Next-Generation Integrated Simulation of Living Matter
Strategic Programs for Innovative Research Field 1 Supercomputational Life Science

2012.12Page 6, Fig. 1 : Gk(r) defined by Equation [2]. The bold black line
shows the values obtained by the PME method, the thin red line
shows the values obtained by the ZD method where dc is 14 Å (α= 0), the dotted blue line shows the values obtained by the ZD
method where dc is 12 Å (α= 0). See Reference 7) for parameterα.
Figure 5 in Reference 7) was modified.

CONTENTS●SPECIAL INTERVIEW●Report on Research

Page 6, Fig. 2 : (a) Changes over time of cumulative mean dielectric permittivity
obtained by Molecular Dynamics (MD) simulation. (b) Changes over time of
the ratio of the second term to the first term in the curly brackets of Equation [1]expressed as a percentage on a logarithmic scale. The individual lines indicate
the same items as those in Fig. 1.

◦ Interview with “K computer” Developer regarding Efforts in Exascale and Coming Supercomputer Strategies
Executive Architect, Technical Computing Solutions Unit, Fujitsu Limited Motoi Okuda◦ Large-scale Virtual Library Optimized for Practical Use and Further Expansion into K computer
Professor, Department of Chemical System Engineering, School of Engineering, The University of Tokyo Kimito Funatsu◦ Old and new subjects considered through calculations of the dielectric permittivity of water
Institute for Protein Research, Osaka University Haruki Nakamura（Molecular Scale WG）◦ Development of Fluid-structure Interaction Analysis Program for Large-scale Parallel Computation
Advanced Center for Computing and Communication, RIKEN Kazuyasu Sugiyama（Organ and Body Scale WG）◦ SiGN : Large-Scale Gene Network Estimation Software with a Supercomputer

Graduate School of Information Science and Technology, The University of Tokyo Yoshinori Tamada(Data（Data Analysis Fusion WG)●SPECIAL INTERVIEW

7

Vol.

◦ ISLiM research and development source codes to open to the public
Computational Science Research Program, RIKEN Eietsu Tamura◦ Understanding Biomolecular Dynamics under Cellular-Environments by Large-Scale Simulation
using the “K computer”
Chief Scientist, Theoretical Molecular Science Laboratory, RIKEN Advanced Science Institute Yuji Sugita

2-3
4-5
6
7
8
9
10-11

（Theme1 GL)◦ Innovative molecular dynamics drug design by taking advantage of
excellent Japanese computer technology

Professor, Research Center for Advanced Science and Technology, The University of Tokyo Hideaki Fujitani（Theme2 GL)●Report●Promotion●Event information◦ Lecture on Computational Life Sciences for New undergraduate Students
HPCI Program for Computational Life Sciences, RIKEN Chisa Kamada◦ Research System for Computational Science

12-13
14
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PECIAL INTERVIEW

Searching out Trends in the Supercomputer Environment andTechnology Development Following the “K computer”

Interview with“K computer”Developer
regarding Efforts in Exascale andComing Supercomputer Strategies
Executive Architect, Technical Computing Solutions Unit, Fujitsu Limited

Motoi Okuda● Will the “K computer” class supercomputer be available
onsite around the year 2015!?

━ The era of the “K computer” has begun, but it is restricted toproposal-based usage and is not yet available to everyone. This isinevitable because there is no other supercomputer that has thecalculation performance of 10 PFLOPS other than the “K computer” inJapan. However, I believe many researchers dream of having an advanced
supercomputer environment available on demand. “FX10”, which is acommercial model of the “K computer” has been installed inside andoutside Japan, but I have not heard of 10 peta class installations. With
the current situation in mind, we’d like to hear how you feel about
advancements in technology. When do you believe researchers will beable to freely use 10 peta class supercomputers? How will supercomputersthat exceed the “K computer” be developed, and what are the challenges?
Okuda Looking at the history of the world’s supercomputing performancecompetition (first place in the TOP500), the performance ofsupercomputers has risen approximately 1,000 times over 10 years.
At this rate, it is predicted that an 1EFLOPS (exaflops) machine will
be developed around the year 2018 to 2020. This is a target also
held by Japan. On the other hand, in order for supercomputers to
be readily available to researchers, supercomputers need to beinstalled all over the world and their performance may be 1/10 ofthat of the top supercomputer. As an actual example, at the sametime the “K computer” was completed, a new supercomputer
system (Oakleaf-FX) that has 1.13PFLOPS performance, which isapproximately 1/10 the peak performance of the “K computer”,
was installed in the Information Technology Center of the
University of Tokyo. Since performance values are improved 10
times in about 3.3 years, it is predicted that a supercomputer
having the performance values of today’s leading TOP500 values
will be deployed throughout the world in various institutionsin approximately 3.3 years. Therefore, you may foresee thatsupercomputers with a calculation speed equivalent to the “Kcomputer” will be available to universities and research institutions
around the year 2015.
━ If 10 peta class supercomputers become available in the year 2015,
does that conversely mean a supercomputer of 100 peta class would be
developed by then?
Okuda All vendors have different views and approaches. We at Fujitsu
are planning to follow Japan’s national project for future productdevelopment by using our experience in the development of
the “K computer”. We also will provide operation assistance andsupport along with application optimization support for the “Kcomputer” which will soon be operating officially. Another mission
is to continue to maintain an environment that allows further
advancement of software assets, while applications for the “Kcomputer” are developed, tuned, and optimized by researchers.
Like the provision of the commercial machine “FX10”, we aredeveloping a model to be commercialized around the year 2014
to 2015 called the “100PFLOPS Level Trans-Exa System” where“Trans-Exa” means a bridge to exascale computing. Applications
developed for the “K computer” and “FX10” will run on thismachine as is. The architecture will be of the same concept, andthe same programming model can be applied. Of course, higher
performance will be produced if the program is recompiled. A CPU
in “FX10” performs 1.85 times better than the “K computer” (peakperformance), and has other functions that improve operability.
In “Trans-Exa”, CPU performance, network performance, and also
packaging density and power consumption levels are planned tobe highly improved. Although the machine will have the ability
to provide up to 100 peta, we assume the number of full 100
peta scale installation would be limited. Nevertheless, onsite
supercomputer environments of the several peta class should beready for universities and research institutions around the year
2015.

2 BioSupercomputing Newsletter Vol.7● The road to realizing exascale━ W hat are yo ur v iews o n co ming tre n ds in sup e rco mp ute r
development?
Okuda There has been an extremely large technology spurt in this year’s
TOP500. This is marked by the power efficiency (performance per
power) of the first place machine, “Sequoia”, of the United States.
Until this year, the “K computer” held the leading performance outof the TOP1 machines, but “Sequoia” surpassed its records with2,000GFLOPS/KW or higher with a large margin. To state it briefly,
a highly power efficient supercomputer that has never been seen
before, was born. However, the performance of a core in a TOP1
machine (LINPACK performance) hasn’t changed very much overthe last five years (a core is a calculation unit located in the CPU).
The “K computer” has a relatively high value of approximately
15GFLOPS, but Sequoia’s values is lower and is approximately
10GFLOPS. Currently, the trend is to improve per formanceby increasing the number of cores rather than improving theperformance of the core itself.
━ So I understand that lowering power consumption and increasing
the number of cores are two trends in technology. In other words, could
you say that they also are the challenges faced in the coming exascale
development?
Okuda I agree. We are planning to realize exascale, through “TransExa”, which I just explained, as the first step of technologicaldevelopment, and the second step will consist of research anddevelopment. The f irst step, “ Trans-Exa” which is currently
being developed, raises the performance of one core and also
implements many-core processing. The “K computer” consistsof 8 cores and “FX10” consists of 16 cores, and the next machineis planned to have even more cores. Along with improvements
in CPU performance, we also plan to improve interconnection
performance. We also are planning to lower power consumption,
but this is our biggest challenge. Nevertheless, the development ismoving forward with the goal of surpassing “Sequoia”. In order toattain a higher performance and density, we also plan to improvepackaging density, which is the number of CPUs per dimension.
We aim for exascale with these improvements which we define
as the first step. During this period, we believe that there will bea big technology spurt. As predicted earlier, exascale is assumed
to be achieved around the year 2018 to 2020, which is three tofive years after the first step. To be honest, it is hard to predictchanges in technology five years ahead. We can speculate trends
for improvements in CPU calculation performance, but lowering
power consumption is unclear. Technologies required for a
100PFLOPS class machine which is under development have mostly
been revealed, but improvements on the current technology
itself will not be sufficient to bring out 10 times the performance,
and therefore we are awaiting the birth of new technologies. In
addition, research and development for improving reliability will
also be necessary for realizing exascale.
Parallel processing abilities will need to be improved in exasystems
for application development regardless of the form it takes. It
may be necessary to change the programming model. We still
have not untwined the entirety of exascale. Therefore, we believe
the 100PFLOPS scale system will be a platform to prepare us toadvance to the future exascale. Along with progress in multi-coretechnology, and research and development in discovering usageof machines with SIMD capability, we hope to advance towards the
future.

