Virtual screening techniques

drug discovery-virtual screening

1. Virtual screening techniques Presented by- Presented to- Rohit Pal Dr. Bhupinder Kumar M.Pharm ( Pharmacuetcal Chemistry, 2 nd Semester) Department of Pharmacuetical Chemistry ISF College of Pharmacy, Moga (Punjab)

2. Table of content Introduction Virtual screening methods Ligand based Similarity Searching Pharmacophore Mapping Machine learning methods Structure based Protein Ligand Docking Merits and Demerits Hybrid virtual screening

3. Drug discovery

4. Introduction Virtual screening  ( VS ) is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target typically a protein receptor or enzyme. Virtual screening has been defined as the "automatically evaluating very large libraries of compounds" using computer programs.

5. As this definition suggests, VS has largely been a numbers game focusing on how the enormous chemical space of over 10 60  conceivable compounds can be filtered to a manageable number that can be synthesized, purchased, and tested. Although searching the entire chemical universe may be a theoretically interesting problem, more practical VS scenarios focus on designing and optimizing targeted combinatorial libraries and enriching libraries of available compounds from in-house compound repositories or vendor offerings. As the accuracy of the method has increased, virtual screening has become an integral part of the drug discovery process. Virtual Screening can be used to select in house database compounds for screening, choose compounds that can be purchased externally, and to choose which compound should be synthesized next.

6. r o h I t F I L T E R Filters— Shape Conformers Any rule like Lipinski’s rule, Veber rule,Ghose rule

7. Techniques of virtual screening

8. Methods of virtual screening Ligand-based Structure-based Hybrid methods

9. Ligand Based Virtual Screening In LBVS process, the most effective biologically active lead molecule is detected using structural or topological similarity or pharmacophoric similarity search. Taking into consideration several criteria such as structure as well as shape of individual fragment or electrostatic properties of the molecule carries out the similarity comparisons. The leads generated are ranked based on their similarity score, obtained using different methods or algorithms .

10. Classification of ligand based virtual screening

11. Similarity Searching What is it ?? Chemical, pharmacological or biological properties of two compounds match. The more the common features, the higher the similarity between two molecules. Chemical The two structures on top are chemically similar to each other. This is reflected in their common sub-graph, or scaffold: they share 14 atoms Pharmacophore The two structures above are less similar chemically (topologically) yet have the same pharmacological activity, namely they both are Angiotensin-Converting Enzyme (ACE) inhibitors

12. Similarity scale 80-99% 100% 60-79% 30-59% Below 30% Known inhhbitor Downloaded library of ligands

13. 2D fingerprints: molecules represented as binary vectors Each bit in the bit string (binary vector) represents one molecular fragment. Typical length is ~1000 bits. The bit string for a molecule records the presence (“1”) or absence (“0”) of each fragment in the molecule. Originally developed for speeding up substructure search for a query substructure to be present in a database molecule. Each bit set to “1” in the query must also be set to “1” in the database structure. Similarity is based on determining the number of bits that are common to two structures .

14. 3D based similarity Shape-based -ROCS (Rapid Overlay of Chemical Structures). - Silicos-it.com (Shape it). Computationally more expensive than 2D methods. Requires consideration of conformational flexibility Rigid search - based on a single conformer Flexible search.

15. Pharmacophore Mapping Pharmacophore is an abstract description of molecular features which are necessary for molecular recognition of a ligand by a biological macromolecules. Pharmacophore mapping is the definition and placement of pharmacophoric features and the alignment techniques used to overlay 3-D. It consist of three steps:- Identifying common binding element that are responsible for biological activity. Generating potential conformations that active compound may adopt. Determining the 3-D relationship between pharmacophore element in each conformation generated. Pharmacophore Mapping Software : Window and Linux based protein modelling software. Programs that perform pharmacophore based searches are 3D search UNITY, MACCS-3D and ROCS.

