Introduction to Chemoinformatics and Drug Discovery
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1 Introduction to Chemoinformatics and Drug Discovery Irene Kouskoumvekaki Associate Professor February 15 th, 2013
2 The Chemical Space There are atoms and space. Everything else is opinion. Democritus (ca. 460 BC ca. 370 BC) 2 CBS, Department of Systems Biology
3 Systems Chemical Biology 3 CBS, Department of Systems Biology
4 Today s Learning Objectives To introduce you to the field of chemoinformatics and the most commonly used terms and methods To show examples of the use of chemoinformatics in modern drug research To give you practical experience through hands-on exercises 4 CBS, Department of Systems Biology
5 Drug Discovery Process Disease Drug target Drug candidate Animal studies Clinical studies Marketed drug 5 CBS, Department of Systems Biology
6 A drug candidate... is a (ligand) compound that binds to a biological target (protein, enzyme, receptor,...) and in this way either initiates a process (agonist) or inhibits it (antagonist/inhibitor) The structure/conformation of the ligand is complementary to the space defined by the protein s active site The binding is caused by favorable interactions between the ligand and the side chains of the amino acids in the active site. (electrostatic interactions, hydrogen bonds, hydrophobic contacts...) 6 CBS, Department of Systems Biology
7 7 CBS, Department of Systems Biology
8 Wet-lab drug discovery process HTS Screening collection 10 6 cmp. Actives 10 3 actives 8 CBS, Department of Systems Biology
9 Wet-lab drug discovery process HTS Screening collection 10 6 cmp. Actives 10 3 actives High rate of false actives!!! High throughput is not enough to get high output.. 9 CBS, Department of Systems Biology
10 Wet-lab drug discovery process Screening collection 10 6 cmp. HTS Actives 10 3 actives Follow-up Chemical structure Purity Mechanism Activity value 10 CBS, Department of Systems Biology
11 Wet-lab drug discovery process HTS Follow-up Screening collection 10 6 cmp. Actives 10 3 actives Hits 1-10 hits Analogues synthesis and testing ADMET properties 11 CBS, Department of Systems Biology
12 Wet-lab drug discovery process HTS Follow-up Hit-to-lead Screening collection Actives Hits Lead series 10 6 cmp actives 1-10 hits 0-3 lead series Analogues synthesis and testing ADMET properties 12 CBS, Department of Systems Biology
13 Wet-lab drug discovery process HTS Follow-up Hit-to-lead Lead-to-drug Screening collection Actives Hits Lead series Drug candidate 10 6 cmp actives 1-10 hits 0-3 lead series 0-1 Analogues synthesis and testing ADMET properties 13 CBS, Department of Systems Biology
14 14 CBS, Department of Systems Biology
15 Failures 15 CBS, Department of Systems Biology
16 We need more.. to find less.. 16 CBS, Department of Systems Biology
17 Drug Discovery Process Chemoinformatics Disease Drug target Drug candidate Animal studies Clinical studies Marketed drug 17 CBS, Department of Systems Biology
18 Wet-lab + Dry-lab drug discovery in vitro in silico + in vitro Diverse set of molecules tested in the lab Computational methods to select subsets (to be tested in the lab) based on prediction of drug-likeness, solubility, binding, pharmacokinetics, toxicity, side effects, CBS, Department of Systems Biology
19 The Lipinski rule of five for druglikeness prediction Octanol-water partition coefficient (logp) 5 Molecular weight 500 # hydrogen bond acceptors (HBA) 10 # hydrogen bond donors (HBD) 5 If two or more of these rules are violated, the compound might have problems with oral bioavailability. (Lipinski et al., Adv. Drug Delivery Rev., 23, 1997, 3.) 19 CBS, Department of Systems Biology
20 Exercise : Drug-likeness by rule of five Go to the following webpage Draw proguanil, calculate properties and decide if this compound is a drug according to rule of five 20 CBS, Department of Systems Biology
21 21 CBS, Department of Systems Biology
22 Proguanil antimalarian tablets 22 CBS, Department of Systems Biology
23 Chemoinformatics Gathering and systematic use of chemical information, and application of this information to predict the behavior of unknown compounds in silico. data prediction 23 CBS, Department of Systems Biology
24 Major Aspects of Chemoinformatics Databases: Development of databases for storage and retrieval of small molecule structures and their properties. Machine learning: Training of Decision Trees, Neural Networks, Self Organizing Maps, etc. on molecular data. Predictions: Molecular properties relevant to drugs, virtual screening of chemical libraries, system chemical biology networks 24 CBS, Department of Systems Biology
25 Major Aspects of Chemoinformatics Databases: Development of databases for storage and retrieval of small molecule structures and their properties. Machine learning: Training of Decision Trees, Neural Networks, Self Organizing Maps, etc. on molecular data. Predictions: Molecular properties relevant to drugs, virtual screening of chemical libraries, system chemical biology networks 25 CBS, Department of Systems Biology
26 26 CBS, Department of Systems Biology
27 Representing a chemical structure How much information do you want to include? atoms present connections between atoms bond types stereochemical configuration charges isotopes 3D-coordinates for atoms C 8 H 9 NO 3 27 CBS, Department of Systems Biology
28 Representing a chemical structure How much information do you want to include? atoms present connections between atoms bond types stereochemical configuration charges isotopes 3D-coordinates for atoms 28 CBS, Department of Systems Biology
29 Representing a chemical structure How much information do you want to include? atoms present connections between atoms bond types (aromatic ring identification) stereochemical configuration charges isotopes 3D-coordinates for atoms 29 CBS, Department of Systems Biology
30 Representing a chemical structure How much information do you want to include? atoms present connections between atoms bond types stereochemical configuration charges isotopes 3D-coordinates for atoms 30 CBS, Department of Systems Biology
31 Representing a chemical structure How much information do you want to include? atoms present connections between atoms bond types stereochemical configuration charges isotopes 3D-coordinates for atoms 31 CBS, Department of Systems Biology
32 Representing a chemical structure How much information do you want to include? atoms present connections between atoms bond types stereochemical configuration charges isotopes 3D-coordinates for atoms 32 CBS, Department of Systems Biology
33 Representing a chemical structure How much information do you want to include? atoms present connections between atoms bond types stereochemical configuration charges isotopes 3D-coordinates for atoms 33 CBS, Department of Systems Biology
34 From chemists to representations 34 CBS, Department of Systems Biology
35 Structural representation of molecules Line notations Structural representation of molecules Connection tables 35 CBS, Department of Systems Biology
36 SMILES (Simplified Molecular Input Line Entry System) Canonical SMILES: unique for each structure Isomeric SMILES: describe isotopism, configuration around double bonds and tetrahedral centers, chirality 36 CBS, Department of Systems Biology
37 InChI (IUPAC International Chemical Identifier) 37 CBS, Department of Systems Biology
38 MOLfile format (.sdf) 38 CBS, Department of Systems Biology
39 Small molecule databases 39 CBS, Department of Systems Biology
40 Try it yourself! Go to PubChem: pubchem.ncbi.nlm.nih.gov/ Type proguanil and press Go Click on the first result on the list 40 CBS, Department of Systems Biology
41 Try it yourself! Scroll down and find the SMILES and InChI 41 CBS, Department of Systems Biology
42 Try it yourself! Click on SDF (top right icon) Select: 2D SDF: Display 42 CBS, Department of Systems Biology
43 Try it yourself! Go back and click again on SDF Select: 3D SDF: Display 43 CBS, Department of Systems Biology
44 44 CBS, Department of Systems Biology Questions?
45 45 CBS, Department of Systems Biology
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