● To develop a supercomputer that leads the world━ So that means the machine which will become “Trans-Exa”, will be
regarded as a test model for future exascale machines for engineers andresearchers.
Okuda In the case of the “K computer”, application development projects

SPECIAL INTERVIEWWhen will exaflops be realized?
▪ If performance values continue to rise, a performance of 1 exaflops is estimated to beattained around 2018 to 2020.
▪ In various institutions across the world, machines of expected performance (=1/10 of
the Top1 performance) have been installed 3.5 years later. (For example, while the K computeris 10PFlops, a machine of 1PFlops class performance is installed at a university computing center).
→ A machine of the same performance as the K computer may be installed in other centers in Japanaround the year 2015?

Challenges and efforts for realizing exascale▪Attaining an exascale system as a continuation from current technology isdifficult, and a two-step development approach is necessary.
▪The largest challenge is lowering power consumption and implementing
high density.

▪First step: 100 petaflops scale system currently under development
Technological ▪ Enhancements in CPU multi core and SIMD extension
development ▪ Enhanced interconnect performance and lower power

After approximately 3.5 years, anumber of machines of 10PFlops
class may be installed?

consumption▪ Improved performance and density in memory technology

Performance▪Second step: Realization of exascale system
Research anddevelopment▪ Promoting research and development for enhanced
performance and lower power consumption▪ Promoting research and development for
Exascale system
improving reliability
100 petaflops classsystemAround 2018 to 2020,
EFlops will be realized?

History of TOP1
Estimated value for TOP1
1/10 value of TOP1

Year

Challenges and efforts for realizing exascale
When will exaflops be realized?
Up to now, supercomputer performance has grown 1,000 times in 10 years, and if
performance values continue to rise, a performance of 1EFLOPS is estimated to be attained
around 2018 to 2020. Furthermore, supercomputers which are 1/10 the performanceof the TOP1 machine in several locations in the world are likely to be upgraded to theperformance of the TOP1 machine of that time in approximately 3.3 years.

were initiated along with the commencement of the project. The
HPCI Strategic Project started in the year 2011. What should bedone using exascale should be revealed once preparation for the
next step kicks off on a 100PFLOPS scale machine in the latter halfof the HPCI project.
━ Yes, we’re no longer in the times when calculation speed would beenhanced solely by improvements in hardware.
Okuda As the phrase “Co-Design” states, this is the age for engineers andresearchers to work together to design and develop machines andapplications. Like the grand challenge application developments
for the “K computer”, a preparation period is necessary, or else,
we will be missing applications to make use of the machine s
p e r f o r m a n ce w h e n t h e e x a s c a l e m a c h i n e i s co m p l e te d .
Preparation started four to five years before in various fields in thecase of the “K computer”, and we have now reached a stage where
we can start seeing the fruit of their achievements. If positive
results are available in the years 2013 and 2014, they will encourage
progress for future research and development, and conversely,
the development of another machine for moving onto the next
step along with the “K computer” will be very important. We aredeveloping a 100PFLOPS scale machine with this vision in mind.

unknown, through various technological developments.
━ As a developer, you cannot keep yourself from trying to aim higher.
Okuda Yes, I believe we must continue to do so. We’ve heard fromresearchers that they cannot go back to their old environment once
they started using the “K computer”. When the development firststarted, there were questions such as “Is 10 peta really necessary?”and “Are there applications that can be used? ” After five years, I
believe the situation has totally changed. Furthermore, Japanese
researchers need some “strength” to fall back on to discuss equallywith workers from across the world. People all over the world
are interested in the “K computer” and are awaiting researchachievements through the “K computer”. Therefore, we must
continue to develop a machine that can be praised on a global
level.

━ “FX1” was released just before the “K computer”, and since theirarchitecture was similar, several research institutions and universities
installed the machine early on. From your explanation, you want “TransExa” to be used as a preparation for exascale in a proper manner, correct?

Okuda Yes, even if a machine with high performance is available, you will
not be able to produce performance values without that level of
preparation.
━ What is the largest challenge in the second step in realizing
exascale?
Okuda I feel that lowering power consumption will be the largest concern.
Semiconductor technologies have advanced and it is possible toincrease the number of arithmetic circuits in a chip to improve
CPU performance. However, if the arithmetic circuit in this highperformance CPU is used for all calculations, an outrageous amountof power will be consumed, and the machine cannot be operated
efficiently. Therefore, the challenge is to lower power consumption.
It is difficult to develop a circuit that can calculate without usingmuch power, but we must overcome these obstacles. Our desire
and mission is to attain exascale, regardless of the unclear and“PRIMEHPC FX10”
developed by Fujitsu
as a commercial machine

BioSupercomputing Newsletter Vol.7 3

S

PECIAL INTERVIEW

Biosupercomputing Pioneers the Future of Life Science

Large-scale Virtual Library Optimized
for Practical Use and Further Expansion
into K computer
Professor, Department of Chemical System Engineering,
School of Engineering, The University of Tokyo

Kimito Funatsu● Present status of compound library, a key for drug discovery
New drug development is very time-consuming, taking a dozen years or
so. It is also said that only one out of several ten thousand compounds
is sent to the medical work front. Therefore, it is a reality that both thedevelopment cost and R&D risk are extremely high. Such a new drug
development starts from identification of a drug target, which is followedby discovery of a lead compound and its optimization for good activity.
Then, the compound is subjected to clinical trial. The key point for successis the on-target capture of a group of lead compounds in the early stages
which starts with screening of a compound library for a lead compound.
This is why the potential development capability of a pharmaceuticalcompany is dictated by the chemical variety, quality and scale of thecompound library it owns.
Then what about the current status of compound libraries? It is estimatedthat the theoretical total of compounds which may become drug discovery
targets is 10 to the sixth power. Meanwhile, existing compound libraries
owned by pharmaceutical companies (megapharma) cover only several

million compounds. This results in unsuccessful screening without a hit,or discovery of only low-active compounds. The large number of absent
compounds has always been a major concern. For those reasons as wellas improvement in the hit ratio of screening, an increase in the scale andvariety of the compound library is strongly desired. From a viewpoint ofsearching more promising compounds exhaustively, we expect much fromthe use of virtual libraries constructed in computers. However, existing
virtual libraries stock only several 10 million compounds at most. They are
paltry compared to the theoretical total. You can get only a partial view ofchemical space from their screening, so the fact is they are not very helpful.
Since it is a virtual library, it has another problem. Even if we can make ashort list of a group of high-score lead compounds, study of their synthesis
would require great cost. If we rely only on theoretical manipulation basedon a combination of atomic species and bond orders each atom can havefor creation of a virtual compound structure, the long-awaited library may
include many unstable compounds which cannot be synthesized.

● Feature and brief overview of large-scale virtual library“The large-scale virtual library composed of chemical structures able tobe synthesized and reaction schemes” we are engaged in constructing,is an unprecedented new virtual library that solves the aforementioned
problems and develops an acceptable quality, variety and scale of thegroup of virtual compounds. We don’t intend to simply make a “largescale virtual library”. It is important that the library consists of “chemical
structures able to be synthesized and reaction schemes”. The library also
includes synthesis routes for producing compounds. Of course, scale isalso of the essence. We aim at construction of a 1 to 2 billion-scale virtual
library, which exceeds the existing tens of millions-scale library, while
ensuring drug-likeliness and variety. What we have developed for thispurpose is a system to create new structures, in which seed structures are
put into the construction system sequentially from the existing compound
library covering 420 thousands compounds, followed by applicationof structure transformation information called Transform. Transformis structural change information on the reaction site before and afterreaction, which is extracted from a reaction database. In short, Transform isa database built by extracting and storing information on changes in bondorders and structural environments at reaction sites of the reactant andthe product of each reaction scheme, and or “essence of the reaction” from

Brief Overview of the System

existing reaction databases.
The virtual library with ordinary synthesis routes is constructed by applying
Transform information and continuously running an Ordinary Synthesis
Reaction Construction System that presents product structure for reactant
structure as a reaction scheme. Compound structures included in thevirtual library form an ordinary synthesis tree structure that maintains a
relation between reactant structure and product structure. In contrast,by continuously running a Retrosynthesis Reaction Construction Systemthat presents a precursor structure for the target compound structure as areaction scheme, a virtual library with retrosynthesis routes is constructed.
Compound structures included in this virtual library form a retrosynthesis
tree structure that maintains a relation between synthetic precursor
structure and product structure. Schemes for predicting products fromreactants shown in the ordinary synthesis tree domain, and a synthetic
precursor with the reaction site, are proposed from the target structure in
the retrosynthesis tree domain. Briefly, virtual compound structures not
only from the ordinary reaction direction but also from the retrosynthesis
direction suggesting the starting material, are included in the virtual
library.
When a seed structure of the existing compound library is actually entered,several candidates for the product structure appear by applying Transform.
Then by applying Transform to the candidates for the product structure asa reactant structure, candidates for the next-level product structure comeout. On the other hand, you can track back to the starting material for acertain compound. Some compounds have small molecular size. Or theylack drug-likeliness and are considered to be inadequate as candidates forthe lead compound. They are not retrieval objects of the virtual library.
However, since they are necessary information in the sense of connecting
synthesis routes, they are included in the library.
In this fiscal year, we are continuing scale expansion by converting theoutput structure into an input structure recursively and generating a
multistage scheme, and aim at storing a total of 1 billion not-overlapping,unique compounds including both ordinary- and retro-reaction schemesin the virtual library as a whole. By making them branch further and adding
initial seed structures, I believe a virtual library with 2 billion compounds
would be attainable.

