = 0.7589 q2 se = 0.1361 pred_r2 = 0.4752 pred_r2se = 0.2987 Optimum component = 2, n = 25, Degree of freedom = 21, q2=0.7589, q2_se = 0.1361, Pred_r2 = 0.4752, pred_r2se = 0.2987. The values of different parameters in model X was shown in Table II.    Figure 3 — Grid point of descriptors contributing in 3D QSAR model  Table III — The actual and predicted activities of the training set Training Set Actual Values Predicted Values 07 8.4609 8.5932 14 8.2343 8.5449 15 9.0555 8.9476 20 8.6401 8.69505 11 8.6675 8.5449 05 9.3187 9.04 08 8.886 8.8041 25 9.0315 8.97825 33 8.6126 8.5932 22 8.8961 8.807 32 9.1426 9.088 01 8.6289 8.5642 06 9.1366 9.07595 18 8.503 8.58505 17 8.8996 9.02555 34 8.7144 8.8328 03 8.8326 8.6538 16 9.0604 9.11405 28 8.7695 8.80525 04 8.9956 8.97755 12 8.5003 8.75005 19 8.5575 8.48195 21 8.5199 8.7493 30 9.1804 9.15715 09 9.0915 9.0985  INDIAN J. CHEM., SEC B, MAY 2021   730 Table IV — The actual and predicted activities of the test set Test Set Actual Values Predicted Values 29 9.4814 9.16144 23 9.1487 8.74537 37 8.9281 9.24915 13 8.4078 8.56629 31 8.8068 8.47796 24 9.1249 8.81068 02 8.8096 8.94093 35 8.6925 8.87152 36 8.4921 8.413 26 8.238 8.3943 27 8.3757 8.66801 10 8.1844 8.64883    Figure 4 — Actual and Predicted biotic activity of Training and Test set molecules   Figure 5 — Fitness plot of actual vs predicted biotic activity values which is the minus logarithm of the IC50 7 that has represented in Table I.  Molecular modeling study/ 3D-QSAR study 3D QSAR and kNN- MFA studies were performed in Molecular Design Suite (MDS) installed in Lenovo computer system having genuine Intel Pentium i3 processor with windows XP operating system. Structures of the compounds were drawn using the 2D draw application and convert them to 3D by convert to 3D tool. Energy minimization and geometry optimization has done by Merck Molecular Force Field (MMFF) method and maximum number of cycle has 1000, convergence criteria that is root mean square gradient selected as 0.01 and medium dielectric constant is takes 1.0 in dielectric properties for steric energy and electrostatic cutoff 30.0 and 10.0 Kcal/mol were used7. For alignment of dataset template based alignment method was used by choosing the most active compound as template for alignment (Table I) molecule 1 has also used as reference for alignment (Figure 1), now all the molecules were align in template and reference molecule.  The dataset were aligned by template based alignment method using most active molecule (37, Table I) as a reference molecule 1 and structure 1 as a template (Figure 1). The alignment of all the molecules on the template has shown in Figure 2. In the template based alignment method, a template structure was defined and used as a basis for alignment of a set of molecules. After alignment of all molecule a grid or lattice has set on 2 Å resolution and now various descriptors represents by electrostatic, steric and hydrophobic interplay energies were calculated by setting the value of probe of charge +1. These descriptors show how the molecule binds with active site. The data set of all compounds was divided in to training and test set using sphere exclusion method. In this algorithm training sets were constructed by capturing whole descriptor space which is cover by representative points. Size of test and training sets were deicide by dissimilarity value represented by c, larger the value of c larger the size of test set and small the size of training set. In QSAR predication the value of dissimilarity level gives the predictive ability of the model if it is high than the predicative power may ne decreases. After the generation  of training and test set k-NN method has to be  applied 8, 9.  MISHRA et al.: QUINOXALINE DERIVATIVES   731 k-NN-MFA method In k nearest neighbour molecular field investigation is based on distance learning path in which unidentified element is classified giving to the mainstream of its k-NN in the training set of molecules. A large number of models were developed any means of this k-NN methodology.  k-NN approach begins with the selection of training and test sets followed by choosing descriptors which were later created over grid. The grid frame points show the interaction of different descriptors which were involved in the generation of best models. The interaction energies of descriptors were generated by means of methyl probe with positive charge 1. The values of interaction energy of different descriptors were considered for generation of bond and oppressed as descriptors to set proximity between set of molecules 9.  Conclusion In the present work 3D QSAR model was developed by Vlife Sciences MDS QSAR Plus software and the developed model help to predict the features required for good anti malarial activity of quinoxaline. In this study 3D QSAR model developed by k-NN method associated with stepwise forward- backward selection method gives acceptable q2 (0.7589) and pred_r2 (0.4752) values as well the this model has satisfactory internal and external predictive power. The selected model reports that the electrostatic descriptor plays a significant role in determining biotic activity, Positive value in electrostatic descriptor represents the electron withdrawing groups that are favourable in that region  and negative value represents the electron rich or electron donating groups that are advantageous in particular region. On the above mentioned model, the description for descriptor in particular region helps to design the new analogues of quinoxalines as antimalarial.  Acknowledgement Authors are thankful to the Chhattisgarh Council of Science and Technology, Raipur Chhattisgarh for financial support under the mini research project  grant programs respectively. Authors are also  thankful to Faculty of Pharmaceutical Sciences  Shri Shankaracharya Technical Campus Bhilai Chhattisgarh for providing necessary infrastructure of the conduction of the work.  References 1 WHO Expert Committee on Malaria, Technical Report Series, Twentieth Report, World Health Organization, Geneva (2000). 2 Hansch C, Kurup A, Garg R & Gao H, Chem Rev, 14 (2001) 619. 3 Gil A, Pabón A, Galiano S, Burguete A, Pérez-Silanes S, Deharo E, Monge A & Aldana I, Molecules, 19 (2014) 2166. 4 Tariq S, Somakala K & Amir M, Eur J Med Chem, 1 (2018) 542. 5 Vicente E, Lima L M, Bongard E, Charnaud S, Villar R, Solano B, Burguete A, Perez-Silanes S, Aldana I, Vivas L & Monge A, Eur J Med Chem, 1 (2008) 1903. 6 Molecular Design Suite, Vlife Sciences Technologies Pvt. Ltd, Pune, India (2004). 7 Shen M, LeTiran A, Xiao Y, Golbraikh A, Kohn H &  Tropsha A, J Med Chem, 20 (2002) 2811. 8 Vaishnav Y, Kashyap P & Deep Kaur C, Curr Nanomed,  7 (2017) 59. 9 Sanmati K J & Achal M, J Theor Comput Sci, 2 (2015) 1.     

3D QSAR analysis on quinoxaline derivatives as anti-malarial using K-nearest neighbour molecular field analysis

http://nopr.niscair.res.in/bitstream/123456789/57586/1/IJCB%2060B%285%29%20727-731.pdf

3D QSAR analysis on quinoxaline derivatives as anti-malarial using K-nearest neighbour molecular field analysis

Abstract

Similar works

Full text

Available Versions

NOPR