موضوعات سمينار : Side chain dihedral roramer libraries Polarizable force fields Plop...

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ار : ن ي م س وعات ض و مSide chain dihedral roramer libraries Polarizable force fields Plop software Loop prediction Pka prediction by simulation SMD simulation REMD simulation ΔG binding prediction by simulation Salvation model in simulation Inverse-docking Replica exchange simulation Quantum molecular dynamics simulation Quantum Monte Carlo Biodetergent simulation Simulation in nano tecnonogy Nanobuiorobate simulation DNA computing Analysis with molecular dynamics simulation Normal Mode .... هد و ام د ج ن و ا ل کار ت ن و م ي ه سار ي% ي& سمه اي ا رن ب ا کار نر ر ط و ي ف ر مع
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: Side chain dihedral roramer libraries Polarizable force fields Plop software Loop prediction Pk a prediction by simulation SMD simulation REMD simulation G binding prediction by simulation Salvation model in simulation Inverse-docking Replica exchange simulation Quantum molecular dynamics simulation Quantum Monte Carlo Biodetergent simulation Simulation in nano tecnonogy Nanobuiorobate simulation DNA computing Analysis with molecular dynamics simulation Normal Mode .... Slide 2 2 Slide 3 (MM) (MD) (MC) (Docking) 3 Slide 4 Medicinal Chemistry Proteins from Natural Organisms Ligands from Natural Sources or Synthesis Preparative Biochemistry Assay, Characterization Crystallization X-Ray Sequence Database 3D Structure Gene Cloning Gene synthesis Site-Directed Mutagenesis Expression Computer Graphics Knowledge Based Modelling and Design CD, NMR Simulation by EM, MD,... Molecular Biology Biophysics Biocomputing Organic chemistry Protein-Ligand Complex 4 Slide 5 5 : . : ( ): ( ) . ( ) ( ) Slide 6 6 : ... . . ( ... ) ( ...) . : 1- 2- 3- : Slide 7 Structural biologyComputational chemistry Medicinal chemistry Biochemistry - - - - .... - - - 7 : - . ! - . Slide 8 CompanyProject Air LiquideDesign zeolites for O 2 /N 2 separation Air Prod & ChemAdhesives, adsorption AlbemarleFlame retardancy AmocoCatalysis-homo/heterogeneous, thermochemistry DuPontThermo, kinetics, catalysis Exxon R&ENO x kinetics, elementary and networked reactions, safety Procter & GambleDesigned detergent enzyme HerculesPolysaccharide rheology 8 Slide 9 0000,1001001950 0000,1001001960 1 000,000, 2 000,152981970 2000,300000,204961980 4000,100000,308921990 8000,50000,4012881995 15000,20000,5020802000 25000,10000,7030702005 30000,5000,10040502010 40000,2000,14050 2015 60000,180202020 9 Slide 10 Drug design for Diabetes Type II William Lipscomb falcipain inhibitors Ring et al. Proc. Natl. Acad. Sci. USA, 90, 3583-3587 (1993) FluA HA fusion inhibitors Bodian et al., Biochemistry, 32, 2967-3978 (1993) HIV Tat-TAR interaction inhibitors Filikov et al. J. Comput-Aided Mol. Des. 12, 229-240 (1998) CD4-MHC II inhibitors Gao et al. Proc. Natl. Acad. Sci. USA., 94, 73-78 (1997) HIV gp41 inhibitors Debnath et al.; J. Med. Chem, 42, 3203-3209 (1999) aquaporin-1 mechanism B. de Groot & H. Grubmller,Science 294: 2353-2357 (2001)photo-isomerization G. Groenhof et al.,J. Am. Chem. Soc. 126:4228-4233,(2004) reaction pathways Diels-Alder cyclo-addition mechanism Examples and Success Stories 10 Slide 11 11 computer-aided design and drafting Slide 12 Biocomputing: Intersection of Biology and Computation Bioinformatics : This includes management of biological databases, data mining and data modeling, as well as IT-tools for data visualization Computational Biology : This includes efforts to solve biological problems with computational tools (such as modeling, algorithms, heuristics) DNA computing and nano-engineering : This includes models and experiments to use DNA (and other) molecules to perform computations Computations in living organisms : this is concerned with constructing computational components in living cells, as well as with studying computational processes taking place daily in living organisms. Computational chemistry: Slide 13 13 : : 1- ( MM ) : Hyperchem 2- (MC): 3 - (MD): . 