Brief overview of the large-scale virtual library system

4 BioSupercomputing Newsletter Vol.7

SPECIAL INTERVIEW
Composition of the large-scale virtual library

Composition of Virtual Library
1 billion structures

500 million
structures

420 thousandmolecules

Arrows indicate virtual reaction scheme configuration information, open
circles indicate compounds that can be a lead compound, and filled
circles indicate compounds not suitable as a lead compound. Only forconnecting synthesis routes, compounds with a filled circle consist of
the library although they are not the target of search. In the ordinary
synthesis tree domain, a product prediction scheme from the reactant
is presented, and in the retrosynthesis tree domain, a proposal of asynthetic precursor for the target compound is presented.

500 million
structures● Assessment of library construction engine
solubility but also other characteristics. The empirical rule for predicting
such absorption is “Lipinski’s rule of five”. This time, we examined the
distribution of each characteristic value shown by “Lipinski’s rule”. As aresult, we confirmed that the virtual chemical structures output by thissystem take over characteristics of the group of input compounds while
expanding the distribution of characteristics values. We also confirmedthat when using a group of compounds with high conformance ratio foreach characteristic value index as a seed, the system can output a groupof chemicals with high index conformance ratio. Briefly, it shows thatappropriate selection of the group of seed structures enables output of
virtual compounds suitable for drugs with high probability.

While continuing fur ther development, we make assessments forunderstanding the features of the virtual library construction engine andgroups of output compounds. Although all compounds are not covered,
when we generated 15 million compounds from 420 thousand structures
used as a group of seed structures, we got 6.3 million non-overlapping,unique chemical structures. The overlapping percentage of output
compounds was 58%, a little more than half. As regards novelty, when
the group of 6.3 million generated compounds was compared with theexisting compound library including 15 million available compounds, theoverlapping percentage with commercially-available compounds was
only 1.33%. Therefore, most structures output by this system were novelcompounds. I conclude that novelty is fully ensured.

After this, we will actually input this large-scale virtual library to the “Kcomputer” and have it used by users including the public. Since screening
software is developed by another group, we provide the large-scale
virtual library. If a library construction engine for constructing elemental
chemical structures and reaction schemes is put into the “K computer”,
pharmaceutical companies, would-be users of the “K computer”, would be
able to construct a virtual library from their own compound library. The
library construction engine itself has already been developed by Funatsu
Laboratory. Since there is a great demand for this engine, we think we will
be providing this construction engine.

We are also examining the influence of character distribution of thegroup of input compounds. The virtual library is generated from the
input seed structures. As criteria to determine whether the generated
chemical structure has significance as a drug, we have ADMIT (absorption,
distribution, metabolism, elimination and toxicity) characteristics. By
calculating the characteristic prediction, we can assess the chemical
structure preliminarily. For example, an orally administered compound
does not work as drug if it is not absorbed into the body. Drugs are organic
compounds and have considerably large molecular weights, so basicallythey are insoluble in water. Naturally, they are absorbed poorly. Since this is
no good to us, those compounds need to maintain a certain level of watersolubility. Solubility is not a sole determinant. A large polar surface area
may increase solubility. Or, more hydrogen bond donors or receptors mayhelp them blend with water. Like this, they can be assessed not only by

We think we have almost finished preparations. From now on, we have tothink how to use the large-scale virtual library for promoting its practicaluse toward actual drug discovery while listening to what users want. I cansay that the large-scale virtual library is entering a new phase.

Influence of characteristic distributionof the group of input compounds
Group of input compounds A

Group of input compounds B

Molecular weight
Number of hydrogen bond donors
Number of hydrogen bond receptors
Calculated partition coefficient (cLogP)
Number of rotatable bonds
Polar surface area (Å2)
Molecular weight vs. Calculated partition coefficient
Polar surface area vs. Number of rotatable bonds
Number of hydrogen bond donors vs. Number of hydrogen bond receptors
Satisfy all the items
Characteristic distribution of output structure is influenced
by the characteristics of the group of input structures.
→ Probability of obtaining an appropriate group of output structures increases
by utilizing an appropriate group of seed structures.

An example of influence assessment on characteristic distribution of the group of
input compounds using Lipinski’s rule of five as index

BioSupercomputing Newsletter Vol.7 5

Report on Research

Old and new subjects consideredthrough calculations of the dielectric
permittivity of water
Institute for Protein Research, Osaka University

Haruki Nakamura（Molecular Scale WG）

Textbooks say that “water has approximately 80 times as high dielectric
permittivity as a vacuum, and exhibits significant polarization.” Calculationof the dielectric permittivity of purified water using molecular simulation
has had a long history, and some issues were clarified only recently. Looking
back at these conventional and new issues might give researchers somepoints to keep in mind not only in a molecular simulation, but in an entire
computational simulation. This paper describes an outline as follows.
The dielectric permittivity of a purified water system, ε is obtained from→the statistical average of the sum total of the electric dipole moment {μi } ofindividual water molecules { i } within the system, and is given as:

whereμ0 is the electric dipole moment of a water molecule, and G K is a scalarvalue called as the Kirkwood factor that is defined as:[1]
On the other hand, the distance-dependent Kirkwood factor given by theequation below serves as an important measure that reflects the alignmentand structure of water molecules with extremely high sensitivity ascompared to the radial distribution function (see Fig. 1 on the cover):[2]

The factor shows a correlation with the electric dipole moment of watermolecules on the first and second layers surrounding a water molecule,and even those on far-off layers. The second term of Equation [1] becomeszero in a sufficiently large statistical population, and therefore, G K(r) given
by Equation [2] where r is infinite is equal to G K that represents the dielectric
permittivity. In other words, a value on a sufficiently far-right point on
the horizontal axis in Fig. 1 corresponds to the dielectric permittivity. It is
calculated as approximately 96 by the particle-mesh Ewald (PME) method, astandard calculation technique, at 300 K and 1 atm under periodic boundary
conditions. The discrepancy from 80 would have arisen from the TIP3P watermolecule model1) used in the calculation.
Hydrogen-bond network relaxation in aqueous purified water is slow interms of dynamics. Therefore, as shown in Fig. 2 (a) on the cover, simulation
over a short period of time such as 1 to 2 ns is not enough for convergenceof the dielectric permittivity value, and a long-time simulation at least 6 ns
needs to be performed. The phenomenon, however, was pointed out in asystematic manner for the first time by Gereben and Pusztai (2011)2). Thismeans that the values obtained by simulation over a short period of timein other papers published before are not reliable. My team had been facedwith the same problem that the dielectric permittivity often varied in a short
trajectory for several ns even using the same computational simulation
method, and the finding by Gereben and Pusztai provided an unequivocal
answer to our question. Furthermore, it was found that the second termof Equation [1] has not become a negligible, small amount of time averagejust for 1 to 2 ns as shown in Fig. 2 (b) on the cover. These findings areconsequences through enhancement of computational resources.
In addition, there is another issue as regards handling long-range
electrostatic forces in purified water. Only the interaction between watermolecules within a certain cutoff distance d c had often been taken into
consideration because of limited computational resources although the PME
method is currently used as a standard technique. A simple idea is to set d c

