1. Chemoinformatics in Drug Design
Irene Kouskoumvekaki,
Associate Professor,
Computational Chemical Biology,
CBS, DTU-Systems Biology
Biological Sequence Analysis, May 6, 2011

2. Computational Chemical Biology group
Tudor Oprea
Guest Professor
Olivier Taboureau
Associate Professor
Irene Kouskoumvekaki
Associate Professor
Sonny Kim Nielsen
PhD student
Kasper Jensen
PhD student
Ulrik Plesner
master student

4. Word cloud

5. Definition: Chemoinformatics
Gathering and systematic use of chemical information, and application of this information to predict the behavior of unknown compounds in silico.
data
prediction

6. Definition: 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)
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...)

7. In vitro / In silico studies
Drug Discovery
Animal studies
Disease
Biological Target
Drug candidate
In vitro / In silico studies
Clinical studies

8. The Drug Discovery Process
Chemoinformatics

9. The Drug Discovery Process
We identify/predict the binding pocket
We know the structure of the biological target
MKTAALAPLFFLPSALATTVYLA
GDSTMAKNGGGSGTNGWGEYL
ASYLSATVVNDAVAGRSAR…(etc)
Challenge:
To design an organic molecule that would bind strong enough to the biological target and modute it’s activity.
New drug candidate

10. Example: – Alzheimer’s disease
What is it?
Alzheimer's is a disease that causes failure of brain functions and dementia. It starts with bad memory and disability to function in common everyday activities.
How do you get it?
Alzheimer's disease is the result of malfunctioning neurons at different parts of the brain. This, in turn, is due to an inbalance in the concentration of neurotranmitters.

11. Example: – Alzheimer’s disease
How can we treat it?
Acetylkolin neurotransmitter
Drug against Alzheimer’s

12. Old School Drug discovery process
HTS
Follow-up
Hit-to-lead
Lead-to-drug
Screening collection
Lead series
Drug candidate
Actives
Hits
106 cmp.
103 actives
1-10 hits
0-3 lead series
0-1
Clinical trials
High rate of false positives !!!

14. Failures

15. in vitro in silico + in vitro
4/17/2017
Drug discovery in the 21st Century
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, ...
In silico results can be used to make in vivo methods more efficient. If there are several thousand compounds available for testing, in silico methods are used to identify those that are most likely to be active and these would take priority for screening.

16. The Lipinski ‘rule of five’ for drug-likeness 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.)

17. Major Aspects of Chemoinformatics
Experimental data
Model generation
Prediction for unknown compounds

18. Major Aspects of Chemoinformatics
Information Acquisition and Management: Methods for collecting data (mainly experimental). Development of databases for storage and retrieval of information.
Information Use: Data analysis, correlation and model building.
Information Application: Prediction of molecular properties relevant to chemical and biochemical sciences.

19. Major Aspects of Chemoinformatics
Information Acquisition and Management: Methods for collecting data (mainly experimental). Development of databases for storage and retrieval of information.
Information Use: Data analysis, correlation and model building.
Information Application: Prediction of molecular properties relevant to chemical and biochemical sciences.

20. Information Acquisition and Management

21. Small molecule databases
One tricky thing when storing

22. Growth In PubChem Substances & Compounds
Recent count: Substance: 72,156,631 Compound: 28,807,320 Rule of 5: 20,692,980
The PubChem Compound Database contains validated chemical depiction information that is provided to describe substances in PubChem Substance.
The PubChem substance database contains chemical structures, synonyms, registration IDs, description, related urls, database cross-reference links to PubMed, protein 3D structures, and biological screening results. If the contents of a chemical sample are known, the description includes links to PubChem Compound.

23. Searching in PubChem

24. Structural representation of molecules

25. Major Aspects of Chemoinformatics
Information Acquisition and Management: Methods for collecting data (mainly experimental). Development of databases for storage and retrieval of information.
Information Use: Data analysis, correlation and model building.
Information Application: Prediction of molecular properties relevant to chemical and biochemical sciences.

26. Beyond the Lipinski Rule of 5...
Chemometrics: The application of mathematical or statistical methods to chemical data (simple, linear methods)
e.g. Principal Component Analysis
Machine Learning: The design and development of algorithms and techniques that allow computers to learn (complex, non-linear algorithms)
e.g. Artificial Neural Networks, K-means clustering

27. Major Aspects of Chemoinformatics
Information Acquisition and Management: Methods for collecting data (mainly experimental). Development of databases for storage and retrieval of information.
Information Use: Data analysis, correlation and model building.
Information Application: Prediction of molecular properties relevant to chemical and biochemical sciences.

