Molecular Descriptors and Virtual Screening using Datamining approach Abhik Seal OSDD Ch

1. Molecular Descriptors and Virtual Screening using Datamining approach Abhik SealOSDD Cheminformatics

2. Aim of Cheminformatics Project • To screen molecules interacting with the Potential TB targets using classifiers. • Select the selected molecules and dock with Targets to further screen the molecules for leads. • Use cheminformatics techniques such as QSAR ,3D qsar, ADMET to look for potential leads and design Drugs using the leads – by building combinatorial libraries.

3. Compounds + biological activity QSAR New compounds with improved biological activity QSAR and Drug Design

4. What is QSAR? • QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics. A general formula for a quantitative structure-activity relationship (QSAR) can be given by the following: activity = f (molecular or fragmental properties) • QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds.

5. Molecule Properties SPC : Structure Property Correlation CHEMICAL PROPERTIES pKa Log P Solubility Stability MOLECULE STRUCTURE INTRINSIC PROPERTIES Molar Volume Connectivity Indices Charge Distribution Molecular Weight Polar surface Area.... ....... BIOLOGICAL PROPERTIES Activity Toxicity Biotransformation Pharmacokinetics

6. Molecule Descriptors • Molecular descriptors are numerical values that • characterize properties of molecules. • The descriptors fall into Four classes . • a) Topological • b) Geometrical • c) Electronic • d) Hybrid or 3D Descriptors

7. Classification of Descriptors Topological Descriptors Topological descriptors are derived directly from the connection table representation of the structure which include: a) Atom and Bond Counts b) substructure counts c) molecular connectivity Indices (Weiner Index , Randic Index, Chi Index) d) Kappa Indices e) path descriptors f) distance-sum Connectivity g) Molecular Symmetry

8. Geometrical Descriptors Geometrical descriptors are derived from the three-dimensional representations and include: a) principal moments of inertia, b) molecular volume, c)solvent-accessible surface area, d) Charged partial Surface area e) Molecular Surface area

9. Electronic Descriptors Electronic descriptors characterize the molecular Strcutures with such quantities : • dipole moment, • Quadrupole moment, • polarizibility, • HOMO and LUMO energies, • Dielectric energy • Molar Refractivity

10. Hybrid and 3D Descriptors • geometric atom pairs and topological torsions • spatial autocorrelation vectors • WHIM indices • BCUTs • GETAWAY descriptors • Topomers • pharmacophore fingerprints • Eva Descriptors • Descriptors of Molecular Field

11. Limit Of Descriptors • The data set should contain at least 5 times as many compounds as descriptor in the QSAR. • The reason for this is that too few compounds relative to the number of descriptors will give a falsely high correlation: • 2 point exactly determine a line. • 3 points exactly determine a plane (etc.) • A data set of drug candidate that is similar in size meaningless correlation

12. Tools To calculate Molecular Descriptors Freely available • CDK tool http://rguha.net/code/java/cdkdesc.html • POWER MV http://nisla05.niss.org/PowerMV/?q=PowerMV/ • MOLD2 http://www.fda.gov/ScienceResearch/BioinformaticsTools/Mold2/default.htm • PADEL Descriptor http://www.downv.com/Windows/install-PaDEL- Descriptor-10439915.htm

13. Admet Descriptors to Screen Molecules

14. Bioavailability The Bioavailability of a compound is classified as : Bioavailability Absorbtion Liver Metabolism Permeability Gut-wall Metabolism Transporters Lipophilicity Solubility Flexibility Hydrogen Bonding Molecular Size/Shape

15. PREDICTION OF ADMET PROPERTIES • Requirements for a drug: • Must bind tightly to the biological target in vivo • Must pass through one or more physiological barriers (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

16. Lipinski Rule of Five(Oral Drug Properties) • 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)

17. Polar Surface Area • Defined as amount of molecular surface(vander-walls) arising from polar atoms(Nitrogen and oxygen atom together with attached hydrogens) • PSA seems to optimally encode those drug properties which play an important role in membrane penetration: molecular polarity, H - bonding features and also solubility. • It provide excellent correlations with transport properties of drugs.(PSA used in the Prediction of Oral absorbtion,Brain penetration, Intestinal Absorption, Caco-2- permeability) • It has also been effectively used to characterize drug likeness during virtual screening & combinatorial library design. • The calculation of PSA, however, is rather time-consuming because of the necessity to generate a reasonable 3Dmolecular geometry and the calculation of the surface itself. • Peter Ertl introduced an extremely rapid method to obtain PSA descriptor simply from the sum of contributions of polar fragments in a molecule without the necessity to generate its three - dimensional (3D) geometry.

