Prediction Of Bioactivity From Chemical Structure

Prediction of bioactivity from chemical structure Small Molecule Bioactivity Resources At The EBI Jérémy Besnard [email_address]

Myself
PhD student at the university of Dundee
Supervisor: Pr. Andrew Hopkins
Lab: medicinal informatics
Background
Chemistry degree with some biology
One industrial year at Pfizer on computational chemistry

Prediction of bioactivity
Type of predictions
How active is a compound?
Continuous model
Is the compound active, or not?
Categorical model
QSAR – Quantitative Structure-Activity Relationship Some slides are adapted from Richard Lewis (Novartis) presentation at the University of Sheffield Practical introduction to Chemoinformatics course (next in 2011)

Example
Molecular Weight = 360
Activity?
Linear regression:
Activity = 0.01 Molecular weight + 1.7 (R 2 = 0.900)
Activity = 5.3
Active?
Category:
Molecular weight > 260 = active
Active : Yes
Molecular Weight 180 220 250 290 340 380 450 500 Activity (pIC50) 4 4.3 4.8 5.4 4.8 5.8 7.5 7.7

QSAR Activity = IC50, Ki, Ratios… Molecular Descriptors Topological (shape, size) Physical & Thermodynamics Chemical feature (substructure) Activity = f (Molecular Descriptors) Statistics

The absolute basics
Activity + Representation + Method = QSAR
Activity = experimental data
Representation = description of the molecule
Method = Statistical tool to use
Underlying principle: similar molecules should have similar activities

Advantages of Models
Fast and cheap method
Virtual screening: the computer does the manipulation
Human: day – week
Computer : seconds - hours
Help understand the science behind the observation
Tool to design compounds with higher chance of being active

Activity
It can be anything
Continuous: IC50, %Inhibition, EC50, ratios,…
Categorical: Yes/No, Low/Medium/High
Better if
Data come from the same assay/condition
Good quality (you trust the experimental data)
For ADME endpoints
Lots of software solutions: not easy to predict!
Few experimental data points (and not very reliable)
In vivo phenomena

Molecular descriptors
Many Many Many
Simple counts
Number of atoms, rings, hydrogen bond donors, acceptors, molecular weight…
Physicochemical
Hydrophobicity, polarity: cLogP, Polar Surface Area (PSA)
Shape – Topological indices
Big, small, long, round
2D fingerprints
Presence or absence of certain substructures
From a dictionary (MACCS eg count of acids)
On the fly: look at the substructures present in the data
3D: fingerprints, electrostatics, shape
www.moleculardescriptors.eu

Fingerprint
Binary vector: list of 0 and 1
Dictionary: fixed size with each bit = one group (defined in advance)
Hashed: fragment the molecules and insert the fragment in a bit position of the vector
Acid Cl Amide 6 aromatic ring …

Focus on Fingerprint
Example of FCFP and ECFP
Internal to Accelrys software (Pipeline Pilot, will be used for the practicals).
Circular substructure
No dictionary
FCFP = Functional Circular fingerprint
Atom abstraction: function only (acid, aromatic…)
ECFP = Extended Circular fingerprint
Atom and atom properties (C aromatic, aliphatic)

FCFP: Initial Atom Codes

Extending the Initial Atom Codes
Fingerprint bits indicate presence and absence of certain structural features
Fingerprints do not depend on a predefined set of substructural features
O N A A A A O N A A A A A Each iteration adds bits that represent larger and larger structures Iteration 0 Iteration 1 Iteration 2

Generating the Fingerprint
Iteration is repeated desired number of times
Each iteration extends the diameter by two bonds
Codes from all iterations are collected
Duplicate bits may be removed

Data Sets

Validity of a model
It is easy to introduce artefacts and “false correlation”
The Trouble with QSAR (or How I Learned To Stop Worrying and Embrace Fallacy), Johnson, J. Chem. Inf. Model., 2008, 48 (1), pp 25–26

Training and Test Sets
Build the model from training set
Predict the test set
Also called Leave-N-Out validation where N=1 compound to 50% of the dataset.
Cross validation: repeat the steps using complementary training and test set N times.
http:// www.cs.cmu.edu/~awm/tutorials http://research.cs.tamu.edu/prism/lectures/iss/iss_l13.pdf

Space of the sets
The training set should cover the representation space evenly

Training vs Test Sets
The test set should be not too dissimilar to the training set
Too similar = over estimated the good quality
Too dissimilar = difficult prediction
Test Set Test Set Test Set

Questions?

