The PubChemQC project aims to create a comprehensive database of molecular properties through first-principles calculations, addressing the challenges of big data in chemistry. The project utilizes advanced computational methods, particularly density functional theory (DFT), to generate molecular data for potential drug candidates and aims for a Google-like search engine for chemistry. Despite the project's progress, including the processing of over a million molecules, the completion and comprehensive understanding of molecular data remain a significant challenge.

1. The PubChemQC Project
A big data construction by first-principles
calculations of molecules
中田真秀(NAKATA Maho)
ACCC RIKEN
maho@riken.jp
2014/12/3 10:35-11:05
JST CREST International Symposium on Post
Petescale System Software

2. Background
• Atoms and molecules are all composed of matter.
• The dream of theoretical chemist: do chemistry
without experiment!
• On computers 
• We treat big data in chemistry!
– Chemical space is really huge!
• The number of candidates for drugs
1060
http://onlinelibrary.wiley.com/doi/10.1002/wcms.1104/
abstract)
• Cf. Exa: 1018

3. Current status of computational
chemistry
• Relatively good agreements with experiments.
• Can explain nature in many cases.
– Many good quantum chemistry programs are
available!
– “DFT B3LYP 6-31G*” calculations rule!
• We want to lead chemistry
– We only explain what happened.

4. Difference between experiment and
calculation/theory
• Finding interesting phenomena or problem
– How we convert from CO2 to O2? N2+H2 to NH3?
– How to synthesize a compound?
• Design a key chemical reaction.
• Calculations
• Experiments
– Analyze
• Analysis of results
• Propose new experiments
Only One Difference

5. Difference between experiment and
calculation/theory
• No difference as science
• Most important thing is curiosity!
New insights from
big data and
my sensitivity!
Unfortunately, not so many easy-to-use
big data for chemistry

6. Googling molecule
＋
Give you recommended molecules!

7. What are needed for Googling molecule?
1. Types, kinds, variety of molecules
– # of molecules are infinity; but cover important ones
2. Required properties of molecules
– Molecular structure, energy, UV excitation energy,
dipole moment
3. Getting properties of molecules by calculation?
– Accuracy of calculation, and computer resources…
4. Coding or Encoding molecule
– IUPAC nomenclature is not suitable
– Do not think about graph theory

8. Databases for lists of molecules
• PubChem: 50,000,000 molecules listed, made by NIH,
public domain, no curating (imported from catalogs,
etc), can obtain via ftp.
• ChemSpider : 28,000,000 entries, better curating, no
ftp. Restricted for redistribution, download
• Web-GDB13 : 900,000,000 entries, just generated by
combinatorics. No
• Zinc, CheMBL, DrugBank …
• CAS : 70,000,000 molecules, proprietary
• Nikkaji: 6,000,000, proprietary
We use for source of molecules

9. The PubChem

10. Ex. A molecule listed in PubChem

11. Database for molecular properties by
experiments
• We must do some experiments for obtaining
molecular properties.
– No free comprehensive database is known so far.
– Pharmaceutical companies do O(1,000,000)
experiments for high throughput screening.
• Experiments cost huge!
– Time consuming, large facilities, costs, hazardous
We do not do experiments!

12. Database for molecular properties by computer
calculation
• Golden Standard method “Density functional
theory (B3LYP functional) + 6-31g(d) basis set”
– Accuracy is quite satisfactory (1-10kcal/mol) for
biological systems, organic chemistry.
– Good implementations are available.
– Costs less (fast, just super computer, no hazardous)
– Time for calculations becomes less
• Intel Core i7 (esp. SandyBridge) is very fast.
• Still we need huge resources, though.
We calculate by computer instead!

13. What is a molecule?
No rigorous definition for a molecule
3D coordinates
Hard to understand
but regours
Easy to understand
But many coner cases
Propionaldehyde
wavefunction
Common name
IUPAC
nomencleature
Structure
Wikipediaより

14. What is a molecule?
• No rigorous definition for “what is a molecule”
• nomenclature
– 3D coordinates for nucleus
– Structural formula
– IUPAC nomenclature
– Higher abstraction or less abstraction?
• Better molecular encoding method?
– Easy to understand for human
– Easy to understand for computer as well
– Can describe most cases, and less corner cases.
– Compromise between dream and reality

15. Encoding molecule : SMILES
Encoding molecule
IUPAC nomenclature
tert-butyl N-[(2S,3S,5S)-5-[[4-[(1-benzyltetrazol-5-yl)
methoxy]phenyl]methyl]-3-hydroxy-6-[[(1S,2R)-
2-hydroxy-2,3-dihydro-1H-inden-1-yl]amino]-
6-oxo-1-phenylhexan-2-yl]carbamate
We can encode molecule
• SMILES
CN(C)CCOC12CCC(C3C1CCCC3)C4=CC=CC=C24
• InChI Made by IUPAC
InChI=1S/C20H29NO/c1-21(2)13-14-22-20-12-11
-15(16-7-3-5-9-18(16)20)17-8-4-6-10-19(17)20/
h3,5,7,9,15,17,19H,4,6,8,10-14H2,1-2H3
…
SMILES is a good encoding method for molecules