18. PSA In Intestinal absorption • Intestinal absorption is usually expressed as fraction absorbed (FA), expressing the percentage of initial dose appearing in a portal vein. • A model for PSA was done for the β - adrenoreceptor antagonists[1].A excellent sigmoidal relationship between PSA and FA after oral administration was obtained. Similar sigmoidal relationships can also be obtained for the topological PSA (TPSA). • These results suggest that drugs with a PSA < 60 Å 2 are completely (more than 90%) absorbed, whereas drugs with a PSA > 40 Å are absorbed to less than 10%.This conclusion was later confirmed with the correct classification of a set endothelin receptor antagonists as having either low, intermediate or high permeability. • PSA was also shown to play an important role in explaining human in vivo jejunum permeability[2]. A Model based on PSA and LogP for the prediction of drug absorption was developed for 199 well absorbed and 35 poorly absorbed compounds[3].

19. PSA In Blood brain barrier penetration(BBB) • Drugs that act on the CNS need to be able to cross the BBB in order to reach their target, while minimal BBB penetration is required for other drugs to prevent CNS side effects. • A common measure of BBB penetration is the ratio of drug conc’s in the brain and the blood, which is expressed as log (C brain /Cblood ). • Van de Waterbeemd and Kansy were probably the first to correlate the PSA of a series of CNS drugs to their membrane transport. They obtained a fair correlation of brain uptake with single conformer PSA and molecular volume descriptors. • Clark etal. Derived a model of 55 compounds using TPSA and LogP LogBB= 0.516-0.115* TPSA n= 55 r2 =0.686 r= 0.828 σ = 0.42 TPSA in combiantion with ClogP LogBB= 0.070-0.014*TPSA+0.169*ClogP n=55 r2 =0.787 r=0.887 σ =0.35 • Great majority of orally administered CNS drugs have a PSA <70 Å2 . Non CNS compounds suggested that these have a PSA < 120Å2 . • Thus to conclude a majority of the Non CNS penetrating and orally absorbed compounds have PSA values between 70 and 120 A2. .

20. Partition coefficients P Xaqueous Xoctanol Partition coefficient P (usually expressed as log10P or logP) is defined as: [X]octanol P = [X]aqueous P is a measure of the relative affinity of a molecule for the lipid and aqueous phases in the absence of ionisation. 1-Octanol is the most frequently used lipid phase in pharmaceutical research. This is because: • It has a polar and non polar region (like a membrane phospholipid) • Po/w is fairly easy to measure • Po/w often correlates well with many biological properties • It can be predicted fairly accurately using computational models

21. Branch Phenylbutazone clogP for windows output C: 3.16 M: 3.16 PHENYLBUTAZONE Class | Type | Log(P) Contribution Description Value FRAGMENT | # 1 | 3,5-pyrazolidinedione -3.240 ISOLATING |CARBON| 5 Aliphatic isolating carbon(s) 0.975 ISOLATING |CARBON| 12 Aromatic isolating carbon(s) 1.560 EXFRAGMENT|BRANCH| 1 chain and 0 cluster branch(es) -0.130 EXFRAGMENT|HYDROG| 20 H(s) on isolating carbons 4.540 EXFRAGMENT|BONDS | 3 chain and 2 alicyclic (net) -0.540 RESULT | 2.11 |All fragments measured clogP 3.165 Calculation of logP LogP for a molecule can be calculated from a sum of fragmental or atom-based terms plus various corrections. logP = S fragments + S corrections

22. Binding to enzyme / receptor Aqueous solubility Binding to P450 metabolising enzymes Absorption through membrane Binding to blood / tissue proteins – less drug free to act Binding to hERG heart ion channel -cardiotoxicity risk What else does logP affect? logP So log P needs to be optimised