4- : : ( ab inititio) (( Semiemperical Density function theory)) Games Orca ... 5- - (Molecular Mechanic- Quntum Mechanic ) Slide 14 : ( ) ( ) ( ZPE = ) - . PES Slide 15 ( Force field ) : .. : Slide 16 : - - : r0r0 16 Slide 17 : 17 Slide 18 B-C A-B-C-D n=3 ethane n=2 ethylene n=1 butane : ABC BCD BC ( ) 18 H H H H HH Newman projection 0= 60= n (multiplicity) Slide 19 Improper torsions and out of plane bending motions 19 - - .. Slide 20 : : : : : 1 : : : : ( ( : ( (dispersion)) 20 Slide 21 : - 5 - 2 . r eq 00 N3.50.16 C3.40.12 : r m - 21 2r 0 0 Slide 22 Pk a : pH HA H + + A - pH = pK a : [A - ] = [HA] pH > pK a : [A - ] > [HA] pH < pK a : [A - ] < [HA] pKa : pk a . : Pka . pka propka, H ++ pK a : 4 pK a : 11 Pk a : 6.5 : pK a = -log K a k a = [H + ] [A - ] [HA] Pka : 22 Slide 23 (q) = pH (PI) : pH pH = pH = Pk a -pH pH = PI-pH = : : pH : : 23 Slide 24 ( )( ) : : ( ) : ( ) : - 1 I 24 Slide 25 : : F=q 1 q 2 /r 2 ( ) : q 1 . : E=F/q 2 =q 1 /r 2 () : q r : =q/r () U: q 1 q 2 : U=w= F r=q 1 q 2 /r 2 r=q 1 q 2 /rV : U= q 2 =q 1 q 2 /r 25 Slide 26 : : : The electrostatics scheme colors each vertex on the surface according to the electrostatic potential at that vertex using a red-to-blue gradient from -7.0 to +10.0 = 78 80 = 3 = 1 : : : 26 Slide 27 = 80 1.5 = 3 40 : : pH 140 27 Slide 28 - - Acceptor D-H :A - Donor O, N O, N, F : 3.1 2.7 - : 5 :A-C D-H U(r)-Uc rrc . U(r)-Uc (du(r)/dr)(r-rc) rrc 3) : . - : . Slide 60 : - : 1) 2) : : ) ) Slide 61 Use of a shift function to replace the truncated forces by continuous forces that have continuous derivatives at the cutoff radius (Coulomb interactions are only calculated for short long-range interactions due to computational cost). Removes noise caused by cutoff effects but drastically changes the Coulomb potential (LJ dispersion and repulsion term changes only slightly). (A) Ewald Summation: - sums up the long-range interactions between particles and their infinite periodic images. - sums up the potential energy of two converging series plus a constant term. (B) Particle-Mesh-Ewals Summation (PME): - substantially faster than Ewald summation ( Nlog(N) ) - uses a grid, to which the charges are assigned, which is Fourier transformed. The forces are calculated, transformed back to real space and interpolated. 61 Slide 62 62 Slide 63 63 Slide 64 64 Slide 65 65 Slide 66 66 Slide 67 67 Slide 68 68 Slide 69 : 1): 2) : : ... . E=k(t)+u(t) T(t)=(2/3NKB)K(t) Slide 70 : =1/N A(t) . : : 1) : 2) :. 3) : ) ) 4) Slide 71 71 Slide 72 72 Normal Mode Slide 73 1-Complementary tool for Protein crystallography 2-Application at NMR 3-Simulating Truncated Active Sites in a Virtual Fluid 4-Qualitative Analysis of Ligand Binding 5-Ligand Design and Docking 6-Synergistic Use of MD and 3D QSAR 7. Qualitative Analysis of Ligand Binding 8-Protein folding 73 Slide 74 Application of MD simulations : 1-Complementary tool for Protein crystallography: Protein crystal structures represent one of the most attractive starting points for a rational drug-design procedure. However, X-ray structures may not be accurate enough for drug design because: (a) Only few informations on the dynamics of the macromolecule can be derived. (b) Electron density cannot be interpreted due to a too low resolution. (c) Conformational artefacts given by X-ray diffraction 74 Slide 75 One examples : The enzyme acetylcholinesterase (AChE) function is to hydrolyze acetylcholine in cholinergic nerves. The X-ray structure of AChE from Torpedo california has been obtained at a resolution of 2.8 . Remarkably, the active site is located far from the enzyme surface (about 20 ) at the bottom of a deep,narrow gorge. This gorge may function as a cation pump by the combined action of a dipole gradient (aligned within the gorge axis) and aromatic side chains delimiting the walls of the gorge. 