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Virtual Screening
C371
Fall 2004
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
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
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
Virtual Screening Methods for NonSpecific Targets
• Prediction of the likelihood that a molecule
has “drug-like” characteristics and
possesses desired physicochemical
properties
“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)
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)
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
“Lead-Likeness”
• Increase in molecular complexity occurs
during optimization phase of a lead
molecule
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
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
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 2.3-3.5
Angstroms
– Pair-wise linear potential looks at attractive and
repulsive regions, taking into account steric and
hydrogen bonding interactions
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
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
ADMET (cont’d)
• Permeability through the intestinal cell
membrane or blood-brain barrier
– Paucity of experimental data in vivo studies,
especially for humans
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
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
Descriptors Based on 3D Fields
• Molecular descriptors quantify the
molecule’s overall size and shape and the
balance between hydrophilicity,
hydrophobicity, and hydrogen bonding
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
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