1. Virtual Screening

2. INTRODUCTION
Virtual screening – Computational or in silico analog of biological screening
Score, rank, and/or filter a set of structures using one or more computational procedures
Helps decide:
Which compounds to screen
Which libraries to synthesize
Which compounds to purchase from an external source
Also used to analyze the results of HTS screening runs

3. Ways to Assess Structures from a Virtual Screening Experiment
Use a previously derived mathematical model that predicts the biological activity of each structure
Run substructure queries to eliminate molecules with undesirable functionality
Use a docking program to ID structures predicted to bind strongly to the active site of a protein (if target structure is known)
Filters remove structures not wanted in a succession of screening methods

4. Main Classes of Virtual Screening Methods
Depend on the amount of structural and bioactivity data available
One active molecule known: perform similarity search (ligand-based virtual screening)
Several active molecules known: try to ID a common 3D pharmacophore, then do a 3D database search
Reasonable number of active and inactive structures known: train a machine learning technique
3D structure of the protein known: use protein-ligand docking

5. Virtual Screening Methods for Non-Specific Targets
Prediction of the likelihood that a molecule has “drug-like” characteristics and possesses desired physicochemical properties

6. “DRUG-LIKENESS” AND COMPOUND FILTERS
Which features of drug molecules confer biological activity?
Substructure filters to eliminate molecules known to have problems
For a specific target, may have to modify or extend the filters
Analyze the values of simple properties (MW, logP, No. of rotatable bonds)

7. Lipinski Rule of Five
Poor absorption or permeation is more likely when:
MW > 500
LogP >5
More than 5 H-bond donors (sum of OH and NH groups)
More than 10 H-bond acceptors (sum of N and O atoms)

8. Other Findings 70% of drug-like molecules have:
Between 0 and 2 H-bond donors
Between 2 and 9 H-bond acceptors
Between 2 and 8 rotatable bonds
Between 1 and 4 rings
Other techniques (neural networks, genetic algorithms, decision trees) consider more complex possibilities

9. “Lead-Likeness”
Increase in molecular complexity occurs during optimization phase of a lead molecule

10. STRUCTURE-BASED VIRTUAL SCREENING
Protein-Ligand Docking
Aims to predict 3D structures when a molecule “docks” to a protein
Need a way to explore the space of possible protein-ligand geometries (poses)
Need to score or rank the poses to ID most likely binding mode and assign a priority to the molecules
Problem: involves many degrees of freedom (rotation, conformation) and solvent effects
Conformations of ligands in complexes often have very similar geometries to minimum-energy conformations of the isolated ligand

11. Protein-Ligand Docking Methods
Modern methods explore orientational and conformational degrees of freedom at the same time
Monte Carlo algorithms (change conformation of the ligand or subject the molecule to a translation or rotation within the binding site
Genetic algorithms
Incremental construction approaches
Problem: Lack of a comprehensive knowledge base

12. Distinguish “Docking” and “Scoring”
Docking involves the prediction of the binding mode of individual molecules
Goal: ID orientation closest in geometry to the observed X-ray structure
Scoring ranks the ligands using some function related to the free energy of association of the two units
DOCK function looks at atom pairs of between Angstroms
Pair-wise linear potential looks at attractive and repulsive regions, taking into account steric and hydrogen bonding interactions

13. Structure-Based Virtual Screening: Other Aspects
Computationally intensive and complex
Multitude of possible parameters figure into docking programs
Docking programs require 3D conformation as the starting point or require partial atomic charges for protein and ligand
X-Ray Crystallographic studies don’t include hydrogens, but most docking programs require them

14. PREDICTION OF ADMET PROPERTIES
Requirements for a drug:
Must bind tightly to the biological target in vivo
Must pass through one or more physiological bariers (cell membrane or blood-brain barrier)
Must remain long enough to take effect
Must be removed from the body by metabolism, excretion, or other means
ADMET: Absorption, Distribution, metabolism, Excretion (Elimination), Toxicity

15. ADMET (cont’d)
Permeability through the intestinal cell membrane or blood-brain barrier
Paucity of experimental data in vivo studies, especially for humans

16. Hydrogen Bonding Descriptors
Count of the numbers of donors and acceptors
Calculation of the overall propensity to be an acceptor or donor
Modeling solubility, octanol/water partition coefficient, and blood-brain barrier permeability

17. Polar Surface Area
Amount of molecular surface due to polar atoms (N and O plus attached hydrogens)
Especially good for prediction of oral absorption and brain penetration
Polar surface are greater than 140 square Angstroms has been associated with poor absorption

18. Descriptors Based on 3D Fields
Molecular descriptors quantify the molecule’s overall size and shape and the balance between hydrophilicity, hydrophobicity, and hydrogen bonding

19. Toxicity Prediction Very difficult problem
Most limit studies to single toxicological phenomenon or a single class of compounds (e.g., Polycyclic aromatic hydrocarbons)
Some based on known toxic effects

20. SUMMARY
Virtual screening methods are central to many cheminformatics problems in:
Design
Selection
Analysis
Increasing numbers of molecules can be evaluated using these techniques
Reliability and accuracy remain as problems in docking and predicting ADMET properties
Need much more reliable and consistent experimental data