exploring the chemical space of screening results spring 2013 echampness.pdf[8] applying medicinal...

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Exploring the chemical space of screening results

Edmund Champness, Matthew Segall, Chris Leeding, James Chisholm, Iskander Yusof, Nick Foster, Hector Martinez

ACS Spring 2013, 7th April 2013

© 2013 Optibrium Ltd. 2

Overview

• Introduction

• Part 1: Chemical Space

• Part 2: Balancing Properties in Drug Discovery

• Part 3: Exploring the Space Around Us

• Conclusions

Part 1: Chemical Space

3

© 2013 Optibrium Ltd.

Chemical Space: What is it?

• Chemical space is the space spanned by all possible (i.e. energetically stable) molecules and chemical compounds [1]

• …estimated to exceed 1060 — an amount so vast when compared to the number of such molecules we have made, or indeed could ever hope to make, that it might as well be infinite… …how we should best direct our efforts towards regions of chemical space that are most likely to contain molecules with useful biological activity? [2]

[1] Wikipedia

[2] Kirkpatrick, P.; C. Ellis (2004). "Chemical space“, Nature - Vol 432 No 7019 (Insight) pp823-865

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Chemical Space: How do we think about it?

• Moon Landing

− 240,000 miles

• Solar system exploration

− 3.5 billion miles

• Galaxy mapping

− 30,000 light years

• Universe?

− Quite large!

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• Preclinical

− several compounds

• Lead optimisation

− Tens/hundreds of compounds

• Hit-finding

− Many thousands of compounds

• All theoretical molecules

− Too many!

“Outer” space “Chemical” space


Methodology: Chemical Space

• Chemical information

− Fingerprints

− Descriptors

• Similarity measure

− Tanimoto

− Euclidean

• Dimension reduction

− Expectation maximisation PCA [3]

− tSNE (t-distributed Stochastic Neighbour Embedding) [4]

[3] Sam Roweis (1998) Neural Information Processing Systems 10 (NIPS'97) pp.626-632

[4] van der Maaten, L., & Hinton, G. (2008). Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research , 9, 2579-2605.

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Similar compounds close together


Two Approaches to Visualisation

• PCA

− Well known

− Can be optimised for large sets

− Linear transformation results in information loss

− Biased towards keeping dissimilar compounds far apart rather than similar compounds close together

• tSNE (t-distributed Stochastic Neighbour Embedding)

− Computationally expensive

− Optimised for visual representation at expense of some intra-compound relationships

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How They Compare...NK2 Compounds

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How They Compare...Dopamine Actives

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Clustering?

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[5] Butina, D. (1999). Unsupervised Data Base Clustering Based on Daylight's fingerprint and Tanimoto Similarity: A fast and automated way to cluster small and large data set. J. Chem. Inf. Comput. Sci , 39, 747-750.

Clustering - Tanimoto level of 0.7 using path based fingerprints [5]


Comparing Chemical Families

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Part 1: 5HT1A Hit Space

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pKi = 7 pKi = 9.5

Looking promising...

Part 2: Balancing Properties in Drug Discovery

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The Objectives of Drug Discovery Multi-parameter optimisation

• Identify chemistries with an optimal balance of properties

• Quickly identify situations when such a balance is not possible

−Fail fast, fail cheap

−Only when confident

Absorption

Metabolic

stability

Potency Safety

Property 1

Pro

pe

rty 2

X

Solubility

Absorption

Solubility

Metabolic

stability

Potency

Safety

Pro

pe

rty 2

Property 1

Drug

X

Hit

No good drug


An Approach to MPO Probabilistic Scoring [6]

[6] Segall et al. (2009) Chem. & Biodiv. 6 p. 2144


Probabilistic Scoring

• Property data

− Experimental or predicted

• Criteria for success

− Relative importance

• Uncertainties in data

− Experimental or statistical

• Score (Likelihood of Success) • Confidence in score

Sco

re

Best Worst

Error bars show confidence in overall score

Data do not separate these as error bars overlap

Bottom 50% may be rejected with confidence



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pKi = 7 pKi = 9.5



Part 2: Potential Property Space

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Score = 0 Score = 0.4

Still promising?


Part 2: Property + Hit Space

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Still promising?

Part 3: Exploring the Surrounding Space Generating New Ideas...

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Generating Compound Ideas Applying Med. Chem. ‘Transformation Rules’

• Compounds generated must ‘make sense’ from a medicinal chemistry perspective

• Apply ‘transformation rules’, derived from medicinal chemistry experience, to initial compound [7][8]

− Library of >200 transformations

− Not only functional group replacement but also framework

transformations

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[7] “Drug Guru: A computer software program for drug design using medicinal chemistry rules” K.D. Stewart et. al. Bioorg. Med. Chem. 14 (2006) p. 7011 [8] Applying Medicinal Chemistry Transformations and Multiparameter Optimization to Guide the Search for High-Quality Leads and Candidates Matthew Segall, Ed Champness, Chris Leeding, Ryan Lilien, Ramgopal Mettu and Brian Stevens J. Chem. Inf. Model. (2011) 51(11) pp. 2967-2976

Functional group addition:

e.g. sulfonamide addition


Linker modification:

e.g. ester to amide






transformations

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Ring addition:

e.g. benzene to indole






transformations

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Iterative Application Exponential Growth!

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25

pKi = 7 pKi = 9.5



Part 2: Potential Property Space

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Still promising?


Part 3: Exploring the Space

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Interesting?


Part 3: Exploring the Space

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Interesting...


Conclusions

• We can use chemical space visualisation to help identify potentially interesting areas of chemistry

• Using an appropriate approach to MPO we can make confident decisions despite the uncertain nature of drug discovery data − We can explore a library to find chemistries with the best chance of

having a good balance of properties while avoiding missed opportunities due to uncertainty

• By automatically generating new chemistry ideas we can determine the potential of different areas of the chemistry space around which to expand upon our hit data

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Acknowledgements

• StarDrop group past and present, including:

− Matthew Segall

− Chris Leeding

− Iskander Yusof

− James Chisholm

− Nick Foster

− Hector Martinez

− Olga Obrezanova

− Alan Beresford

− Dawn Yates

− Dan Hawksley

− Joelle Gola

− Brett Saunders

− Simon Lister

− Mike Tarbit

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...and if you would like to try it yourself...

• StarDrop Hands-on Workshop

− Tuesday 9th, 12:00 – 2:30p.m.

− Hall B2-C, Exhibitor Workshop Room 1

− Lunch provided...

− Register at Optibrium booth #708

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