Statistical Methods Activity Molecular Descriptors Training and test sets Activity = f (Molecular Descriptors) Statistics

Categorical
The focus is on a specific criterion:
Is the activity < 10uM? (like in HTS assay)
The data is not continuous
Soluble/Insoluble
Try to find a rule (or set of rules) to split the data in classes with the lowest rate of misclassification
Different coefficients to measure the quality
(ref: Assessing the accuracy of prediction algorithms for classification: an overview . Baldi et al. Bioinformatics 2000, 16:412-424)

Recursive Partitioning
Using decision trees
Rules are organized like a tree, each node = one rule
Cut-off : Molecular weight <450
Absence/presence of a group: Acid group
Usually easy to interpret
Drawback: overfitting and model to specific to the training data

Molecular Weight >450 ≤ 450 Polar surface area >100 0 , 10 ≤ 100 2 , 0 cLogP Acid Group 0 , 7 >4.2 ≤ 4.2 18 , 2 1 , 5 Yes No 21 Actives , 24 Inactives 2 , 10 19 , 14 19 , 7 MW: 178 PSA: 37 LogP: 3 MW: 205 PSA: 20 LogP: 3

Substructural Analysis
Idea: each fragment of the molecule makes a contribution to the activity , independent of the other fragments in the molecule.
Fragments get a score for their activity and a molecule has the score of the sum of the fragments.
A simple fragment scoring function:
Act i = Nb of active compounds containing fragment i Inact i = Nb of inactive compounds containing fragment i

Naïve Bayesian Classifiers
Related to the substructural analysis (slight differences in the weight sum calculation ref )
Use with fingerprints
Each substructure (bit in the fingerprint) gets a weight
Fingerprint can be mixed with other properties
Properties are binned and each bin obtains a weight
Molecules are scored, the higher the score the higher the chance to be in a specific category
Native implementation in Pipeline Pilot (practical)
Ref: New Methods for Ligand-Based Virtual Screening: Use of Data Fusion and Machine Learning to Enhance the Effectiveness of Similarity Searching , Hert et al., J. Chem. Inf. Model., 2006, 46 (2), pp 462–470

Validation
Collection of coefficients
Most common ones
Specificity and sensitivity – ROC curve
Enrichment plot

Specificity & Sensitivity
Specificity
Example:
If all compounds are predicted inactives:
Specificity = 1 (very good)
Sensitivity = 0 (very bad)
If all compounds are predicted actives:
Specificity = 0 (very bad)
Sensitivity =1 (very good)
TP=True Positive TN=True Negative FP=False Positive FN=False Negative
Sensitivity
http:// en.wikipedia.org/wiki/Sensitivity_and_specificity

ROC curve
Plot sensitivity versus 1-specificity
Coefficient = Area Under Curve 1 is ideal, 0.5 is random http:// www.medcalc.be/manual/roc.php

Enrichment curve
On some study the rank of compounds is not that important: idea is to select X percent of the data
Use the model to select the Top X compounds: try to have most of the active molecules inside
There 40% of the active in the top 10%. This plot doesn’t tell how many compounds this represents (could be 40 actives and 10,000 inactive in the top 10%)

Other methods
There are other statistical methods.
There is no perfect method and it is project dependent (also “personal” choice)
Most common:
Forest of trees
Support Vector Machine
Neural Networks

Questions?

Regression
Provide a value with more information than yes or no
Usually smaller set than classification
Link activity to the structure by an equation (simple to complicated)

Historical
First equation: Hansch in 1964
Link activity to molecule’s electronic characteristics and to its hydrophobicity
C is the concentration required to produce a response
LogP the octanol/water partition coefficient (possibility to cross membrane)
σ the Hammett substitution parameter ( strength of the electron-withdrawing or -donating properties of the aromatic substituent )
It is a linear equation
Then improved with a parabolic function
p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure , Hansch et al., J. Am. Chem. Soc., 1964, 86 (8), pp 1616–1626 Parabolic dependence of drug action upon lipophilic character as revealed by a study of hypnotics , Hansch et al., J. Med. Chem., 1968, 11 (1), pp 1–11

Deriving a QSAR equation
Most common method is the linear regression
In QSAR x is usually a descriptor (eg logP)
Aim: reduce the sum of the differences between the predicted and the real values
With more than one descriptors:

Quality
Most common way is to use the square of the correlation coefficient, R 2
Need to review the data:
Almost the same R 2

Cross validation
Involves the removal of some of the values from the dataset, build a QSAR from the remaining data, and apply this model on the previous removed data.
The R 2 of cross validation is written Q 2 , it represents the goodness of the prediction (R 2 goodness of fit).
Q 2 should be lower than R 2 but not too much (otherwise the model was over-fit).

