Virtual Screening

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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


				
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