6 BioSupercomputing Newsletter Vol.7

to as a large value as possible, but Yonetani3) pointed out in his paper that
G K(r) varied greatly by an order of magnitude in both positive and negative
directions even if d c was set to as large as 18 Å. The paper demonstrated thatit not only caused a quantitative variation of the dielectric permittivity, butalso failed to reproduce a qualitative water structure.
In the past, Neumann4) proposed a method in which the artifact causedby the cutoff mentioned above is removed by the Reaction field method,
assuming a dielectric material with the dielectric permittivityεRF lies outside
a sphere with a radius of d c , resulting in creation of a reaction field introduced
by Fröhlich5) . This method had been tried by many researchers, but has notbeen gaining high popularity recently, because it is difficult to estimate theparameterεRF in a non-uniform system such as a protein solution. Recently,
Dr. Ikuo Fukuda, RIKEN, proposed the “Zero-dipole summation principle”(ZD method)6)-8) as one of the non-Ewald methods. This is an outstanding
method that incorporates the long-range effects by imposing not only the
charge neutral condition proposed by Wolf 9) but also that of electric dipole
moment, allowing for high computational accuracy with a simple algorithm.
By using the method, almost identical values of dielectric permittivity and
G K(r) as those from the PME method can be obtained as shown in Figs. 1 and 2,
even though d c is 12 Å to 14 Å. Namely, the ZD method works well even in astate where molecular interactions are cut off within a short range and only
short-range interactions are taken into consideration. Interestingly, the ZD
method gives exactly the same equation under certain conditions as that in
the Reaction field method whereεRF approaches infinity. It was also foundthat the ZD method has some characteristics in common with other various
non-Ewald methods proposed recently 8). It seems that common physics
works as a basis in these methods in that no periodic boundary conditions
are given.
It is often the case in computational science that researchers compete witheach other on computational speed according to given algorithms. This isdefinitely necessary, but creation of the original algorithms or models would
open up completely new possibilities. My team has been working on studies
that apply the ZD method mentioned above to heterogeneous systems
such as protein and DNA solutions not as a periodic boundary system, but asa three-dimensional torus system, and are beginning to produce favorable
results.
In computational research using new algorithms and models, researchers
always conflict with reviewers who are insistent on the “Ptolemaic theory”
and have some difficulties in publishing a paper. However, successful
research results have a significant ripple effect. In fact, eliminating the useof the periodic boundary system in computation will allow us for easier
high-speed simulation of many biological supramolecules with fewer
computational resources.
Finally, my sincere acknowledgements are for the collaborators of thisresearch, Dr. Ikuo Fukuda (RIKEN), Dr. Narutoshi Kamiya (Institute for Protein
Research, Osaka University) and Dr. Yasushige Yonezawa (Kinki University).

[References]
1) W. L. Jorgensen et al., J. Chem. Phys . 79, 926 (1983); 2) O. Gereben, L.
Pusztai, Chem. Phys. Lett. 507, 80 (2011); 3) Y. Yonetani, J. Chem. Phys. 124,
204501 (2006); 4) M. Neumann, Mol. Phys. 50, 841 (1983); 5) H. Fröhlich,“Theory of Dielectrics” Clarendon Press (1958); 6) I. Fukuda et al., J. Chem.
Phys. 134, 164107 (2011); 7) I. Fukuda et al., J. Chem. Phys. 137, 054314 (2012);
8) I. Fukuda, H. Nakamura, Biophys. Rev. 4, 161 (2012); 9) D. Wolf et al., J. Chem.
Phys. 110, 8254 (1999)

Report on Research

Development of Fluid-structureInteraction Analysis Program forLarge-scale Parallel Computation
Advanced Center for Computing and Communication, RIKEN
School of Engineering, The University of Tokyo (until September 30, 2012)

Kazuyasu Sugiyama(Organ and Body Scale WG)

Blood flow has the functions of maintaining healthy conditions (such as
hemostasis, transport of substances, removal of foreign bodies and bodytemperature control). For example, if a blood vessel wall is damaged,
platelets adhere to the wall and form thrombi, allowing for repair of thevessel. On the other hand, overgrown thrombi caused by any factors
produce vessel occlusion, resulting in cardiac or cerebrovascular diseases
with a high risk of sequelae or death. Accurate prediction of normal andabnormal blood conditions by a physical computation methodology basedon logical rationales contributes to advancement of treatment and drug
discovery. Our research group developed an interaction analysis programthat combines fluid and structure dynamic actions (the ZZ-EFSI code) with afocus on blood flow phenomena at the continuum level.
Blood contains massive amounts of blood cells that deform flexibly. In a
microcirculation system with a submilimetric scale vessel, the deformabilityof red blood cells and the characteristics of dense particulate flow dictate
blood flow functions. The laws and the principles of dynamics (theconservation law, and constitutive equations that describe viscosity andelasticity) of blood and blood cells are simple. But, the blood system involves
phenomena over a wide range of the time-space domain and exhibits
complicated behavior. We have developed the ZZ-EFSI code with the aim of
performing large-scale computation based on simple laws and principles.
To this end, we determined new equations to be implemented andreexamined the computation scheme and algorithms, without having tuned
conventional interaction analysis codes, keeping in mind that we should
exploit the performance of the “K computer” [1, 2].

Fig. 1 : Weak scaling parallel performance of the ZZ-EFSI code in the “K computer”(left: effective computation speed, right: efficiency rate). The number of grid points
per compute node is 512 × 128 × 128.

that included red blood cells and platelets (see Fig. 2 (a)). Fig. 2 (b) and (c)shows the path of some platelets. Under the condition that no red blood
cells were included (Fig. 2 (b)), individual platelets had small changes inradial coordinates and moved along the vessel axis almost in a straightline. On the other hand, under the condition that red blood cells were
included (Fig. 2 (c)), platelets had larger changes in radial coordinates, orin other words, dispersed easily. These results mean that red blood cells
disrupt the blood flow and cause massive fluctuation of platelets, resulting
in increased opportunities for platelets to approach the vessel wall. This
theory is consistent with experimental results that suggest the importanceof disruption effects by red blood cells against formation of platelet thrombi.

Distance from center axis of bloodvessel/ radius of blood vessel

The recent scalar-type supercomputers such as the “K computer” arecharacterized by hierarchical parallel processing (MPI parallel computing
accompanied by communication between compute nodes, thread-level
parallelism between cores within compute nodes, and multiplex operation
We have introduced a model of platelet adherence to a damaged vessel wallwithin cores). We developed an Eulerian method that does not require[2] and are working on a study that demonstrates experimental knowledge
the process of mesh generation and reconstruction (more specifically,
concerning thrombus formation. If the efficacy of drugs can be assessed
all physical quantities are updated on a spatially-fixed mesh). In thisbased on patient-specific data in the future, it will facilitate development
computing program, the rectangular computational domain is divided
and discovery of new, attractive drugs. Toward this goal, we will be
into meshes in the X-, Y- and Z-axis directions to describe equations andcommitted to creating models of changes in physical properties, the process
allow for MPI domain decomposition. The Eulerian method is compatibleof coagulation and dissolution, and biochemical reactions.
with any hardware structure at all layers and excels in expanding the scaleof computation. Standard fluid applications tend to access memory too[References]frequently with respect to the operation load. This means that a huge
[1] BioSupercomputing Newsletter, Vol. 2, p. 11.
backlog is caused frequently in a scalar-type supercomputer that takes more
[2] BioSupercomputing Newsletter, Vol. 6, p. 2-3.
time to read and write memory as compared to performing operations.
The efficiency (the effective computation
speed as compared to the theoretical peak(b) No red blood cells included
performance) of the “K computer” is limited
to about 10 percent. To address this issue,
we developed an algorithm that requires
less frequent memory access to improvethe computation speed. Fig. 1 shows theperformance of fluid-structure interaction
analysis by the “K computer”. The efficiency
as a singl e n o de w as 4 6.6 p ercent , a(c) Red blood cells included
suf f iciently- high level for continuum
dynamics computation by a scalar-type
supercomputer. In addition, small changes
in the ef f icienc y with increase in thenumber of compute nodes mean high linear
scalability. We performed computation of
the system that contained approximately
5 million disp er s e d obje c t s by using
82,944 nodes with 6.96×1011 grid points,
and successfully achieved an ef fective
Fig. 2 : Computation results of massive dispersed objects in a blood vessel with a diameter of approximately 100 μm.
computation speed of 4.54 petaflops.
(a) Snapshots of blood cell distribution (red: red blood cells, white: platelets). Blood flows from left to right. (b) and (c)We have simulated a cerebral arteriole flow

Time-varying changes of radial coordinates of platelets (meaning the distance from the center axis of the blood vessel).
The path of platelets is different depending on the presence or absence of red blood cells.

