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Pro-/Seminar SS 08: Molecular Modeling in Drug Design

General Information

Lecturer: PD Dr. Michael Hutter

Time: All meetings take place in building E1 3, seminar room 015, 2:00 pm - 4:30 pm
meeting 1: Friday, May 9, topics 1, 2 
meeting 2: Friday, May 23, topics 3, 4
meeting 3: Friday, June 6, topic 5  
meeting 4: Friday, June 13, topics 6, 7, 8
meeting 5: Friday, June 20, topics 9, 10
meeting 6: Friday, July 4, topics 11, 12, 13

Proseminar: Vorkenntnisse entsprechend dem 4. Studiensemester
Seminar: Vorkenntnisse entsprechend dem Umfang des Bachelorstudiums/Knowledge corresponding to the bachelor course


Preliminary discussion and placement of the topics: Friday, April 18, at 2:00 pm in building C7 1, room 1.08

Condition for certification: successful presentation, regular (≥ 75 %) participation

Maximum number of participants: 12

study regulations 2006: 5 (proseminar) or 7 (seminar)
study regulations 2004: 5 (proseminar) or 8 (seminar)
study regulations 2001: 9 (proseminar and seminar)

Topics (in order of presentation):  

  1. Force Fields: Atom types, force fields of proteins, DNA, organic compounds, inorganic compounds, AMBER, CHARMM, MM3, UFF
  2. Minimization Algorithms: local, global, with and whithout the use of gradients, DFP, eigenvector following, simplex, simulated annealing, genetic algorithms
  3. Solvent Models in Force Fields: explicit and implicit water models, OPLS force field, Generalized Born Model, Poisson-Boltzmann, Ewald Summations 
  4. Scoring Functions used for Docking: Energy vs. information based, calibration, DrugScore, consensus scoring
  5. Pharmacophores and de novo Generation: derivation, virtual screening of databases
  6. Molecular Descriptors: concepts, 1D, 2D, 3D-descriptors, topological, similarity indices and measurements, CoMFA, GRID
  7. Quantitative Structure-Activity Relationship: multivariate regression analysis, partial least sqaure, leave one out, cross-validation, stratified testing, boostrapping, t-test, p-values
  8. Classification: machine learning algorithms, neural networks, support vector machines, recursive partitioning, random forests, particle swarms
  9. Encoding Molecular Structures: File format of pdb, SMILES, file format conversion, Open Babel, visualizing molecules
  10. Generation of 3D Molecular Structures: 3D coordinates from scratch, CORINA
  11. In silico Drug Development: Lipinski´s rule of five, drug-likeness criteria of Oprea, Wendolowski et al., leadlike vs. druglike
  12. Publicly Accesible Substance Databases: RCSB, Relibase, PubChem, AffinDB, searching in databases for similar compounds using SMILES and SMARTS 
  13. Conformational Search: conf. space, systematic search, tree search, random and stochastic search, distance geometry
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