75 Slide 76 76 Slide 77 However, its mechanism of action and notably the high catalytic rate of the enzyme could not be fully explained from the crystal structure alone Notably, three residual electron density peaks present in the gorge have to be attributed to either water molecules or small cationic species that may drive the substrate entry and fix its bound conformation. MD simulation of AChE in presence of either three water molecules or three ammonium cations filling the extra electron density provided a plausible xplanation. Simulations performed in presence of water molecules yielded to altered conformations of active site residues (rms deviations of 1.5)whereas MD with explicit definition of three small cations led to structures in remarkable 77 Slide 78 agreement with X-ray diffraction data (rmsd < 0.5 ). The combined use of X-ray crystallography and molecular dynamics simulations clarifies here the dynamical behavior of AChE. It reveals the transient formation of a short channel through the active site, large enough for a water molecule. A so-called back-door hypothesis was formulated to explain substrate/product entry/elimination. Although it was supported by the electrostatic potential of the enzyme, it still has not been fully evidenced by recent sitedirected mutagenesis studies. (Axelsen, P.H., Harel, M., Silman, I. and Sussman, J., Structure and dynamics of the active site gorge of acetylcholinesterase: Synergistic use of molecular dynamics simulation and X-ray crystallography, Prot. Sci., 3 (1994) 188197.) 78 Slide 79 79 Slide 80 2-Application at NMR : The conformational analysis of tetra-O-methyl-(+)-catechin. In the crystalline state, the two observed conformations of the benzopyran ring places the dimethoxyphenyl moiety at C2 in an equatorial position.This is in disagreement with proton NMR coupling constants which suggest an interconversion of axial and equatorial conformations. 80 Slide 81 A 4.5 ns MD simulation in vacuo starting from the two crystal structures not only showed the interconversion, but was also able to reproduce the NMR-derived ratio between the two populations of axial and equatorial conformations 81 Slide 82 3-Simulating Truncated Active Sites in a Virtual Fluid: 82 Slide 83 4-Qualitative Analysis of Ligand Binding: MD high-affinity or low-affinity Ligands predict binding properties (a)Atomic positional fluctuations of the bound ligand The more RMSD The more flexible The less bound 83 Slide 84 The more flexible the less buried (b) Buried surface areas could also be well related to binding affinities The more buried The less flexible The more binding affinity 84 Slide 85 85 Slide 86 (c) Proteinligand non-bonded distances critical topological features (non-bonded distances, angles) is often used to analyze trajectories of protein ligand complexes Constant distance between protein and ligand cmass the most stable complex Increasing distance between protein and ligand cmass after 200 ps MD the less stable complexes 86 Slide 87 (d) Proteinligand hydrogen-bonds: The more number of strong and medium H-bonds The more bound The less number of medium and (or) strong H-bonds The less bound 87 Slide 88 5-Ligand Design and Docking Flexible docking of small molecules to known three-dimensional protein structures An application of MD to drug-design techniques MD different conformations or rotameric states of the protein active site use these coordinates as starting points for parallel docking. 