BioSupercomputing Newsletter Vol.7 7

Report on Research

SiGN : Large-Scale Gene Network
Estimation Softwarewith a Supercomputer
Graduate School of Information Science and Technology,
The University of Tokyo

Yoshinori Tamada（Data Analysis Fusion WG)

Human cells are said to have about 20 to 30 thousand genes. The humanbody is mostly composed of proteins. The gene is a blueprint for proteinsmade in the cell. As well as the kind of protein, the timing and quantityof protein production is also regulated by special genes. Those genes(≒ proteins) are also regulated by another gene. Briefly, genes form
a complicated regulation network. Most of the system is still poorly
understood. Even among the same human cells, they have different
networks in different organs. Drugs modify gene networks and cancer
cells have destroyed networks. Gene network estimation is an approach
to infer or estimate such gene regulatory networks (=gene network) frommeasurable data by mathematical, statistical and informational scientificmethods. Although it is impossible to measure all proteins produced in thecell with current technologies, the amount of mRNA synthesized prior toprotein production is measurable for every gene. Data measured like thisis called gene expression data. Data obtained from one measurement area snapshot of cell status. It is impossible to infer or estimate regulations
between genes only with this one measurement. Massive data are necessary
for that. Therefore, we collect data necessary for estimation by applying
various stimuli to the cell, collecting cells from patients with a particulardisease or taking data temporally at regular time intervals. Inference andestimation of gene networks enables clarification of regulations between
genes by exhaustive computation instead of the conventional timeconsuming method of searching for genes one by one and repeating
experiments. It is expected that this approach will enable ef ficientdevelopment of new drugs, identification of cancer-specific genes, andunderstanding of the functions of such genes.
SiGN is software for estimating a gene network with a supercomputer fromgene expression data. As the gene network, various models have been
proposed. However, every model has both merits and demerits. None ofthem is by far the best. After deciding a model, we still have to choose amethod for estimating parameters from the data. Those methods also
have good and bad points. SiGN is a gene network estimation software
implementing multiple gene network models and estimation algorithms,
both of which requires vast amount of computation time assuming
computation using a supercomputer. In particular, SiGN is composed ofthree sub-programs, SiGN-BN using static and dynamic Bayesian networks,
SiGN-SSM using a State Space Model (SSM) and SiGN-L1 implementing aparameter estimation method by L1 regularization. SiGN-BN implements a
new algorithm called NNSR. Conventionally, gene network estimation using
Bayesian networks was applicable to about 1000 genes. Now it is applicable
to all genomes (all genes) thanks to NNSR. Temporal data allows SiGN-SSM toestimate dynamic gene networks that are able to be simulated. It does notgive the network structure but the strength of relationships among all genes
as mathematical values. Thanks to supercomputers, network structures
which have been difficult to compute, are now computable with a degreeof confidence. L1 regularization was originally applicable to large-scale
gene networks. However, the computation time of conventional methods
is not enough to estimate networks focusing on individual differences ingene expression. By using the K computer, it is able to be computed within arealistic time-frame.
Development of SiGN is targeted mainly at the K computer and Shirokane, asupercomputer of the Human Genome Center. Several sub-programs havealready been installed in Shirokane and are available for users. For more
information, please contact the SiGN website at http://sign.hgc.jp.

Gene Network Estimation
Inside the cell
Transcriptional factor

GenemRNA measurement

Gene expression

DNA chip (Microarray)
Gene

Estimation

Regulation
Gene

Gene network

8 BioSupercomputing Newsletter Vol.7

Reverse engineeringof the underlying system

Gene expression data

Discovery of the target gene of a novel drug
Prediction of side eﬀects
Understanding of gene regulation speciﬁc to cancer cells

Report on Research

ISLiM research and development
source codes to open to the public

Computational Science Research Program, RIKEN

Eietsu Tamura

1. A software research and development project
unprecedented anywhere in the worldprocess of the release, and share in the progress as shown in Fig. 1.

One of key goals of the ISLiM project is to develop software that allows the
K computer to realize its ability, to publish excellent scientific papers, and to
make the software available to the K computer.
One of key advantages of ISLiM is that it consists of about thirty codes,
which comprehensively cover everything from the molecular scale to bodyscale, and from simulation to data analysis, and that they are tuned for the K
computer in a sophisticated way. The software built in this comprehensive
manner in the life science/healthcare field is unprecedented in the world,and confers significant value as a Japanese software asset used in research
applications as well as educational applications.

2. Activities for releasing the source code
Since the latter half of 2010, this project, in collaboration with “ResearchGroup for Promoting the Utilization of Next-Generation Supercomputers in
the Drug Discovery Industry,” has promoted exchange of information withthe domestic pharmaceutical industry in terms of how to utilize the software
after its completion. In these discussions, we reaffirmed that when publicly
releasing such leading-edge software, it is important to publicly release
the source code that can be verified and corrected by users rather than toprovide binary code such as commercial software that is much used, andcan be provided together with the structure for providing prompt support.
In addition, we reaffirmed that there is a need not only for the “K computer”
version, but also for a “cluster system version” commonly used in business.

◦ Sharing goals via principal developer's meetings
To release the source code,
it is important that softwaredevelopers specificallyunderstand the significance ofand the required process forreleasing the source code, anddispel doubts and concerns
about the release. The ISLiM
project was establishedand discussed by principal
developer's meetings threetimes, i.e., on November 9th
2011, on February 21st 2012,and on July 23rd 2012. In thesemeetings, we explained andd is c us s e d t h e p u r p o s e a swell as received useful advice
from Mr. Takahiro Honma, aspecially appointed professorof the Center for Industrial
and Governmental Relationsof The University of ElectroCommunications, and Kazuki
Shigemori, a patent attorney ofAndersen Mori & Tomotsune,
who has go o d k nowle dgeof intellectual property withrespect to software. In terms
of promotion of the project,
we provide a “Flow Chart toPrepare for Releasing ISLiM
Developed Software” so thatthe persons responsible forprogram development canunderstand the standard◦ Each software program available from the download site as it isready to be released
We have set up a download site (http://www.islim.org/islim-dl_e.html) in2011 so that our source code is widely utilized not only in academia but also
in industry, and released each software program when it was ready to bereleased as shown in Fig. 2. A goal set by principal developer's meetings is torelease 50% of all the software in April 2012, and 100% in October 2012, sixmonths before completion of the project. We plan to conduct promotional
activities, such as debriefing meetings and training sessions, during these six
months.

3. Current status of the public release of the source code,and challenges for the future
Thirty four (34) codes were involved when we started research anddevelopment in 2006. Some software programs have been consolidated
into one program in the final stage of development and, as a result, about
thirty (30) codes are expected to be provided. For the latest information on
the release status, visit our download site. Access to either the “K computer”
version or the “cluster system version” can be switched over by a dedicatedcompiler.
We have been investing a great deal of resources to research anddevelopment of the software for six years. Challenges that remain to beaddressed are how we bring such valuable Japanese software asset to next
players effectively by a project completion.

Team: Molecular Scale Team

(Deadline for response: June 20th 2012 (Wed.))Date of Response:

Please provide the target release date for each development code (for both K computer and cluster versions), and update the details.
Code Name
Development
Notification“-K” is used for the Copyright
Copyright holder who
Integrating into
Other codes are Measures in case other Public license
Code Nameprocedure forcode name of the K holder’s check performed the checka public package
included?
codes are included
affiliated institutions
computer version?
Platypus-MM/CG(mu2lib-k)
Target release date(July 1st)Yes
No (Reason)

Being checked RIKEN
Acceptanceprocess in progress
CheckedBeing checked
CheckedQuestions and comments

Being checkedNo
Yes

Follow the license
Obtain permission to use
Under consideration

GPL
MIT
OthersDocuments such as manuals
Not yet drafted
Drafting in progress
DraftedFinal version is readyFig.1 : Sharing the progress status

Fig.2 : A part of the download site ( http://www.islim.org/islim-dl_e.html)

BioSupercomputing Newsletter Vol.7 9

S

PECIAL INTERVIEW

Strategic Programs for Innovative Research Field 1 Supercomputational Life Science

Theme 1 Simulation of Intracelluar Molecular Dynamics

Understanding Biomolecular Dynamics
under Cellular-Environments by Large-Scale
Simulation using the“K computer”
Chief Scientist, Theoretical Molecular Science Laboratory,
RIKEN Advanced Science Institute

Yuji
Sugita左より、木寺、柳田、江口（Theme1 GL)●Overview of the Project
Simulation studies in life science are now one of the most active research
fields with rapidly progressing development of methodologies andalgorithms. Many experimental data such as genome information, atomic
structures and intracellular protein expression information are obtained one
after another. Conventional life science focused on only the experimental
data. However, it’s about time that we integrate these experimental dataand promote better understanding of biological phenomena in a living
system. In that sense, simulations achieved by the excellent computing
capability of the “K computer” can be said to play a great role in changing
life science into a new, predictable and controllable research system. It wasin these circumstances that Strategic Programs for Innovative Research
Field 1 Supercomputational Life Science began. In the theme we areengaged in (biomolecular simulations under cellular-environments), we
aim at understanding and prediction of intracellular molecular dynamics byperforming large-scale computer simulations on a molecular and cellularscale in which we focus intensively on the cellular environment. Manybiomolecular simulations have been done so far. However, most of themtried to reveal the behavior of a single protein or DNA in aqueous solution
or in a lipid bilayer membrane. When computed from the number ofproteins in the cell and size of the cell, there is no doubt that the cytoplasmic
environment is very different from the environment of aqueous solution.
This has also been revealed experimentally. Just near one acting protein,
there are other proteins. It is not yet completely understood how such anenvironment (intracellular molecular crowding environment) influences●Goal of Molecular Simulation Studies
In order to realize Theme 1, “Molecular and cellular-scale simulations in thecellular environments,” two elements are necessary. One is simulation of the“considerably slow motion” of biomolecules such as proteins and nucleic
acids. The other is elucidation of cellular functions from the molecularpoint of view by connecting molecular-scale (in the atomic detail) studies
with cellular-scale (in the molecular details) studies. As for the former,
we would like to attempt the simulation of millisecond-scale structuralchanges. Although I said it is slow, it is slow as our researchers’ standards.
Actually it is an extremely fast molecular movement. However, in current
molecular simulation studies, the time scale of long dynamics is microseconds. Therefore, millisecond molecular movement is a thousand times
longer than microsecond molecular movement, and thus far slower. The
computation environment available to us before completion of the “Kcomputer” was about 100TFLOPS or so. Thanks to the advent of the “KUnderstanding of the cellular environments
and their role on biomolecules is the first step
toward the whole cell modeling.

