2. Cheminformatics Basics
Ryszard Kubinski

3. Why do we care about cheminformatics?
Reasons:
More and more drug development is being done on the computer.
Prediction of drug toxicity and potency can be done based on structure.
Prediction models based on structure can reduce attrition rates in clinical trials.
Virtual docking can be performed on thousands of compounds at low cost.

4. What is the alternative?
High throughput screening using robotics.

5. Common Cheminformatics tools
Databases that contain binding and structural data about ligands for specific proteins
Programs for data extraction/processing
Programs for virtual screening of ligands at specific docking sites on proteins

6. Common Ligand Databases
Kegg Ligand Database
Bindingdb.org
ChEMBL
These databases provide pharmacological information about chemical compounds in CSV file format which are easy to work with for data extraction

7. Today’s target: HER2
Normally, HER2 is a receptor that mediates cell growth and division in the breast tissue.
In certain types of breast cancer, HER2 is overexpressed and there is an excess of cellular replication, this leads to cancer.
Blocking this overexpressed receptor with a molecular inhibitor has been shown to be an effective treatment against certain types of breast cancer.

8. Understanding chemical properties of different potential inhibitors.
Tutorial Plan
Today we will look at some of the chemical structures that bind to Her2.
Plan for the tutorial:
How to fetch data?
Data Preprocessing
Understanding chemical properties of different potential inhibitors.
Virtual Docking

9. Fetching Ligand Data

10. Fetching Ligand Data Continued

11. Fetching Ligand Data Continued

12. Fetching Ligand Data Continued
Download xls and convert to CSV.

13. Understanding the Data
IC50
AlogP
PSA
Molecular Weight
SMILE structure

14. IC50
The concentration of drug/ inhibitor needed to reduce the activity of a protein to 50%.
0-100nM (nanomolar) concentration for IC50 corresponds to compounds which will be effective drug candidates.
Compounds with IC50 >100nM need structural modifications because they are not potent enough.
The lower the IC50 the better, the less drug is needed to achieve inhibition -> less off target effects.

15. ALogP Logarithm of octanol/water partition coefficient.
Ratio of how soluble drug is in octanol vs in water.
LogP answers the question of how lipid soluble our drug is.
For a drug to be absorbed while travelling through the gastrointestinal tract, it must first pass through lipid bilayers in the intestine.
For efficient transport, the drug must be hydrophobic to enter into the lipid bilayer, but not so hydrophobic that it cannot partition out again.

16. Polar Surface Area (PSA)
Surface sum over all polar atoms
Dictates the drug’s ability to permeate through a cell membrane.
The more polar a compound, the less likely that it will enter a cell.
PSA <140 Angstrom for compound to be a drug candidate.
PSA <90 Angstrom for compound to cross Blood Brain Barrier.

17. Molecular Weight
Sum of the masses of each atom in a chemical compound.

18. SMILES structure
A String of characters representing a chemical compound.
Ex: CC(C)(C)Nc1c(Nc2ccnc(Nc3ccc(cc3)- c3ccncc3)n2)c(=O)c1=O

19. Lipinski’s Rule of Five
Combining molecular descriptors in order to determine if compound is drug-like or not.
LogP : <5
Less than 5 hydrogen bond donors
No more than 10 hydrogen bond acceptors
Molecular mass <500 Da.

20. Please open Sublime Text 3 or a Python IDE
Data Preprocessing
---CODING SECTION---
Please open Sublime Text 3 or a Python IDE

21. Virtual Docking Intro Drug binds to protein like a lock & key.
We predict the preferred orientation of a molecule when it is bound to another.
Scoring functions can predict the binding affinity.
In essence, virtual docking can be used in structure based drug design because it can predict how small molecules bind to a protein.

22. Virtual Scoring
Ligand-Protein interaction energies are calculated for a Ligand-Protein complex.
Ligand can take different poses in the protein’s active pocket and these different poses can have different energies.
Autodock Vina scores these poses and returns their δG.
The more negative the δG, the more stable the binding is and the higher the probability that it would occur in real biological system.

23. Virtual Docking Applications and Caveats
Docking CAN be used to guide drug design and help adjust structures towards more optimal designs for in-vitro binding assays.
Docking CANNOT be used to say if a molecule will be a good binder/ drug for sure.
Compared to in vitro assays, virtual docking has higher probability that results obtained are inaccurate.
Crystal structure of receptor/protein that we dock into might not always be complete and accurate docking will not be possible.

24. Virtual Docking Software
Software to be used:
Autodock Vina
Openbabel
Autodock Tools
UCSF Chimera

25. Please open your terminal or your Ubuntu terminal
Preparing Ligands
---CODING SECTION---
Please open your terminal or your Ubuntu terminal

26. Fetching a receptor: RCSB.org

27. Downloading a PDB
PDB: 3 Dimensional structural data about a protein structure.
-Each atom of the protein is plotted in a 3d grid.

28. Our PDB (modified) Download her2.zip, unzip from:
studentpharma.ca/pharmahackathon

29. Autodock Tools – Prepping Receptor
Please Open Autodock Tools
File-> read molecule
Read her2.pdb from your working directory.

30. Autodock Tools – Prepping Receptor
Processing:
Edit->Add-> Hydrogens.
Grid- >Macromolecule -> Choose-> her2
Save her2.pdbqt in working directory.

31. Autodock Tools – Configuration File Generator
---CODING SECTION---
Please open your terminal or your Ubuntu terminal and
Sublime3 Text Editor.

32. Virtual Docking Scores
Looking at your scores can be done by navigating to the logs file in your directory.
Scores for each of 8 binding poses are shown in the txt files.

33. Visualizing your Virtual Docking
Open UCSF Chimera
Open any file from your docking output
Select “pdb” as file type
Open your her2.pdb as well
Go to presets -> Interactive 3 to get hydrophobicity structure

34. Acknowledgments McGill University Pharmacology Department
NuChem Therapeutics
MICM
InVivo AI
District3 Innovation Centre
Novartis