88 Slide 89 6-Synergistic Use of MD and 3D QSAR 3D QSAR models are highly dependent on the alignment of bioactive conformations MD the conformational analysis of semi- flexible molecules prior to pharmacophore mapping. MD Relevant conformational populations Molecular properties be readily identified and imported into a QSAR table 89 Slide 90 90 Slide 91 (b2) Semiempirical approaches (WASA model) Protein + water G h protein-water 91 Slide 92 92 Slide 93 (a) Free energy perturbation Based on modifying a state-dependent parameter () from 0 to 1 (a1)-G ligand binding Pro+ Lig pro-Lig =0 =1 93 Slide 94 (a2)-G hydration G hyd : hydration free energy. G 1 : free energy associated to the mutation of protein into dummy atoms in water. G 2 : same in vacuo. G 3 : the hydration free energy of dummy atoms. This term is equal to 0 since dummies do not have non-bonded interactions and bonded interactions remain the same. G hyd = G 1 +G 3 - G 2 G hyd = G 1 G 2 94 Slide 95 Simulations of the folding of a three-helix bundle protein. (a and b) (Upper) Semilog plots of the time dependence of the fractions of native helical and interhelical contacts and the inverse fractions of native volume (calculated from the inverse cube of the radius of gyration) for two different trajectories.(Lower) Structures of the protein molecule at selected times. 8-Protein folding 95 Slide 96 MD challenges: 1- MD models will neither substitute for experimentally determined structures 2- MD is not the only potent conformational sampling technique 3- The successful application of molecular dynamics simulations in a drug-discovery program absolutely needs a strong and permanent feedback to the experiment. 4-Only 10100 ns 96 Slide 97 97 Slide 98 98 Slide 99 99 Slide 100 100 Slide 101 Free Molecular Dynamics Package! Installed on binf servers Runs faster than most other MD programs Allows the trajectory data to be stored in a compact way Gromacs provides a basic trajectory data viewer; xmgr or Grace may also be used to analyze the results. Provides other analysis tools: calculates distances over time (i.e. distance between atoms), analyses bonded interactions, analyses structural properties (i.e. solvent accessible surface area) 101 GROMACS GROMACS Slide 102 1-Choosing a pdb file or ligand-protien complex obtained from docking. 2- Choosing an appropriate force field and making gro and topology file. 3-Choosing a proper box and putting water molecules in it and centering protein molecule in this box. 4-Putting appropriate counter ions charge for neutralization of system. 5-Minimization by different algorithm. 6- Position restraint of system. 7-Increasing temperature up to 300 K for reaching to equalization. 8-Molecular dynamics simulation for 10 ns in order to sampling. 102 Slide 103 File Formats *.pdb: format used by Brookhaven Protein DataBank *.top: topology file (ascii), contains all the forcefield parameters *.gro: molecular structure file in the Gromos87 format (Gromacs format) Information in the columns, from left to right: residue number residue name atom name atom number x, y, and z position, in nm x, y, and z velocity, in nm/ps *.tpr: contains the starting structure of the simulation, the molecular topology file and all the simulation parameters; binary format 103 Slide 104 File Formats *.trr: contains the trajectory data for the simulation; binary format. It contains all the coordinates, velocities, forces and energies as was indicated the mdp file. *.edr: portable file that contains the energies. *.xvg: file format that is read by Grace (formerly called Xmgr), which is a plotting tool for the X window system. *.xtc: portable format for trajectories which stores the information about the trajectories of the atoms in a compact manner (it only contains cartesian coordinates). 