Designing QSAR experiment
Find the smallest number of variables to explain as much data as possible
It is easy to calculate thousands of parameters with a computer in seconds.
Rule of thumb
>5 compounds for each descriptor
Check the descriptor: remove the invariant ones
Remove correlated factors (by deleting a descriptor, or using data reduction technique – PCA)
Selection
Algorithms to select most significant descriptors
Forward stepping regression: start from 1 and add
Backward-stepping regression: start with all and remove

Regression algorithms
Multiple linear regression (see practical)
Easy to interpret
Problem of correlations between factors
Partial Least Squares (PLS)
Similar to PCA by reducing the number of factors (x i ) in new orthogonal “latent variables” (t i )
Compare to PCA, add a correlation between observed data and the latent variables (y~a 1 t 1 )

Not limited
Regressions algorithms are multiple
Implementation
Selection of factors
Best way to consider a good model
Other methods
Gaussian Processes ( http://dx.doi.org/10.1021/ci7000633 )
Molecular Field Analysis and Partial Least Square: CoMFA and derivative, using 3D steric and electrostatic information ( http://www.wiley.com/legacy/wileychi/ecc/samples/sample05.pdf and http://www.netsci.org/Science/Compchem/feature11.html )

Regression + Category
Poor regression but good classification

After
Once models are built and have ideas of the mathematical quality:
Look at the observed vs predicted plot
Try to understand the model
Do the descriptors make sense?
LogP important when modelling solubility
Why is a certain substructure so important?

Outliers
What to do with outliers?
Prediction far from observed:
Are the compounds similar to the training set?
Outside your space of confidence
Chemical similarity = Activity similarity not always true
There are activity cliffs ref
Interesting for SAR study
On Outliers and Activity Cliffs−Why QSAR Often Disappoints , Maggiora, J. Chem. Inf. Model.2006, 46, 1535−1535 Structure−Activity Relationship Anatomy by Network-like Similarity Graphs and Local Structure−Activity Relationship Indices , Wawer et al., J. Med. Chem., 2008, 51 (19), pp 6075–6084

A model is a model
It is not the reality
Provides help for experimentations
Understand what happens
Reduce the number of experiments
Do not replace lab work
There is no one perfect model
Depending on the method, data sets, descriptors, tuning parameters…

Real correlation?
The decrease of marriage decreases the risk of death? Should we ban Church of England Weddings?
Why do we Sometimes get Nonsense-Correlations between Time-Series?--A Study in Sampling and the Nature of Time-Series , Yule, Journal of the Royal Statistical Society , Vol. 89, No. 1. (Jan., 1926), pp. 1-63

Further – Multiple Targets
Large scale model:
Prediction of multiple interactions at once
Need large database
Wombat (literature), MDDR (patent)
ChemBl
Identify side effect, or unknown beneficial effect

Principle
SEA approach:
Similarity of a compound to active ligands (similar to Blast)
website: http:// sea.bkslab.org /
Multiple category Bayesian model:
Each fingerprint gets a different weight for each target: the sum is different by target
Output:
List of protein ranked by probability of binding

References
An introduction to Chemoinformatics , A. Leach and V.Gillet
Sheffield course: next one in 2011: http://www.shef.ac.uk/is/research/groups/chem/courses.html , Conference: http://cisrg.shef.ac.uk/shef2010/
Pipeline Pilot documentation and Cheminformatics analysis and learning in a data pipelining environment , Hassan et al., Molecular Diversity (2006) 10: 283–299,
Multiple targets:
Predicting new molecular targets for known drugs , Keiser et al., Nature 462, 175-181 (12 November 2009) and Relating protein pharmacology by ligand chemistry , Keiser et al., Nat Biotech 25 (2), 197-206 (2007)
Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases , Nidhi et al., J. Chem. Inf. Model., 2006, 46 (3), pp 1124–1133
Global mapping of pharmacological space , Paolini et al., Nat Biotech 25 (7), 805-815 (2006)

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