GPU-based Deep Learning Enhances Drug Discovery Says Startup

By John Russell

May 26, 2016

Sifting the avalanche of life sciences (LS) data for insight is an interesting and important challenge. Many approaches are used with varying success. Recently, improved hardware – primarily GPU-based – and better neural networking schemes are bringing deep learning to the fore. Two recent papers report the use of deep neural networks is superior to typical machine learning (support vector machine model) in sieving LS data for drug discovery and personalized medicine purposes.

The two papers, admittedly driven by a commercial interest (Insilico Medicine), are nevertheless more evidence of deep neural network (DNN) progress in LS research where large datasets with high dimensionality have long been difficult to handle. Using DNN to train models and produce answers is proving quite effective; in these two studies both straightforward and more complicated neural network techniques were used. Snapshot:

Part of what’s interesting here is the broad applicability of the DNN approach. As the authors (listed below) note there are many in silico approaches to drug discovery and disease classification, including efforts to use transcriptional response to predict functional properties of drugs. Neural networks’ natural knack for handling high dimensional data is an important capability in LS. Deep learning has already proven very valuable in a range of activities spanning simple image recognition to physics applications.

Broadly, neural networks try to emulate the way biological neural networks operate. Artificial neural networks are generally presented as systems of interconnected “neurons” which exchange messages between each other. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning. In essence they can be trained to understand and solve classes of problems.

For example, a neural network for handwriting recognition might be defined by a set of input neurons that are activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network’s designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, the output neuron that determines which character was read is activated.

The first study cited here relied on a standard multilayer perceptron (MLP), which is a feed forward artificial neural network model that maps sets of input data onto a set of appropriate outputs. In this instance, researchers worked with data from three cell lines (A549, MCF-7 and PC-3 cell lines from the LINCS project) that were treated with various compounds to elicit gene expression transcriptional profiles. Researchers began by classifying the compounds into therapeutic categories with DNN based solely on the transcriptional profiles. “After that we independently used both gene expression level data for “landmark genes” and pathway activation scores to train DNN classifier.” In total, the study analyzed 26,420 drug perturbation samples. Shown below is a representation of the DNN used in the drug study.

Study design: Gene expression data from LINCS Project was linked to 12 MeSH therapeutic use categories. DNN was trained separately on gene expression level data for “landmark genes” and pathway activation scores for significantly perturbed samples, forming an input layers of 977 and 271 neural nodes, respectively.
Study design: Gene expression data from LINCS Project was linked to 12 MeSH therapeutic use categories. DNN was trained separately on gene expression level data for “landmark genes” and pathway activation scores for significantly perturbed samples, forming an input layers of 977 and 271 neural nodes, respectively.

The details of the study are fascinating. Use of all the criteria was key to accuracy and the DNN effectiveness in coping with high dimensionality was a critical enabler.

In the second study, a more complicated ensemble approach proved most effective. Notably, this wasn’t a gene expression data analysis; rather it was based on blood-based markers. Data from roughly 60,000 blood samples from a single laboratory were analyzed. The five most predictive markers – albumin, glucose, alkaline phosphatase, urea, and erythrocytes – were identified. The best performing DNN achieved 81.5 percent accuracy, while the entire ensemble had 83.5 percent accuracy. The paper suggests the ensemble approach is likely most effective for integration of multimodal data and tracking of integrated biomarkers for aging.

DevBox_3qrtrOpen_wMonitorBoth studies required substantial compute power including the parallel processing capability of GPUs. NVIDIA assisted by providing early access to its DIGITS DevBox, which is a roughly 30Tflop deep learning machine featuring 4 Titan X GPU. “We also used a 2X Tesla K80 GPU system,” said Alex Zhavoronkov, an author on both papers and CEO of Insilico Medicine. “The original DNN in the molecular pharmaceutics [work] was trained on a Datalytics GPU cluster in New Mexico,” said Alex Zhavoronkov, CEO of Insilico Medicine and an author on both papers.

It bears repeating that Insilico Medicine was the main driver behind both papers and has a business interest in bolstering its credentials; that said, deep learning is a relatively small community where collaborations between academic, commercial, and technology suppliers are considerable. (For a snapshot of trends at the leading edge see HPCwire article, Beyond von Neumann, Neuromorphic Computing Steadily Advances.)

Insilico, founded in the 2014 timeframe, chose to focus on deep learning and signaling pathway activation analysis, which is an effective way to reduce dimensionality in gene expression data. “We are essentially a drug discovery engine now,” said Zhavoronkov, who has long been familiar with GPU technology having worked for several years at ATI Technologies. He’s also an ex-pat from Russia who has maintained close ties there; Insilico Medicine has grown to a staff of 39 including 22 in Moscow. Eleven are focused exclusively on deep learning.