10 BioSupercomputing Newsletter Vol.7

protein structure, stability and functions. In previous theoretical studies,
the macromolecular crowding was studied using a very simple model inwhich a protein molecule was approximated to one particle. But, under
this assumption, detailed intra-molecular interactions cannot be taken
into account in the simulations. In this project, we would like to look at alot of proteins under conditions close to the cellular environment on an
unprecedented scale.
We have the following three sub-projects: The first is “Molecular andcellular-scale simulations in the cellular environments” for developing a
new research field by cooperation between single-particle simulations
and molecular dynamics simulations. The second is “Substance transport
across the cell membrane by membrane proteins” for achieving aquantitative and predictive molecular simulation by free energy calculations
based on extensive molecular dynamics simulations. And the third is“Understanding the dynamic structures and functions of DNA and proteinsin a nucleus”, analyzed with an all-atom and coarse graining molecular
dynamics calculation. For understanding life phenomena and linking themto prediction, it is essential to understand a “whole cell” by integrating
molecular information with cellular information. For this purpose, at theend of the project, we would like to define the next challenge aiming at“Whole cell simulation” by using the findings obtained from research anddevelopment.

SPECIAL INTERVIEWIn order to see the molecular dynamics of an ion pumpin biological membranes by simulation, a long streamof computing including about 260 thousand atoms
such as proteins, lipid bilayer, water and ions, as well as
their molecular interactions, is required.

Simulation by changing protein concentration. Protein molecules crowded in cytoplasm are simulated bymolecular simulation, showing the differences in hydration effect between in dilute condition and in crowded
environment.

computer”, it is now enhanced 100 times (the calculation performance of“K computer” is about 10PFLOPS (10,000TFLOPS)). It is actually considerably
difficult to make a calculation 1,000 times longer by similar utilization ofcomputers. Therefore, by developing novel computational approaches, we
try to see millisecond dynamics. For this purpose, we have already beendeveloping advanced parallelization techniques. For example, we have analgorithm called the multi-dimension replica-exchange molecular dynamics
calculation. In this method, molecular dynamics calculations at different
temperatures or with different parameters are performed in parallel for the
copy of a system called a replica, and by exchanging temperature or anotherparameter at a certain frequency, the calculation can be accelerated. It isdifficult to do parallelization with several ten thousand CPUs for a single
molecular dynamics calculation. However, in the replica-exchange method,
the molecular dynamics calculation of each replica is parallelized withseveral hundred to several thousand CPUs. By providing several ten to

several hundred replicas, simultaneous and efficient utilization of several
ten thousand CPUs is enabled. We have to integrate those various methods.
Although we use the “K computer” having unprecedented computing
performance, what we can do only by computation is limited as I havealready described. Therefore, I believe collaboration with experiment is of
the essence in order to implement this project. For example, simulations
can collaborate with structural biology, in particular, X-ray crystallography
and NMR (nuclear magnetic resonance). In addition, we can collaborate withsingle-molecular experiments to measure dynamics of proteins and protein
complexes in cell in a most direct way. Experimental difficulties often arise
from the time-resolution, whereas simulations usually suffer from insufficient
sampling due to the short computational time. At milliseconds, real-time
experimental measurements correspond with the simulation results at thesame time scale.

●Results of Molecular Dynamics Simulations
While proceeding with development and upgrading of software for efficientutilization of the computing performance of the “K computer”, some simulation
studies have already been implemented and produced results. One of themis the simulation, which takes account of the cytoplasmic molecular crowding
environment, and we are taking the lead in implementing this simulation study.
In the study, we could show the differences in hydration of proteins between
in dilute condition and in crowded environment. This difference is essentially
important for protein conformational stability, dynamics, and biochemicalfunctions. Since microscopic parameters for the intracellular environment
are difficult to measure experimentally, this computational result can be said
to be quite helpful also for experimentalists. By utilizing the “K computer”
hereon, molecular crowding analysis in much large-scale systems will be
studied. Further progress in this research is expected. Molecular simulation of
membrane transport protein has also made great strides. Conventionally, it
took some time to provide conformational dynamics of membrane proteinsby molecular simulation after clarification of X-ray crystal structure. Recently,

we can perform molecular dynamics calculations including a lipid bilayer
membrane immediately after the determination of the X-ray crystal structure.
Structural change taking place partly in the transport cycle of membranetransport proteins has already been unveiled by simulations and experiments.
We expect to perform molecular dynamics simulations to reveal substancetransportation across membranes by membrane transport proteins using thefull power of the “K computer”. The project we are engaged in is expected
to contribute not only to basic science, but also drug discovery and medicalcare. The information observed here would be useful for such applications
in near future. Finally, I would like to emphasize again the importance inthe developments of simulation methodologies and models to achieve ourresearch goals. We also need strong computational skills to realize large-scale
molecular and cellular simulations in this project. For this, the participation ofyoung researchers or students is requisite. To increase the number of thosepeople is also one of the tasks of the project over the long term.

BioSupercomputing Newsletter Vol.7 11

S

PECIAL INTERVIEW

Strategic Programs for Innovative Research Field 1 Supercomputational Life Science

Theme 2 Simulation Applicable to Drug Design

Innovative molecular dynamics drug
design by taking advantage of excellent
Japanese computer technology
Professor, Research Center for Advanced Science and Technology,
The University of Tokyo

Hideaki Fujitani（Theme2 GL)●Shooting for Real Drug Discovery
Thanks to the advent of the K computer, improvement in computer capacity
enabled molecular dynamics calculation to see how a drug molecule acts on
and binds to a disease target protein. With this step, an IT innovation in drug
discovery, in which atomic-level changes in protein shape are revealed for
drug design, is about to begin.
In Strategic Programs for Innovative Research Field 1 SupercomputationalLife Science, Theme 2 Simulation Applicable to Drug Design which we areengaged in, we make the fullest possible use of the K computer to establish
new Computer Aided Drug Design (CADD) technologies for innovating the
drug discovery process, as well as to discover real drugs. Although they areboth within the field of molecular simulation, Theme 1 deals with broader
phenomena with biological importance. In Theme 2, objects are narrowed
down to disease target proteins which are the target of drug discovery. Thisis the feature of Theme 2.
Until now, neither national institutes nor national universities havediscovered drugs independently. This is because drug development
including clinical trials is very expensive, costing 20 to 30 billion yen. In
addition, in the step before clinical trials, most institutions and universities

unfortunately have facilities that are not suitable to synthesize numerous
drug candidate compounds. Since antibody drugs are proteins anyway,
basically any university can synthesize them and does not have to relyon pharmaceutical companies for synthesis. However, synthesis of lowmolecular weight compounds costs a fortune for their design. Therefore, it is
impossible at present for universities and institutes to do the entire processof drug discovery from the development phase to clinical trials.
For those reasons, we arrange tie-ups with pharmaceutical companies
from the very beginning in our project, and proceed with the developmentin the form of collaborative study. We are obliged to get pharmaceuticalcompanies involved to translate our project into real drug discovery. You
may think government funds are being used for business, but the facts arecontrary to that. In the research and development phase, companies cannot
avoid expenses. In order to get them involved in collaborative studies evenin this situation, we have to produce convincing simulation results. For
discovering a real drug, both we and pharmaceutical companies have totake risks and make a serious effort.