104 Slide 105 File Formats *.mdp: allows the user to set up specific parameters for all the calculations that Gromacs performs. em.mdp file: sets the parameters for running energy minimizations; allows you to specify the integrator (steepest descent or conjugate gradients), the number of iterations, frequency to update the neighbor list, constraints, etc. md.mdp file: sets the parameters for running the molecular dynamics program; allows you to indicate the appropriate settings depending on the force field used, 105 Slide 106 Generic mdp file for energy minimization title = Yo cpp = /lib/cpp include = -I../top define = integrator = md dt = 0.002 nsteps = 500000 nstxout = 5000 nstvout = 5000 nstlog = 5000 nstenergy = 250 nstxtcout = 250 xtc_grps = Protein energygrps = Protein SOL nstlist = 10 ns_type = grid rlist = 0.8 coulombtype = cut-off rcoulomb = 1.4 rvdw = 0.8 tcoupl = Berendsen tc-grps = Protein SOL tau_t = 0.1 0.1 ref_t = 300 300 Pcoupl = Berendsen tau_p = 1.0 compressibility = 4.5e-5 ref_p = 1.0 106 gen_vel = yes gen_temp = 300 gen_seed = 173529 constraints = all-bonds Slide 107 Force Field The set of equations (potential functions) used to generate the potential energies and their derivatives, the forces. The parameters used in this set of equations Gromacs provides the following force fields: 0: Gromacs Forcefield (see manual) 1: Gromacs Forcefield with all hydrogens (proteins only) 2: GROMOS96 43a1 Forcefield (official distribution) 3: GROMOS96 43b1 Vacuum Forcefield (official distribution) 4: GROMOS96 43a2 Forcefield (development) (improved alkane dihedrals) 107 Slide 108 Programs pdb2gmx: - reads in a pdb file and allows the user to chose a forcefield - reads some database files to make special bonds (i.e. Cys-Cys) - adds hydrogens to the protein - generates a coordinate file in Gromacs (Gromos) format (*.gro) and a topology file in Gromacs format (*.top). - issues a warning message if an atom is not well resolved in the structure. 108 editconf: - converts gromacs files (*.gro) back to pdb files (*.pdb) - allows user to setup the box: the user can define the type of box (i.e. cubic, triclinic, octahedron) set the dimensions of the box edges relative to the molecule (-d 0.7 will set the box edges 0.7 nm from the molecule) center the molecule in the box Slide 109 genbox: - solvates the box based on the dimensions specified using editconf - solvates the given protein in the specified solvent (by default SPC- Simple Point Charge water) - water molecules are removed from the box if the distance between any atom of the solute and the solvent is less than the sum of the VanderWaals radii of both atoms (the radii are read from the database vdwradii.dat) 109 grompp (pre-processor program): - reads a molecular topology file (*.top) and checks the validity of the file - expands the topology from a molecular description to an atomic description (*.tpr) - it reads the parameter file (*.mdp), the coordinate file (*.gro) and the topology file (*.top) - it ouputs a *.tpr file for input into the MD program mdrun - since *.tpr is a binary file, it can not be read with more but it may be read using gmxdump, which prints out the input file in readable format (it also prints out the contents of a *.trr file) Slide 110 Programs mdrun: - performs the Molecular Dynamics simulation - can also perform Brownian Dynamics, Langevin Dynamics, and Conjugate Gradient or Steepest Descents energy minimization - reads the *.tpr file, creates neighborlists from that file and calculates the forces. - globally sums up the forces and updates the positions and velocities. - outputs at least three types of files: (1) trajectory file (*.trr): contains coordinates, velocities, and forces (2) structure file (*.gro):contains coordinates and velocities of the last step (3) energy file (*.edr): contains energies, temperatures, pressures 110 Slide 111 gmxcheck: gmxcheck reads a trajectory (*.trr) or an energy file (*.edr) and prints out useful information in them. g_energy: extracts energy components or distance restraint data from an energy file into a *.xvg file (may be