Virtual Screening

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 Non-Specific 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