Zhavoronkov divides the current deep learning community into three segments: one that is using off-the-shelf systems and tools; a second that is pushing the boundary and developing their own tools; and elite third components primarily focused on neural network R&D and developing new paradigms, citing Google DeepMind as one of the latter. “We fall into the middle category but also with domain expertise in drug discovery. There are few companies that have both.”

Perhaps predictably bullish, he said, “Both papers are first in class and demonstrate that deep learning can be very powerful in both drug discovery and biomarker development. In a short time we got over 800 strong hypotheses for both efficacy and toxicity of multiple drugs in many diseases.”

[i] Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data, Molecular Pharamaceutics, published by the American Chemical Society, http://pubs.acs.org/doi/abs/10.1021/acs.molpharmaceut.6b00248; the manuscript is now posted on the “Just Accepted” service of the ACS. Authors listed: Alexander Aliper, Sergey Plis, Artem Artemov, Alvaro Ulloa, Polina Mamoshina, Alex Zhavoronkov

[ii] Deep biomarkers of human aging: Application of deep neural networks to biomarker development, published in the May issue of Aging (Vol 8, No5), http://www.impactaging.com/papers/v8/n5/full/100968.html. Authors listed: Evgeny Putin, Polina Mamoshina, Alexander Aliper, Mikhail Korzinkin, Alexey Moskalev, Alexey Kolosov, Alexander Ostrovskiy, Charles Cantor, Jan Vijg, and Alex Zhavoronkov

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

STEM-Trekker Badisa Mosesane Attends CERN Summer Student Program

June 27, 2017

Badisa Mosesane, an undergraduate scholar who studies computer science at the University of Botswana in Gaborone, recently joined other students from developing nations around the world in Geneva, Switzerland to particip Read more…

By Elizabeth Leake, STEM-Trek

The EU Human Brain Project Reboots but Supercomputing Still Needed

June 26, 2017

The often contentious, EU-funded Human Brain Project whose initial aim was fixed firmly on full-brain simulation is now in the midst of a reboot targeting a more modest goal – development of informatics tools and data/ Read more…

By John Russell

DOE Launches Chicago Quantum Exchange

June 26, 2017

While many of us were preoccupied with ISC 2017 last week, the launch of the Chicago Quantum Exchange went largely unnoticed. So what is such a thing? It is a Department of Energy sponsored collaboration between the Univ Read more…

By John Russell

UMass Dartmouth Reports on HPC Day 2017 Activities

June 26, 2017

UMass Dartmouth's Center for Scientific Computing & Visualization Research (CSCVR) organized and hosted the third annual "HPC Day 2017" on May 25th. This annual event showcases on-going scientific research in Massach Read more…

By Gaurav Khanna

HPE Extreme Performance Solutions

Creating a Roadmap for HPC Innovation at ISC 2017

In an era where technological advancements are driving innovation to every sector, and powering major economic and scientific breakthroughs, high performance computing (HPC) is crucial to tackle the challenges of today and tomorrow. Read more…

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “pre-exascale” award), parsed out additional information ab Read more…

By Tiffany Trader

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid whoops and hollers from the crowd, Thomas Sterling presented t Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out plans to push deeper into climate science and develop more gran Read more…

By John Russell

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale companies and their embrace of AI and deep learning – tha Read more…

By Doug Black

DOE Launches Chicago Quantum Exchange

June 26, 2017

While many of us were preoccupied with ISC 2017 last week, the launch of the Chicago Quantum Exchange went largely unnoticed. So what is such a thing? It is a D Read more…

By John Russell

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid wh Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out pla Read more…

By John Russell

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale Read more…

By Doug Black

At ISC – Goh on Go: Humans Can’t Scale, the Data-Centric Learning Machine Can

June 22, 2017

I've seen the future this week at ISC, it’s on display in prototype or Powerpoint form, and it’s going to dumbfound you. The future is an AI neural network Read more…

By Doug Black

Cray Brings AI and HPC Together on Flagship Supers

June 20, 2017

Cray took one more step toward the convergence of big data and high performance computing (HPC) today when it announced that it’s adding a full suite of big d Read more…

By Alex Woodie

AMD Charges Back into the Datacenter and HPC Workflows with EPYC Processor

June 20, 2017

AMD is charging back into the enterprise datacenter and select HPC workflows with its new EPYC 7000 processor line, code-named Naples, announced today at a “g Read more…

By John Russell

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This