read using Xmgr or Grace). trjconv: allows compression of trajectory file into a *.xtc file that can be analyzed using ngmx ngmx: -Gromacs trajectory viewer - plots a 3-D structure of the molecule - allows ratation, scaling, translation, labels on atoms, animation of trajectories, etc. 111 Slide 112 Energy Minimization Steepest Descent: takes step toward (-) gradient, disregards previous step. Convergence can be slow especially if near local minimum. Conjugate Gradient: uses the gradient information from the previous step. Allows a quicker convergence toward the nearest local minimum (yet slow if far away from the local minimum). grompp (pre-processor program): - checks validity of topology file (*.top) - expands the topology from a molecular description to an atomic description (*.tpr) mdrun: - reads the *.tpr file, creates neighbor lists from that file and calculates the forces. - outputs at least three types of files: (1) trajectory file (*.trr): contains coordinates, velocities, and forces (2) structure file (*.gro):contains coordinates and velocities of the last step (3) energy file (*.edr): contains energies, temperatures, pressures 112 Slide 113 Energy Minimization cpp = /lib/cpp define = -DFLEX_SPC; water topology appropriate for EM constraints = none integrator = steep; steepest descents nsteps = 400 nstlist = 10; update neighbor list every 10 steps ; ; Energy minimizing stuff ; emtol = 1000; convergence when max force is smaller than ; this value (kJ mol -1 nm -1 ) emstep = 0.01; initial step size ns_type = grid; neighbor searching (other option is simple) rlist = 1; cut-off distance for short-range neighbor list ; (nm) rcoulomb = 1.0; distance for Coulomb cut-off rvdw = 1.0; distance for LJ or Buckingham cut-off Tcoupl = no Pcoupl = no; box dimensions are fixed gen_vel = no; do not generate velocities at start up 113 Slide 114 114 Slide 115 115 Slide 116 116 Slide 117 Homology modeling = Comparative protein modeling = Knowledge-based modeling Idea: - The structure of a protein is uniquely determined by its sequence - During evolution, the structure is more stable and changes much slower than the associated sequence, so that the similar sequences adopt practically identical structures, and distantly related sequences still fold into similar structures - Using experimental 3D-structures of related family members (templates) to calculate a model for a new sequence (target). 117 Slide 118 Homology Modeling protein of known 3D structure modeled 3D structure of target protein Build the lock, then find the key If you know the 3D structure of the target receptorDONT target template Known Structures ( Templates) Target Sequence Template Selection Alignment Template - Target Structure modeling Homology Model(s ) 118 Slide 119 Similar Sequence Similar Structure 119 Slide 120 Build or Find the key that fits the lock If you know the 3D structure of the target receptor Receptor-Based Design Docking: 120 Slide 121 121 MEP ( ) Docking 121 Slide 122 Active Site 122 Slide 123 Molecular docking : Predicts the orientation of the ligands bound to receptors by known receptor conformation. (Molecular docking is a fast Method to explore substrate/receptor complexes in the field of drug discovery as well as in understanding biochemical processes) 123 Slide 124 1-DOCK 2-GOLD AutoDock 6-AutoDock ( automated procedure for predicting the interaction of ligands with biomacromolecular targets.) 7-Surflex 4-MOE-Dock 3-FlexX 5-FTDOCK The differences between them are derived from the different search algorithms or different implementation of the same algorithms, and different scoring functions. 