●It is Important to Become a Pioneer of IT Drug Discovery
In these twenty years, drug discovery by computer simulation has been
attempted many times but not yet fulfilled. The major reason for this is we
did not have enough computation power to calculate the protein itself. In
order to simulate a phenomenon in which a compound that may become adrug binds to a protein in solution and inhibits the function of the protein,at least 50 to 100 thousands of atoms are involved and 200 thousands ofatoms are involved when the protein is large. Then about several million
calculations are required. In addition, it was recently revealed that whether
a compound is active is not is determined by simulating only one protein
molecule, and simulation of the whole system is necessary. Then, thenumber of atoms involved easily surpasses 1 million. At last, the K computerhas given us the computation power that we wanted. So far it has been
determined empirically whether a compound binds to a target protein, but,
now it becomes clear by computer-aided calculation. At last we have createdenvironments for developing drugs logically.
The IT drug discovery (Fig. 1) efforts we are engaged also started in Europe
and the US at almost the same time. You may know Mr. David E. Shaw (US)
who made a special computer for MD calculation, ANTON, and is proceedingwith IT drug discover y together with Mega Pharma (mega pharmacompany). Due to those activities, Japanese pharmaceutical companies
which expressed their doubts became interested in IT drug discovery.
However, since it has not yet produced any favorable results worldwide,
they have not reached the stage for investing in and working on IT drug
discovery. So we decided to become a pioneer in this field.
Now that we are on the starting line with other developed countries, it isimportant to start off as a pioneer. While collaborating with researchers frompharmaceutical companies in efforts geared toward real drug discovery andstudying what to calculate, which result we should use and how to use theresult in compound design, I believe we can expand the horizons of researchand development of IT drug discovery. After they understand that they reallycan develop drugs through IT drug discovery, supercomputers comparable
to the K computer will be introduced in major pharmaceutical companies
possibly 5 years from now to promote further research. Opening up such a
new epoch is the final goal of this project in a sense, and I also think it is oneof the important missions for the K computer.

●High-accuracy Prediction of Drug Efficacy by Use of the High Computing Power of the K computer
Since many drugs target proteins, we have to find a compound that interacts
strongly with the intravital target protein (ligand) to discover a more
effective drug. We aim at rapid and efficient development of low molecularweight drugs in the following manner. By use of molecular dynamics
calculations using the supercomputer, we run a simulation of a system
including a target protein and a drug candidate compound, investigate
protein-compound interaction, and design a new compound which acts
only on the target protein.
For this purpose, we devised the MP-CAFEE method (Fig. 2) which is analgorithm for calculating free binding energy using the Jarzynski relation for

12 BioSupercomputing Newsletter Vol.7

free energy difference and non-equilibrium work discovered by Jarzynski
in 1997. In this method, molecular dynamics calculations are performed on
multiple intermediate states between two states. In one state, full interaction
exists between the compound and other molecules. In the other virtualstate, interaction disappears completely and the compound is uncoupled.
Then, the free binding energy is calculated from the work necessary forthe transition to the adjacent state. This enables accurate computation of
the free binding energy between the target protein and the compound in
solution. The feature of the MP-CAFEE method is that its accuracy is always
high for any kind of protein and is not influenced by protein characteristics
due to atomic-level calculation. Since such a huge calculation power isSPECIAL INTERVIEW

CompounddesignSynthesis
evaluation

Identification of
disease-causing
protein

Design of acompound thatacts on the protein

Making thecompound andtesting

Conventional Search for candidate substances
drug discovery among existing compounds

Improvement by repeatedsynthesis and testing

IT drug
discovery

Similar structures
Difficulty in predicting
drug efficacy

Empirical value

Simulation with asupercomputer

Narrowing down to compounds withhigh novelty and high drug efficacy

Creation of various
structures
Highly-accurate
prediction of drug efficacy

Predictionin advancePreclinical
trial

Clinical trial

Animaltesting

HumantrialsLaunch ofthe drug

Targetidentification

Fig.1 : Difference between conventional drug discovery andIT drug discovery
Figure by Fujitsu Limited

Low success rate

despite high cost andtime investment

Reducing cost and timeby increasing
the success rate＋

Breakthrough new drug

Massively Parallel Computation of Absolute binding Free Energy

Fig.2 : Massively parallel computation of absolute binding freeenergy MP-CAFEEIn MP-CAFEE, massive molecular dynamics calculations are
performed while changing drug-interaction parameters withproteins and a compound (drug) in a state of thermal agitation
in solution. The binding free energy of the drug with the target
protein is calculated from the work distribution when theparameter changes.

Non-equilibrium equality (Jarzynski in 1997)

required for that, people often asked to us what kind of computer on earth
we were going to use when we announced the project in 2005. However,thanks to the development of the K computer, conditions for producing
tangible results are now satisfied.
In May 2011, we began work on porting Massively Parallel Computation of
Absolute binding Free Energy (MP-CAFEE) into the K computer. So far, wehave confirmed that MP-CAFEE works correctly in the K computer. Presently
we are making adjustment for faster calculation. In August, we started
calculations for actual development of drugs against target proteins ofcancer and leukemia by using MP-CAFEE modified for the K computer. Thisrequires searching for candidate compounds that may work well among
several millions of compounds while checking their chemical structures.
Therefore, we collaborate with the Bio-IT Business Development Unit, Fujistu
Limited which runs a computer-based drug design business, and selected
several hundreds of candidate compounds for MP-CAFEE calculations.
Candidate compounds include not only existing compounds. We also
design novel compounds that work better. In such a case, we of course
check whether it can be synthesized and is free of toxicity (side effects). It
is an advantage of IT drug discovery that it can reduce development costs
and time. However, its maximum benefit is that we can design various

new compounds and predict candidate compounds through molecularsimulation without being limited by existing compounds.
In this fiscal year, I believe we can produce a series of drug candidate
compounds against relatively small target proteins. Because of computation
time, we give priority to the surest thing, and then prepare to handle larger
and more difficult targets in the future. We will practically complete thisproject in three years. The biggest goal of our project is to develop several
compounds that may be studied in clinical trials by then. Of course it
includes the results we will produce in this fiscal year.
As I mentioned before, it is one of the objectives of this project to get
Japanese pharmaceutical companies interested in IT drug discovery thathas been failing to produce successful results, and to have them work withus. Thanks to worldwide interest and the completion of the K computer,
we have received favorable responses and offers from pharmaceuticalcompanies. Since it is a national project, we have talked with many
pharmaceutical companies about collaboration. Many companies havealready given us various proposals. Some of them wish to start during
this fiscal year. Since we are not yet ready to get off the block, we are now
continuing discussions with them and are preparing for the future.

BioSupercomputing Newsletter Vol.7 13

ReportLecture on Computational Life
Sciences for New undergraduate
Students
HPCI Program for Computational Life Sciences, RIKEN

Chisa KamadaThis February, Mr. Yukihiro Eguchi, Deputy-Program Director of Field Oneof the Strategic Programs for Innovative Research (hereafter “Field One”),
delivered an invited lecture in the “4th Science Fair in Hyogo” at the Kobe
International Exhibition Hall. Following the presentation, Professor Kuniyoshi
Ebina asked that Field One provide a Special Lecture for the Kobe University
subject, entitled “Invitation to Human Development Interdisciplinar y
Subjects”. This compulsory course targets undergraduate students entering
the Faculty of Human Development.
The Field One Special Lecture was delivered on June 29 and again on July
13 by Mr. Eguchi (293 attendees in total: 140 male, 153 female). The Kobe
University Faculty of Human Development encompasses both the liberal artsand the sciences, and addresses various issues on human development andits surrounding environment. Students in the faculty range from those who
major in astrophysics and computational biology to those who major in art,
psychology, and sports science. They are also a diverse and representative
group of Japanese young people in terms of intellectual curiosity. The titleof the Field One Special Lecture was “What are Interdisciplinary Subjects?
—Using Examples of the Integration of Biology with Physics, Chemistry,
and Mathematics through the Mediation of the K Computer”. Mr. Eguchi s
dual focus was the K computer and its versatility, and the importance ofinterdisciplinary science.
Field One is continuously engaged in educational and outreach activities
for high school, college, and graduate school students. Most of the students
we encounter major in one of the sciences and are already interested incomputers. With the Field One Special Lecture, we were able to reach students
with little or no previous exposure to the sciences and the K computer. As aresult, we saw a big difference in the understanding of students before andafter the lecture.

Biology

6h

r. 4

7m

0m

in.

in.
Mathematics

14

Mathematics.

min

14 BioSupercomputing Newsletter Vol.7

Biology

10 min.

During the lecture, Deputy-Program Director Eguchi explained that biology
is not a science separate from mathematics, physics, and chemistry, but
complements them, and that the K computer can further strengthen suchconnections. To supplement the explanation, he provided pattern diagrams
using global maps. Such notions seemed very new to many of the students, who
had just passed university entrance examinations, and all appeared surprised.
When the lecture concluded, one liberal arts student stated “It can be said

Biology is a complementary discipline to mathematics,
physics and chemistry!

.
min
r. 5

Old supercomputers have been transformed into the personal
computers and mobile phones that we use today, and the state-of-theart K computer is destined to become a familiar part of our daily life
within the next twenty years. In turn, new supercomputers will be born,
and we will enter an unprecedented era in modern technology. Just thethought of it is exciting. –excerpted comment–

Finally, Field One wishes to extend its deepest appreciation to those involved
from the Faculty of Human Development, Kobe University, including Professor
Hounoki, Dean of the Faculty, Professors Ebina and Shirasugi, and, most of all,the 293 undergraduate students.

