Molecular Dynamics Graphical Visualization 3-D QSAR Pharmacophore QSAR, COMBINE, Scoring Functions, Homology Modeling,..

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4 Conformational Search Molecular Docking Simulate Annealing Ab Initio QM Molecular Dynamics Graphical Visualization 3-D QSAR Pharmacophore QSAR, COMBINE, Scoring Functions, Homology Modeling,.. Rino Ragno: Computational Medicinal Chemistry Applications to Epigenetic Targets Inhibitors

5 Drug Design = Computational & Synthesis Tandem Basic Research Target Ident Target Valid Hit Ident Hit to Lead Lead Opt Preclin Clinical Trial Prod Diagnos Rino Ragno: Computational Medicinal Chemistry Applications to Epigenetic Targets Inhibitors

6 Unknown Protein Structure Known Ligand Structure Unknown Known Library Screening Ligand Based De Novo Structure-Based Rino Ragno: Computational Medicinal Chemistry Applications to Epigenetic Targets Inhibitors

7 Ligand-Based Structure-Based QSAR Phamacophore 3-D QSAR Scoring Function Docking COMBINE 7

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16 The Hansch Equation 6

17 The first QSAR equations were based on the observation that partition coefficients, as expressed by log P values, are to some extent, correlated to certain biological endpoints. log (/C) = k log P + kσ + k3 Conc. of compound required to produce a standard response in a given t Logarithm of the molecule s partition coefficient (-octanol/water) Hammet Parameter (molecule s electronic characteristics) 7

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21 Identification of Active Ligands Identification of Suitable Descriptors (molecular fingerprint) Establish Mathematical Expression Relating Descriptors to Activity Construction and Validation of the QSAR model

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24 Chimica Farmaceutica Introduction to Ligand-Based Drug Design 4 Squared Correlation Coefficient R or r Cross-Validated R Q or q = = = N i i N i i calc i Y Y Y Y r exp,, exp, ) ( ) ( = = = N i i N i i pred i Y Y Y Y q exp,, exp, ) ( ) (

25 ( Y Squared Correlation i= r = Coefficient R or r N ( Y 0 Fitting r N i= exp, i exp, i Y calc, i Y ) ) r ESS TSS RSS TSS i= = N N ( Y i= exp, i ( Y exp, i Y calc, i Y ) ) 5

26 CV (Cross-Validation) Cross-validated R Q or q q N i= = N ( Y i= exp, i ( Y exp, i Y pred, i Y ) ) SDEP N i= = ( Yexp, Y, ) i N pred i q The predictive ability of a model is estimated using a reduced set of structural data 6

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28 Y-Scrambling A statistical test of prediction tools, in which models are fitted for randomly reordered property/activity values and compared with the model obtained for the actual property/activity values. Anew model newis obtained model for such permuted isdata, Robtained and Q are then recalculated. for such This step is repeated for a sufficient number of times (iterations): permuted data, R and Q are then a good number being 50 to 00. recalculated. Values obtained in the above fashion are compared with the true values obtained for the model that was fitted on the real data. This step is repeated for a sufficient number of times (iterations): a good number being 50 to 00. Original X block Scrambled Y vector Scrambling Original Y vector Values obtained in the above fashion are compared with the true values obtained for the model that was fitted on the real data. 8

29 External Test-Set SDEP (Standard Deviation Error of Prediction) SDEP N = i= ( Y Y ) exp, i pred, i N N of predicted compounds 9