124 Slide 125 Docking procedure Target preparation Ligand preparation Define a grid box Calculation of Grid map for Ligand atoms( by Autogrid) Docking ( by Autodock) Analysing of results and estimation of binding affinity of a ligand Grid G binding = f (Interactions) scoring function 125 Slide 126 1- Preparation of the Ligand and Receptor 2-Defination of grid 3- Making of grid maps by AutoGrid 4- Docking Docking procedures search algorithm scoring function Each grid point stores the potential energy of a probe atom Rigid Root 126 Slide 127 Search algorithms Scoring functions Empirical free energy scoring functions Molecular mechanics force fields Target functions Genetic algorithms Monte Carlo Simulated annealing Lamarckian genetic algorithm and other 127 Slide 128 Creating a random population Mapping Proportion selection Crossover Mutation Elitism Maximum generations or maximum number of energy evaluation Next Generation Global minimum 128 Slide 129 Based on recrystalization process : 1-Melt, disordered 2-Cooled, ordered 3-Adiabatic (T~0) Changes are randomly made to change the ligand's position, orientation, and conformation. The new state is compared to the previous state. If the new states energy level is lower it is accepted, but if it is higher it is accepted with a probability. 129 Slide 130 Lamarckian Genetic Algorithm Hybrid of GA and LS(local Search) 130 Slide 131 Flexible Docking What do do when the ligand is too flexible ? (Nrot>15) 1. find its protein-bound conformation 2. dock the NMR conformation by NMR (tr-NOE) e.g. PTS EI-binding oligopeptide (KISRHGRPTG) (Rognan et al., J. Comput-Aided Mol. Design) 131 Slide 132 challenging aspect The most challenging aspect of drug design Calculation of receptor binding affinity (K D ) of new ligand Very difficult and critical for drug design Methods: 1- Quantum chemical calculations 2-Scoring functions complex, intensive Days to weeks on even the fastest computers Estimation of the free energy of binding 132 Slide 133 Predicting binding affinities Is its possible to predict the binding affinity of a ligand for its host protein from the 3D-structure of the protein-ligand complex ? Binding free energy gas constant Equilibrium dissociation constant Temperature ? Gbinding = f (Interactions) 133 Slide 134 S rt S int H LW H RW H LR SWSW S vib G = H-T S Free energy Enthalpy Entropy Ligand in solution free rotation Receptor bound water loosely associated water molecules free water Receptor-Ligand complex 134 Slide 135 G bind = + HB + LIPO + ROT + BP + DESOLV buried-polar repulsive term Rognan et al. J. Med. Chem, 42, 4650-4658 (1999) 135 Slide 136 Empirical scoring function The method is fast (30 min/complex: docking and scoring) semi-automated is applicable to 3-D models does not need extensive training 136 Slide 137 Types of Molecular Descriptors Constitutional, Topological 2-D structural formula Electrostatic Geometrical 3-D shape and structure Quantum Chemical Hybrid descriptors 137 Slide 138 1-Molecular Modeling, Principles and Applications, A. Leach, Second edition, Printice Hall, 2001. 2-Computational Medicinal Chemistry for Drug Discovery, Patrick Bultinck Ghent Hans De Winter Wi If ried Langenaeker, MARCEL DEKKER, INC. mdekker, NEW YORK: BASEL, 2004. 3- An Introduction to Chemoinformatics, Andrew R. Leach, VALERIE J. GILLET, Springer, 2007. VALERIE J. GILLET, and Revised Edition, GlaxoSmithKline Research Springer, 2007. 4- Computational Modeling of Membrane Bilayers, Scott E. Feller, Elsevier, 2008. 5- Computer Modeling of Chemical Reactions in Enzymes and Solutions, Arieh Warshel, John Wiley & Sons, INC., New York,1997. 6- Computational Medicinal Chemistry for Drug Discovery, Patrick Bultinck, Hans De Winter WiIf ried Langenaeke, MARCEL DEKKER,INC, NEW YORK: BASEL. 7- Essentials of Computational Chemistry, Theories and Models, Second Edition, Christopher J. Cramer, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, 2004. 8-Quantum Biochemistry, Chrif F. Matta, 2010, WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, ISBN: 978-3-527-32322-7. 138