Accelerating Drug Discovery using HPC

HPC at NIBRNick Holway, NIBR Scientific Computing GroupSpeedup 2016September 15, 2016Twitter: @nickholwayLinkedIn: https://ch.linkedin.com/in/nickholway

Novartis Institutes for Biomedical Research(NIBR)

Novartis Institutes for Biomedical Research

Today’s talk

1. HPC at NIBR – a quick introduction

2. HPC in the cloud

3. Accelerating a “compound search engine” using HPC

4. Expediting drug discovery with GPUs

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HPC at NIBR - Hardware

• x86 servers– Intel Xeon– 128-768GB RAM– FDR Infiniband– 10GigE

• Nvidia GPUs on some servers• Isilon storage

– CIFS/NFS– 10GigE to Arista switches

• Lustre– Scratch

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HPC at NIBR - Software

• RHEL 6.x

• Univa Grid Engine for scheduling

• Software compilation & configuration– Easybuild– Modules– GCC, Intel, Nvidia compilers

• Languages: C++, Fortran, CUDA, Python, R, Matlab

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HPC at NIBR - Humans

• Global team (Europe, USA, Asia)

• Complementary backgrounds and skills– Sysadmins– Mathematicians– Scientists

• HPCWire award winners in 2014

• NB: HPC exists elsewhere in the Company for Clinical Trial analysis, CFD etc.

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HPC in the cloud


HPC in the cloud

• NIBR have used Amazon EC2 for compute workloads– Cycle computing

• ISVs eg DNANexus– Bioinformatics NGS

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Docking at scale in the cloud

• Ligand-protein docking is “to predict the position and orientation of a ligand (a small molecule) when it is bound to a protein receptor or enzyme” (Wikipedia)

• Embarrassingly parallel - compute-heavy / data-light

• We used the cloud to screen 10 million molecules against a cancer target

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How we did it

• Cycle computing’s software (Cycle server, Cyclecloud)

• Over 10,000 EC2 spot instances– Extensive benchmarking to select instance type

• Licence files (licence servers cannot cope with the load)

• Proprietary compounds run in NIBR’s VPC, others in “public”

• See http://opensource.nibr.com/videos/aws-litster/ and http://cyclecomputing.com/novartis-taps-cloud-hpc-for-faster-drug-discovery-better-science/

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Where we’re going in the cloud

• “Cloud by default” for many non-HPC applications

• Clinical data (subject to “informed consent”)

• HPC where appropriate– IB etc for tightly-coupled parallel jobs usually unavailable– Data locality challenging

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Accelerating a compound target search engineSlides courtesy Douglas Selenger


Introduction

• There are many disparate public and private sources of information which is hard for experts to query and almost impossible for “normal” scientists

• Scientists would like to ask questions like “What is the target and Mechanism of Action (MoA) of my compound?”

• MOA Central is a web-based tool

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”Flow” of a query

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Diagrammatic network

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Example output

Glivec (Imatinib)

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Impact of HPC

• Our scientists developed a tool, MOA Central, using graph analysis techniques using well known workflow software

• MOA Central worked so well that the server couldn’t keep up with demand

• We helped port it to Python (Pandas, SciKit etc)– Large queries and data preparation can now run on the cluster– Version control!

• Moving from CSV files, database queries & web services to HDF5 will improve scalability

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Want to know more about MOA Central?• Look for more information at

https://www.researchgate.net/profile/Douglas_Selinger

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Accelerating Motor Neuron Disease drug discovery with GPUsSlides courtesy Imtiaz Hossain


In-vitro model for neuromuscular junctions• Faulty junctions between motor neurons and muscle

cells are implicated in MND

• We’d like to create a drug which corrects this

• Motor neurons & myotube (muscle fibre) cells were “co-cultured” in a “plate” to which drug candidates are added

• Cells were imaged in real time to measure their contractility

• This is very hard to see by eye and also hard to segment using computers

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What do the cells look like?

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Motion estimated with Optic Flow

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Different contracting regions

Total area under contraction


Impact of HPC

• A good joint project between bench scientists, lab automation experts & informaticians

• 80x increase of throughput compared to CPU

• NIBR scientists have access to new method of monitoring myotube contractility

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The future

• GPUs– Deep learning– Cryo-EM

• Real time collection & processing of data from clinical trials

• Integration of “big data” technologies such as Apache Spark into HPC

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Thank you


Backup: MOA Central predicting a side effect

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Accelerating Drug Discovery using HPC

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