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2 d qsar model of dihydrofolate reductase (dhfr) inhibitors with activity in toxoplasma gondii and
 

2 d qsar model of dihydrofolate reductase (dhfr) inhibitors with activity in toxoplasma gondii and

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    2 d qsar model of dihydrofolate reductase (dhfr) inhibitors with activity in toxoplasma gondii and 2 d qsar model of dihydrofolate reductase (dhfr) inhibitors with activity in toxoplasma gondii and Presentation Transcript

    • 2D-QSAR model of Dihydrofolate reductase (DHFR) inhibitors with activity in Toxoplasma gondii and Lactobacillus casei
    • QSAR
      • Quantitative structure-activity relationships (QSAR) derive
      • models which describe the structural dependence of
      • biological activity either by physicochemical parameters, by
      • variables encoding different structural features, or by 3D
      • molecular property profile of the compounds.
      • A QSAR generally takes the form of a linear equation:
      • Biological Activity = Constant + (C 1 * P 1 ) + (C 2 * P 2 ) +
      • (C 3 * P 3 ) + ….... (C n * P n )
      Molecular Descriptors
    • Methodology 31 Unique DHFR Inhibitors with reported activity (IC50 values) in Toxoplasma gondii and Lactobacillus casei Chemical Atomic Corrections and Energy Minimization of 31 inhibitors Calculation of Molecular Descriptors Topological: Surface area Constitutional: LogP, molecular weight, polarizability and refractivity Calculation of predicted IC50 (pIC50) pIC50 = -log IC50
    • Methodology (contd.) Dataset : 31 Inhibitors with known IC50, predicted IC50 and Molecular Descriptors 16 molecules 15 molecules Training Set Test Set Develop 2D-QSAR Model using MLR Biological activity = 1.580354080040E + 001 + 9.200251214572E-003*( surface area ) + -4.649129538003E-001*( logP ) + 2.030973405986E-002*( molecular weight ) + 1.017789824217E-001*( polarizability ) + -2.206177356776E-001*( refractivity )
    • 124.77 44.84 421.46 -3.67 557.59 8.37 4.3 5479798 20 119.87 43.01 407.43 -3.92 498.68 8.43 3.7 5479797 19 118.05 42.29 408.42 -3.05 506.46 8.74 1.8 5479796* 18 85.03 30.04 266.31 -0.71 339.85 8.66 2.2 476043 17 97.78 34.98 326.36 -2.69 442.17 9.05 0.88 476035 16 97.10 35.64 328.80 -0.25 402.70 8.77 1.7 472907* 15 116.26 42.96 398.40 -2.66 526.66 8.57 2.7 462577 14 105.03 37.53 339.40 -2.25 421.53 8.41 3.9 462129 13 111.40 40.00 369.42 -3.24 464.39 8.27 5.4 462125* 12 105.03 37.53 339.40 -2.25 458.40 8.33 4.7 462123 11 90.10 31.87 280.33 -0.39 350.29 9.07 0.84 462103* 10 123.35 44.13 422.44 -2.69 525.15 8.51 3.1 457754 9 111.51 41.12 384.44 -3.00 486.57 9.24 0.58 447815* 8 105.03 37.53 339.40 -2.25 431.94 8.41 3.9 447021* 7 100.97 36.82 340.38 -1.91 444.76 8.20 6.3 444617* 6 117.01 43.08 440.42 -2.15 588.46 11.90 0.00125 425380 5 115.74 43.71 439.43 -0.26 514.93 8.42 3.8 329367* 4 120.24 45.06 454.45 -1.45 572.69 8.22 6.0 126941* 3 120.24 45.06 454.45 -1.45 575.36 11 0.01 72440 2 95.88 35.47 325.37 -1.07 430.08 8.82 1.5 54369* 1 Refractivity (Å 3 ) Polarizability (Å 3 ) Molecular Weight (amu) LogP Surface Area (Å 2 ) pIC50** IC50 (nm) Constitutional Topological Activity CID S.No
    • CID highlighted in Red Color constitute Training set ; Otherwise Test set 98.43 36.01 432.27 3.78 291.29 8.89 1.3 103918140 31 92.38 33.71 294.36 -0.02 372.80 8.20 6.3 44388480* 30 108.24 39.15 333.44 0.33 390.22 8.39 4.1 44342970 29 116.52 43.69 444.45 -3.01 595.07 11.30 0.005 44285361* 28 124.25 46.48 452.47 -2.14 544.18 8.66 2.2 44281312* 27 97.02 34.42 293.37 0.80 396.90 8.44 3.6 25132195 26 109.77 39.36 353.42 -1.91 446.62 8.43 3.7 25099170 25 119.90 45.51 441.49 0.10 607.28 8.60 2.5 22708989* 24 121.39 45.53 458.47 -2.95 623.22 11.64 0.0023 13942163 23 146.14 53.63 537.53 -3.96 700.54 8.21 6.2 11979618* 22 125.31 42.22 443.48 -4.29 534.61 8.24 5.8 5481387* 21 Refractivity (Å 3 ) Polarizability (Å 3 ) Molecular Weight (amu) LogP Surface Area (Å 2 ) pIC50** IC50 (nm) Constitutional Topological Activity CID S.No
    • Prediction Accuracy of QSAR Model using Training Data Calculated via Formula Trained model gives the pIC50 value Numbers Highlighted in Red Color Indicates How close is the actual and predicted IC50 values ? 8.27 8.20 44388480 16 10.44 11.30 44285361 15 8.31 8.66 44281312 14 8.49 8.60 22708989 13 8.22 8.21 11979618 12 8.38 8.24 5481387 11 8.44 8.74 5479796 10 8.51 8.77 472907 9 8.58 8.27 462125 8 8.27 9.07 462103 7 9.07 9.24 447815 6 8.36 8.41 447021 5 9.17 8.20 444617 4 8.50 8.42 329367 3 9.04 8.22 126941 2 9.32 8.82 54369 1 Training dataset Calculated pIC50 Actual pIC50 CID S.No
    • Predict Activity using Test Data from the generated QSAR Model Best 5 Molecules for which actual and predicted IC50 values were very closer 7.46 8.89 103918140 31 6.12 8.39 44342970 30 7.14 8.44 25132195 29 7.77 8.43 25099170 28 10.07 11.64 13942163 27 8.24 8.37 5479798 26 8.42 8.43 5479797 25 8.97 8.66 476043 24 9.74 9.05 476035 23 8.70 8.57 462577 22 8.27 8.41 462129 21 8.61 8.33 462123 20 7.74 8.51 457754 19 9.73 11.90 425380 18 9.06 11 72440 17 Test dataset Calculated pIC50 Actual pIC50 CID S.No
    • Best 5 Molecules Superimposed View of 5 Molecules Correlation studies among the descriptors and with the activity revealed LogP had a negative impact on the biological activity . Descriptors such as surface area, molecular weight, polarizability and refractivity contributed very less towards the activity of the molecules . Hence, it is clear that similar chemical structures (analogues) did not optimize the activity of the molecule and stresses the requirement of additional inhibitors reported elsewhere with maximum molecular diversity which can reproduce a better model for structure-activity relationships .