28. Prediction of Solubility, ADME & Toxicity
Membrane
transfer
Liver extraction
Dissolution
Solid
drug
Systemic
circulation
Drug in
solution
Absorbed
drug
Solubility
Absorption
Metabolism

29. Prediction of biological activity/selectivity

30. Prediction models at CBS

31. Virtual screening
Computational techniques for a rapid assessment of large libraries of chemical structures in order to guide the selection of likely drug candidates.
Exploit knowledge of the active ligand molecule or the protein target.

32. Virtual Screening Flavors
TARGET-BASED
1D filters
e.g. Lipinskis Rule of Five 1D
LIGAND-BASED

33. Molecular similarity on the Chemical Space
Similar Property Principle – Molecules having similar structures and properties are expected to exhibit similar biological activity. (Not always true!)
Thus, molecules that are located closely together in the chemical space are often considered to be functionally related.

34. Ligand-based VS: Fingerprints
widely used similarity search tool
consists of descriptors encoded as bit strings
Bit strings of query and database are compared using similarity metric such as Tanimoto coefficient
MACCS fingerprints: 166 structural keys
that answer questions of the type:
Is there a ring of size 4?
Is at least one F, Br, Cl, or I present?
where the answer is either
TRUE (1) or FALSE (0)

35. Tanimoto Similarity
or 90% similarity

36. Tanimoto Similarity

37. Ligand-based VS: Pharmacophore

38. Structure-based Virtual Screening: Docking
Binding pocket of target
Library of small compounds
Given a protein and a database of ligands, docking scores determine which ligands are most likely to bind.

39. Energy of binding Binding pocket of target Library of small compounds
-1 kcal/mol
-10 kcal/mol
+10 kcal/mol
+1 kcal/mol
For any spontaneous change in a closed system, the change in [Gibbs] free energy equals the change in enthalpy minus the change in entropy times the temperature.
ΔG = ΔH - TΔS
Torsional free E
vdW
Hbond
Desolvation E
Electrostatic E

40. “Docking” and “Scoring”
Docking involves the prediction of the binding mode of individual molecules
Goal: new ligand orientation closest in geometry to the observed X-ray structure (Conformations of ligands in complexes often have very similar geometries to minimum-energy conformations of the isolated ligand)
Scoring ranks the ligands using some function related to the free energy of association of the two partners, looking at attractive and repulsive regions and taking into account steric and hydrogen bonding interactions
Goal: new ligand score closest in value to the docking score of the X-ray structure

41. Docking algorithms Most exhaustive algorithms:
Accurate prediction of a binding pose
Most efficient algorithms
Docking of small ligand databases in reasonable time
Rapid algorithms
Virtual high-throughput screening of millions of compounds

42. Scoring functions Molecular mechanics force field-based
Score is estimated by summing the strength of intermolecular van der Waals and electrostatic interactions between all atoms of the ligand-target complex
-CHARMM, AMBER
Empirical-based
Based on summing various types of interactions between the two binding partners (hydrogen bonds, hydrophobic, …)
- ChemScore, GlideScore, AutoDock
Knowledge-based
Based on statistical observations of intermolecular close contacts from large 3D databases, which are used to derive potentials or mean forces
-PMF, DrugScore

43. Combination of pharmacophore, docking and molecular dynamics (MD) screens
Ligand-based VS
good enrichment of candidate molecules from the screening of large databases with less computational efforts
too coarse to pick up subtle differences induced by small structural variations in the ligands
many options for model refinement
Structure-based VS
better fit for analyzing smaller sets of compounds, especially in retrospective analysis
include all possible interactions thus allowing the detection of unexpected binding modes
Changing parameters for docking algorithms and scores is demanding
Mutants are being developed:
pharmacophore methods with information about the target’s binding site
docking programs that incorporate pharmacophore constraints

45. Public Web Chemoinformatics Tools http://pasilla.health.unm.edu/

46. ChemSpider www.chemspider.com

47. Open Babel http://openbabel.org/wiki/Main_page

48. D. Vidal et al, Ligand-based Approaches to In Silico Pharmacology, Chemoinformatics and Computational Chemical Biology, Ed J. Bajorath, Springer, 2011

49. Questions?