bipredict: moe/web server enabled delivery of in silico properties and models

BI di MOE/ b S E bl d D li BIpredict: MOE/web Server Enabled Delivery of In Silico Properties and Models

David C. Thompson, Ph.D

125 Years of Innovating for Patients and Their Families

Founded 1885 in Ingelheim, Germany

Family-owned global company

Privately-heldfor 125 years

$17.7 billion 2009 net sales

Products marketed in

150+ countries41,500

employees worldwide

g p y y

2009 net sales150+ countries

142 ffili t

employees worldwide

Focus on 142 affiliates

in

50 countries

Human Pharmaceuticals& Animal Health

2

One Pill Makes You Larger [*]

My favourite papers from each period:[1] J. Chem. Phys. 122, 124107 (2005)[2] J. Chem. Phys. 128, 224103 (2008)[3] J. Chem. Inf. Model. 49, 1889 (2009)[4] J. Chem. Inf. Model. 51, 93 (2011)[*] This slide title brought to you courtesy of Lewis Carroll

The Magic of Clip Art

What are we trying to do in the pharmaceutical industry?

[*]

+ =[*]

What are we trying to do as computational scientists working in the pharmaceutical industry?

+ =

We build models to try and expedite the drug

Chemical space

4

discovery process

[*] Side effects may include changing hair colour

Shameless slide reuse … [5]

“All models are wrong, but some models are useful”– G. E. P. Box

“…the validity of any given model is of limited scope, as is the case with any mental construct

that we have about what our molecules are doing, whether we used a software package or waved our whether we used a software package or waved our

hands around in the air.” – D. Lowe

Simulation and its discontents, Sherry Turkle, Cambridge, MA: MIT Press (2009)

[5] D. C. Thompson et al. Schrödinger Regional User Meeting, New York, NY 2009

Taxonomy of Risk [6]

Risk Uncertainty

Randomness amenable to formal Randomness not amenable to formalRandomness amenable to formalstatistical analysis

Randomness not amenable to formalstatistical analysis

“ … a given phenomenon may contain several levels of uncertainty at once, with some components being completelycertain and others irreducibly uncertain”

“In fact, we propose that the failure of quantitative models [in economics and finance] is almost always attributable to a mismatch between the level of uncertainty and the methods used to model it.”to a mismatch between the level of uncertainty and the methods used to model it.

6[6] “WARNING: Physics Envy May be Hazardous To Your Wealth!”, A. W. Lo, and M. T. Mueller arXiv:1003.2688v3 [q-fin.RM]

Okay, now what?

• Focus on physicochemical properties [7, 8]

• Enable scientists through light-weight clientsEnable scientists through light weight clients

• Provide core scientific functionality through web services architecture

=+

Chemical space

We build models to try and expedite the drug discovery process

7[7] Nature Rev. Drug. Discov. 10, 197 (2011)[8] Med. Chem. Comm., 2011 (DOI: 10.1039/c1md00017a)

The Internet …

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… is here to stay [*]

9* Probably

If it’s going to stay, we might as well use it

A Web Service is a method of communication between two electronic devices over a network[9]

Example*:

Web Service

10* Probably[9] http://en.wikipedia.org/wiki/Web_service

BIpredict: An in silico molecular property prediction framework

Initial requirement: Build a real-time physchem. property calculator engine that could be used to address project concerns and issues at the medicinal chemistry desktop

Buy or Build?

Offer multiple interfaces to complement users preferred workflow

Proposal: Rapid development of an in-house solution to allow us to focus on optimizing the interaction between a web services layer and other BI systems and, most importantly, the y y , p y,scientists

• Leverage Molecular Operating Environment (MOE), and newly developed MOE/web SOAP application server technologypp gy

— Java-based web server— No Apache setup

M OEM OE

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• Focus on flexibility, ease of deployment, and extensibilitySOA P

M OEPipeline

Pilot®

ba tc h

M OE

BIpredict architecture:How you consume, depends on what you see

MOE BIDATA Pipelining tools Web Apps.BIModel Command Line(G) (G)

Multiple front-ends(d t )

(G + A) (G)(G + A) (G +A)

BIpredict

(data consumers)

Single back-end(data producer)

(web services layer)

General (G): Advanced (A):

Synchronous

( )Intended for

general consumption

( )Abstract

descriptors for comp. chem.

Interface determines which descriptors are exposed

Development:Oct. 2009 – Jan. 2010Production:Jan. 2010

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“One of the things about a real-time system is everything has to be timed out” [10]

BIpredict(web services layer)

single back-end(data producer)

y

Asynchronous / Batched

Descriptor l Scatterclasses

User IDJob IDGeneral:

Y

42 descriptors8 engines

Advanced:

Development:Jan 2010 June 2010

Gather

Collect outputN?Y

Descriptor names

Advanced:3431 descriptors15 engines

Jan. 2010 – June 2010Production:June 2010

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Packaged output

Job ID

[10] Bernie Cosell, “Czar of the PDP-1 timesharing system”

What does this magic look like?

With BIpredict panel open, workspace is ‘live’

Physicochemical properties are updated

l l i b ilas molecule is built

Atomistic descriptor values are appended values are appended directly to the molecule

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Accessing Models in BIpredict

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Reimplementation of Pfizer CNS Multi-Parameter Optimization design tool [11,12]

• Driven by the scientists, turnaround of days

G h 9 li d i ll i d i• Gather 119 literature compounds, visually inspect and triage

• Identify physicochemical property Descriptorsy p y p p y p• Sybyl clogP• ACD logD (@ pH 7.4)• MOE MW

6

7

• MOE MW• CADD TPSA• MOE Lipinski HB donors

ACD M B i K

R² = 0.9834

3

4

5-i

mpl

emen

tatio

n

• ACD Most Basic pKa

• Enable model through BIpredict1

2

Re-

g p0

0 1 2 3 4 5 6 7

Literature

16[11] ACS Chem. Neurosci., 1, 435 (2010)[12] Bioorg. Med. Chem. Lett, 18, 4872 (2008)

Begin at the beginning and go on till you come to the end: then stop [*]

• All models are wrong• Expose those models that we think will expedite the p p

drug discovery process• Focus on extensible, light-weight, service delivery, g g , y

[*] This slide title brought to you courtesy of Lewis Carroll

Cultural Highlight

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Acknowledgements

Dr. Jörg BentzienDr. Alex ClarkC th F llCathy FarrellDr. Sandy FarmerAmy GaoDr Scott OloffDr. Scott OloffDr. Miguel Teodoro