23. Admet Descriptors Calculation Tools • PreADMET http://preadmet.bmdrc.org/ • Molecular Descriptors Calculation - 1081 diverse molecular descriptors • Drug-Likeness Prediction - Lipinski rule, lead-like rule, Drug DB like rule • ADME Prediction  - caco-2, MDCK, BBB, HIA, plasima protein binding and skin permeability data • Toxicity Prediction - Ames test and rodent carcinogenicity assay • SPARC Online Calculator http://ibmlc2.chem.uga.edu/sparc/ SPARC on-line calculator for prediction of pK,, solubility, polarizability, and other properties; search in the database of experimental pKa values is also available • Daylight Chemical Information Systems www.daylight .com/ daycgi/clogp Calculation of log P by the CLOGP algorithm from BioByte; also access to the LOGPSTARdatabase of experimental log P data .

24. Admet Tools Continued.. • Molinspiration Cheminformatics www.molinspiration.com/seruices/index. Calculation of molecular properties relevant to drug design and QSAR, including log P, polar surface area, Rule of Five parameters, and drug-likeness index • Pirika - www.pirika.com Calculation of various types of molecular properties, including boiling point, vapor pressure, and solubility; web demo restricted to only aliphatic molecules • Actelion -www.actelion.com/page/property_explorer Calculation of molecular weight, logP, solubility, drug-score and toxlcity risk . • Virtual Computational Chemistry Laboratory www. vcclab. org Prediction of log P and water solubility based on associative neural networks as well as other parameters; comparison of various prediction methods

25. Virtual Screening

26. 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

27. 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

28. STRUCTURE-BASEDVIRTUAL 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) • Scoring of the ligand poses uch that the score reflects binding affinity of the ligand; • 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

29. 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

30. 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(eg moldock)

31. 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.

32. Ligand Based Virtual Screening The Ligand based approach mainly uses pharmacophore maps and (QSAR) to identify or modify a lead in the absence of a known three dimensional structure of the receptor. It is necessary to have experimental affinities and molecular properties of a set of active compounds, for which the chemical structures are known. a)PHARMACOPHORE:A pharmacophore is an explicit geometric hypothesis of the critical features of a ligand.Standard features include H-bond donors and acceptors, charged groups,and Hydrophobic patterns.The hypothesis can be used to screen databases for compounds and to refine existing leads. • For a geometric alignment of the functional groups of the leads, it is necessary to specify the conformations that individual compounds adopt in their bound state. • Since the simple presence of a pharmacophoric fingerprint is not sufficient for predicting activity, inactive compounds possessing the required pharmacophoric features must also be considered. • By comparing the volume of the active and the inactive compounds, a common volume can be constructed in order to approximate the shape of the (unknown) receptor site to further refine the pharmacophore model and to screen out additional compounds.

33. 3D compound Structures Feature Analysis Set of Conformers Align to template compare Pharmacophore Modelling Workflow Pharmacophore validation Application

34. Continued....... b)QSAR: The goal of QSAR studies is to predict the activity of new compounds based solely on their chemical structure. The underlying assumption is that the biological activity can be attributed to incremental contributions of the molecular fragments determining the biological activity. This assumption is called the linear free energy principle. Information about the strength of interactions is captured for each compound by,for example, steric,electronic,and hydrophobic descriptors.

35. Molecular similarity and searching Molecules 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

36. Molecular similarity How to calculate it? Quantitative assessment of similarity/dissimilarity of structures • need a numerically tractable form • molecular descriptors, fingerprints, structural keys Sequences/vectors of bits, or numeric values that can be compared by distance functions, similarity metrics . E= Euclidean distance T = Tanimoto index

37. Molecular descriptors a) chemical fingerprint hashed binary fingerprint • encodes topological properties of the chemical graph: connectivity, edge label (bond type), node label (atom type) • allows the comparison of two molecules with respect to their chemical structure Construction find all 0, 1, …, n step walks in the chemical graph generate a bit array for each walks with given number of bits set merge the bit arrays with logical OR operation

38. Molecular descriptors Example 1: chemical fingerprint Example CH3 – CH2 – OH walks from the first carbon atom merge bit arrays for the first carbon atom: 1111011110 This example illustrates how a 10 bits long topological chemical fingerprint is created for a simple chain structure. In this example all walks up to 3 steps are considered, and 2 bits are set for each pattern.