14 h

The second most common reaction from students was that they were
surprised to discover that supercomputers developed during the past twenty
years are impacting our life today, and the prediction that the technology oftoday’s supercomputers will become more common in daily life. Knowing thehistory and background of supercomputers, they were also surprised about
the rapid progress of science and technology, and became more interested
in supercomputers. One of the feedback comments, from which one canappreciate how impressions were changed by the lecture, is shown below:

Thanks to Professor Naoko Shirasugi of Kobe University, Field One received
feedback about the lecture. It was a good opportunity for us to hear the
students’ direct and honest opinions. It appears likely that the understandingof the relationship between supercomputers and society shared by many
students reflects a broader view within Japanese society. In order to win public
understanding of the need for supercomputer technologies, it is important tomake efforts to “familiarize” the public with supercomputers, even reaching
those with no immediate interest in the subject. We hope the activities of Field
One will help nurture young people, who will forge our future.

9 hr. 45 min.

The most striking response from the students as to supercomputers andthe K computer was that they believed supercomputers were different fromwhat they had expected. More than ten percent of students thought thatsupercomputers had nothing to do with daily life. Those already aware ofsupercomputers had wondered what kind of change they could expect from
improvements in computational speed, until they learned about the versatilityof the K computer and its applicability to diverse simulation challenges. Some
respondents described a negative image of supercomputers, because theyoften heard in the media that investment in supercomputers represents a
waste of tax money. This Special Lecture offered a good opportunity for us toexplain the importance of the K computer in society to such students.

that science supports progress in the world now, but I think it is important tointegrate liberal arts into science. What’s your opinion?” Mr. Eguchi responded
that real-life problems could not be solved by science alone, and described theimportance of interdisciplinary subjects, illustrating his answer with examples
of approaches to create a safe and secure society. As previously described, thefocus of the Faculty of Human Development is interdisciplinary. It was apparent
that more than a few of these newly entered students felt anxiety about college
life, because of the challenges posed by the more diversified curriculum thanundergraduate students at other faculties of Kobe University. It was gratifying
to see all participants better understand the importance of acquiring a broad
undergraduate education.

Chemistry

Chemistry

Biology

Biology

Pattern diagram: The K computer can strengthen ties between academic fields.
(http://www.data.kishou.go.jp/obs-env/portal/chishiki_ondanka/p01.html)

As part of efforts to strengthen educational and outreach activities, thelectures planned and given by the Faculty of Human Development, Kobe
University and Field One in collaboration with RIKEN Advanced Institute for
Computational Science and the other four strategic fields in the Strategic
Programs for Innovative Research, are as follows:“Special Lecture on Natural Environmental Science D”
in the second semester of FY 2012
- New science opened up by the K computerNov. 17 “Computing Machinery and the K computer in Society”
RIKEN Advanced Institute for Computational Science“History and Changes of the Magnificent Natural Environment
Surrounding Us”<Field 5>

Dec. 1“How to Assess the Severity of Energy Problems,and How to Overcome Them”<Field 2>“The Mystery of Microorganisms Coexisting with Humans”<Field 1>

Dec. 15 “The Environment Created by Humans, its Safety,

and its Soundness” <Field 4>“Roles of the K computer in the Study of Massive Ocean-trench
Earthquakes and Disaster Mitigation”<Field 3>

Promotion

Research System for Computational
Science

Working Group of Field 1 of the Strategic Programs for Innovative Research, MEXT
Field 1 Manager (Haruki Nakamura, Osaka University)
Advisory Committee

Toshio Yanagida, Program Director (PD), RIKEN

Research and Development

Close collaboration / Coordination(Akinori Kidera, Deputy PD, RIKEN)

Establishment of the Research System
for Computational Science(Yukihiro Eguchi, Deputy PD, RIKEN)

Process management / Coordination of overall R&D

Theme 1: Simulations of biomolecules under cellular environments

High Performance Computing Development Group■ Yuji Sugita, GL, RIKEN Advanced Science Institute■ Makoto Taiji, GD, RIKEN

Koichi Takahashi, RIKEN
Motonori Ota, Nagoya University
Ryuichiro Ishitani, The University of Tokyo
Hidetoshi Kono, Japan Atomic Energy Agency

1) Management of computational resources2) K computer-use support in Field 1

Planning and Coordination Group

Theme 2 : Simulation applicable to drug design■ Hideaki Fujitani, GL, Research Center for Advanced Science and Technology, The University of Tokyo

Noriaki Okimoto, RIKEN■ Satoru Tomita, GD, RIKEN

3) Human resource development
4) Establishment of human networks5) Dissemination of research outcomes
6) Interdisciplinary projects

Theme 3 : Hierarchical integrated simulation for predictive medicine

7) Holding of steering group meetings andholding of external advisory committees■ Shu Takagi, GL, School of Engineering, The University of Tokyo

Yoshihiko Nakamura, The University of Tokyo
Kenji Doya, Okinawa Institute of Science and Technology Graduate University
Taishin Nomura, Osaka University

Steering Committee

Theme 4 : Large-scale analysis of life data
■ Satoru Miyano, GL, Institute of Medical Science, The University of Tokyo

Yutaka Akiyama, Tokyo Institute of Technology
Kiyoshi Asai, The National Institute of Advanced Industrial Science and Technology
Hideo Matsuda, Osaka University
Takashi Gojobori, National Institute of Genetics

Partnership

Outside organizations

Coordination of project management
through regular meetings(PD) Program Director(Deputy PD) Deputy Program Director(GL) Group Leader(GD) Group Director

Use of HPCI computing resources

Molecular biology, cell biology, biological physics, leading-edge experimental facilities,
pharmaceutical companies, hospitals, various databases, etc.

BioSupercomputing Newsletter Vol.7 15

Event Information
Announcement of the ISLiM International Symposium

4th Biosupercomputing Symposium⃝Date : December 3(Mon) -5 (Wed), 2012
⃝Location : Tokyo International Forum (Chiyoda-ku, Tokyo)⃝Conference fee : Free (get-together fee is charged.)
http://www.csrp.riken.jp/4thbscs/top_e. html
For more details, please see the website

Academic-Industrial Collaboration in the Supercomputer“K Computer”and Drug Discovery/Medical Care
─ HPCI Program for Computational Life Sciences ─⃝Conference fee: Free

For more details, please see the website

http://hpci.me.es.osaka-u.ac.jp/

Third Seminar⃝Date: December 19 (Wed), 2012, 13:00 - 17:00⃝Location: Umeda Center Building (Kita-ku, Osaka)

Fourth Seminar⃝Date: January 25 (Fri), 2013, 13:00 - 17:00⃝Location: Fukuracia Tokyo-Station (Chiyoda-ku, Tokyo)

News

The logo design for
Field 1 was completed

Logo design concept
The monogram combining the two letters of “S” in the motif of the double-helicalstructure of DNA symbolizes the life sciences, and a circle behind the monogram
symbolizes the “cell = life” and “circle = harmony and coordination (cooperation)”.
Navy blue which is the color suggestive of intelligence represents supercomputers,
and orange which is the color suggestive of upward mobility and liveliness represents
life phenomena and life sciences. “SCLS” is an acronym for the theme of Field 1“Supercomputational Life Science” and is pronounced as “esukurusu”.
Please also see the website of Field 1 (http://www.kobe.riken.jp/stpr1-life/outreach/pr/logo_stpr1.html)

The Development and Use of the NextGeneration Supercomputer Project of the
Ministry of Education, Culture, Sports,
Science and Technology (MEXT)

Strategic Programs for Innovative
Research Field 1

Next-Generation Integrated Simulationof Living Matter
The “Next-Generation Integrated Simulationof Living Matter” is a project sponsored by the
Ministry of Education, Culture, Sports, Scienceand Technology(MEXT ), in which researchand development of simulation software tounderstand various phenomena occurring in the
biological systems, including molecules and thehuman body, have been undertaken to realize
a petascale simulation by making full use of theperformance of supercomputer “K computer”.

Supercomputational Life Science
SPIRE (Strategic Programs for Innovative Research)
is a MEXT program aiming to produce groundbreaking results in computer science technology
by maximizing the benefits of the HPCI (High
Performance Computing Infrastructure) centeredon the supercomputer “K computer”, andencouraging developments in five research fields
that need to be strategically addressed.
“Supercomputational Life Science” has conducted
research with the mission of understanding andpredicting life phenomena based on large-scale
simulation and advanced data analysis, and to
apply these results to design and implement
medicine and medical care with its research.

BioSupercomputing Newsletter Vol.7 2012.12
RIKEN

Research Cluster for Innovation
Computational Science Research Program
2-1 Hirosawa, Wako, Saitama 351-0198, Japan
Tel : +81-48-462-1488 Fax : +81-48-462-1220
http://www.csrp.riken.jp

Issued: December 2012

HPCI Program for Computational Life Sciences
7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe,
Hyogo 650-0047, Japan
Tel : +81-78-940-5692 Fax : +81-78-304-8785
http://www.kobe.riken.jp/stpr1-life