16. Generating pharmacophore models: Ligand-based Rimonabant Trying to predict how the ligands will bind to the receptor without knowing the structure of the receptor

17. 3-D Pharmacophores A three-dimensional pharmacophore specifies the spatial relation-ships between the groups Expressed as distance ranges, angles and planes

18. Workflow of pharmacophore modeling

19. Machine learning methods SAR Modeling: Use knowledge of known active and known inactive compounds to build a predictive model Quantitative-Structure Activity Relationships (QSARs) Long established ( Hansch analysis, Free-Wilson analysis) Generally restricted to small, homogeneous datasets e.g. lead optimization. Structure-Activity Relationships (SARs) “ Activity” data is usually treated qualitatively Can be used with data consisting of diverse structural classes and multiple binding modes Some resistance to noisy data (HTS data) Resulting models used to prioritize compounds for lead finding (not to identify candidates or drugs)

20. Structural based virtual screening Structural based virtual screening begins with the identification of a potential ligand-binding site on the target molecule. Ideally the target site is a pocket or protuberance having a variety of probable hydrogen bond donors and acceptors, hydrophobic characteristics, and with molecular adherence surfaces. The ligand-binding site can be the active site as in an enzyme; an assembly site with another macromolecule or a communication site, which is necessary in the mechanism of the molecule. Determining the structure of a target protein by NMR, X-ray crystallography or homology modelling befalls as a major and initializing stair in structure based virtual screening. Numerous X-ray crystallographic and NMR studies are helpful in determining the Virtual Screening perimental structures of ligands bound to the enzymes, serve as a major source of ideas for analogue design, intern useful for the docking studies.

21. Methods of structure based virtual screening

22. Protein Ligand Docking Computational method which mimics the binding of a ligand to a protein. It predicts - The pose( the geometry of the ligand in the binding site of the molecule in the binding site The binding affinity or score representing the strength of binding.

23. The search space The difficulty with protein–ligand docking is in part due to the fact that it involves many degrees of freedom The translation and rotation of one molecule relative to another involves six degrees of freedom These are in addition the conformational degrees of freedom of both the ligand and the protein The solvent may also play a significant role in determining the protein–ligand geometry (often ignored though) The search algorithm generates poses, orientations of particular conformations of the molecule in the binding site Tries to cover the search space, if not exhaustively, then as extensively as possible There is a trade off between time and search space coverage

24. Dock Algorithms DOCK: first docking program by Kuntz et al. 1982 − Based on shape complementarity and rigid ligands Current algorithms Fragment-based methods: FlexX , DOCK (since version 4.0) Monte Carlo/Simulated annealing: QXP(Flo), Autodock , Affinity & LigandFit ( Accelrys ) Genetic algorithms: GOLD, AutoDock (since version 3.0) Systematic search: FRED ( OpenEye ), Glide (Schrödinger)

25. Merits : Computational. Only high scoring ligands. Reducing real laboratory experiments and accelerates drug discovery. Demerits : Molecular complexity/diversity. False positives. Synthesis issue.

26. Hybrid Virtual Screening Mostly, people in pharmaceutical industry does not follow a specific route they follow a hybrid of methods as discussed in previous slide. Shape Similarity Structure based Pharmacophore Docking based Screening Post Process Starting database Potential Lead compounds Filter : Rule of 5 , ADME, TOX ROCS, FlexS Pharmacophore based Screening Ligand Scout, Phase, Ligand fit Prepared database Dock, Gold, Glide, ICM Cscore, MM/PBSA, Solvation Corrections LUDI, Ligand Scout, Phase, DrugScore Cleaning Molecules Remove isotopes, salts and mixtures Protonation and normalization Remove duplicates and invalid structures Filtering Molecules User defined or other filter Remove problematic moieties using PAINS, Frequent Hitters etc. PhyChem property descriptor calculation and filtration Apply protonation at pH 7.4

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