39. Molecular Similarity Example 1: chemical fingerprint 0100010100010100010000000001101010011010100000010100000000100000 0100010100010100010000000001101010011010100000000100000000100000

40. Molecular descriptors Example 2: pharmacophore fingerprint • encodes pharmacophore properties of molecules as frequency counts of pharmacophore point pairs at given topological distance • allows the comparison of two molecules with respect to their pharmacophore Construction map pharmacophore point type to atoms calculate length of shortest path between each pair of atoms assign a histogram to every pharmacophore point pairs and count the frequency of the pair with respect to its distance

41. Molecular descriptors Example 2: pharmacophore fingerprint Pharmacophore point type based coloring of atoms: acceptor, donor, hydrophobic, none.

42. Virtual screening using fingerprints Individual query structure 0101010100010100010100100000000000010010000010010100100100010000 query fingerprint query proximity 0000000100001101000000101010000000000110000010000100001000001000 0100010110010010010110011010011100111101000000110000000110001000 0100010100011101010000110000101000010011000010100000000100100000 0001101110011101111110100000100010000110110110000000100110100000 0100010100110100010000000010000000010010000000100100001000101000 0100011100011101000100001011101100110110010010001101001100001000 0101110100110101010111111000010000011111100010000100001000101000 0100010100111101010000100010000000010010000010100100001000101000 0001000100010100010100100000000000001010000010000100000100000000 0100010100010011000000000000000000010100000010000000000000000000 0100010100010100000000000000101000010010000000000100000000000000 0101010101111100111110100000000000011010100011100100001100101000 0100010100011000010000011000000000010001000000110000000001100000 0000000100000000010000100000000000001010100000000100000100100000 0100010100010100000000100000000000010000000000000100001000011000 0001000100001100010010100000010100101011100010000100001000101000 0100011100010100010000100001001110010010000010001100000000101000 0101010100010100010100100000000000010010000010010100100100010000 hits targets target fingerprints

43. Hypothesis Fingerprints

44. 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

45. Datamining and Machine Learning Approaches to Virtual Screening

46. Idea of Datamining • Is discovering for patterns in the data i.e for example a)an hunter looks pattern in animal migration behavior. b)farmers seek patterns in crop growth. c) politcians seek patterns in voters opinion d) Pattern in the compound structures . • The Patterns which are discovered must be meaningful and lead to some advantage. • The process must be automatic or semiautomatic.

47. Canonical learning Problems • Supervised Learning: given examples of inputs and corresponding desired outputs, predict outputs on future inputs. a) Classification b) Regression c) Time series prediction • Unsupervised Learning: given only inputs, automatically discover representations, features, structure, etc. a) Clustering b) Outlier detection c) Compression

48. Datamining Methods • Substructural Analysis The Substrcutural fragments makes a contribution to activity irrespective of the other fragments of the molecule. The idea is to derive a weight for each fragment which reflects to be active or inactive. The sum of weight gives the score of molecule which enables a new set of structures to be ranked in Decreasing probability of activity. The weight is calculated using the eq : Where act(i) is the number of active molecules that contain the i th fragment and inact(i) is the number of inactive molecules that contain the i th fragment

49. Discriminant algorithms • The aim of discriminant analysis is try to separate the molecules into constituent classes. • The simplest Linear discriminant which in case of two activity class and two descriptors which aim to find a st. line that separates data such that maximum number of compounds are classified. • If more than variable uses the line become hyperplane. • The idea is to express a class as a linear combination of attributes. X= w0+w1a1+w2a2+w3a3+......... X =class a1 a2 = attributes w1 w2 = weights

50. Neural Networks(NN) • The two most commonly used neural network architectures used in chemistry are the feed forward networks and the Kohonen networks. • The feed forward NN is a supervised learning method as it uses the values of dependent variables to derive the model. The Kohonen or Self Organizing map (SOM) is an unsupervised method. • The Feed forward NN contains layers of nodes with connection between all pairs of nodes in the adjacent layers. A key feature is presence of hidden nodes along with back propagation algorithm makes the network applicable to many fields. • The neural network must first be trained with set of inputs. Once it has been trained it can then be used to predict values for new and unseen molecules.