16. What is SMILES?
• Simplified Molecular Input Line Entry System
– A linear representation of molecule using ASCII.
– Conformation is also encoded
– Human readable, and also machine readable.
– Almost one-to-one mapping between a molecule and
SMILES via universal SMILES
• David Weininger at USEPA Mid-Continent Ecology Division Laboratory invented SMILES
• InChI by IUPAC
– International Chemical Identifier : open standard (non proprietary)
– NM O’Boyle invented “Universal SMILES” via InChI

17. Example by SMILES
http://en.wikipedia.org/wiki/SMILES
分子構造SMILES
Nitrogen molecule N≡N N#N
copper sulfate Cu2+ SO42- [Cu+2].[O-]S(=O)(=O)[O-]
oenanthotoxin CCC[C@@H](O)CCC=CC=C
C#CC#CC=CCO
Vitamin B1 OCCc1c(C)[n+](=cs1)Cc2cnc(C
)nc(N)2
Aflatoxin B1 O1C=C[C@H]([C@H]1O2)c3c
2cc(OC)c4c3OC(=O)C5=C4CC
C(=O)5

18. Some corner cases
Two different SMILES for Ferrocene
• C12C3C4C5C1[Fe]23451234C5C1C2C3C45
• [CH-]1C=CC=C1.[CH-]1C=CC=C1.[Fe+2]

19. Now its my turn

20. Construction of ab initio chemical
database
• Molecular information is from PubChem
• Properties are calculated from the first principle using
computer
– Many program packages are available
– DFT (B3LYP)
– 6-31G(d) basis set and geometry optimization
– Excited states calculation by TD-DFT 6-31G+(d)
– Best for organic molecules or bio molecules
• Molecular encoding : SMILES / InChI
• Huge computer resources
• Dream come true
– Google like search engine for chemistry

21. The PubChemQC Project
• http://pubchemqc.riken.jp/
• A open database for molecules
– Public domain
• Ab initio (The first principle) calculation of
molecular properties of PubChem
• 2014/1/15: 13,000 molecules
• 2014/7/29 : 155,792 molecules
• 2014/10/30 : 906,798 molecules
• 2014/12/3 : 1,137,286 molecules

22. The PubChemQC project
http://pubchemqc.riken.jp/
WIP: no search engine, just data

23. PubChemQC
http://pubchemqc.riken.jp/

24. PubChemQC
http://pubchemqc.riken.jp/

25. Related works
• Related works
– NIST Web Book
• http://webbook.nist.gov/chemistry/
• Small numbers of molecules. Comparing many methods
– Harvard Clean Energy Project
• http://cleanenergy.molecularspace.org/
• 25,000,000 (?), molecules for photo devices made by
combinatrics
– Sugimoto et al :2013CBI symposium poster
• Almost same as our database, currently not open to the
public(now??)

26. How we do?
• Generate initial 3D conformation by OpenBABEL
– SDF contains 3D conformation but we don’t use.
– OpenBABEL –h (add hydrogen) --gen3d (generation of 3d
coordinate)
• Ab initio calculation by GAMESS+firefly
– Using Gaussian can lead to a political problem(?)
– PM3 optimization
– Hartree-Fock/STO-6G geometry optimization
– Firefly+GAMESS geometry optimization in B3LYP/6-31G*
– Ten excitation energies by TDDFT/6-31G+* (no geom
optimization)

27. How we do?
• Heavily using OpenBABEL
• Extraction Molecular information
– Sort by molecular weight of PubChem compouds
– OpenBABEL
• Encoded by SMILES
– Isomeric smiles: 3D conformation retained
– OC[C@@H](O1)[C@@H](O)[C@H](O)[C@@H](O)[C@
@H](O)1
– CCC[C@@H](O)CCC=CC=CC#CC#CC=CCO
– CC(=O)OCCC(/C)=CC[C@H](C(C)=C)CCC=C

28. Our way to pubchem Compound to
quantum chemistry calculation
aflatoxin
O1C=C[C@H]([C@H]1O2)c3c2cc(OC)c4c3OC(=O)C5=C4CCC(=O)5
Ab initio calculation by
OpenBABEL

29. Final results will be
• Uploaded to http://pubchemqc.riken.jp/
• Currently we upload
– input file (ground / excited state)
– Output file (ground / excited state)
– Final geometry in Mol file

30. Scaling of computation
• Embarrassingly parallel for each molecule
• Very roughly speaking, required time for
calculation scales like N^4
– N : molecular weight
• Problems are very hard (complexity theory)
– Hartree-Fock calculation
– DFT (b3lyp) calculation
– geometry optimization
• Practically many molecules can be solved
efficiently

31. Computer Resources
• RICC : Intel Xeon 5570 Westmere, 2.93GHz 8
cores/node) x 1000
– 1000-10000 molecules/day (MW 160)
– Heavily depend on conditions of other users
– Time limit: 8 hours
• Quest : Intel Core2 duo (1.6GHz/node) x 700
– 3000-8000 molecules / day (MW 160)
– 100-1000 molecules / day (MW 200-300)
– Time limit: 20 hours
• Some compounds fail to calculate are ignored for
this time.

32. Computer Resources
• Storage
– Approx. 500GB for 1,000,000 molecules (xz
compressed)
– Approx. 20 TB for 40,000,000 molecules (xz
compressed)

33. Molecular weight and Lipinski Rule
• Lipinski’s five rule (Pfizer's rule of five): rule of
thumb for drug discovery
• No more than 5 hydrogen bond donors
• Not more than 10 hydrogen bond acceptors
• A molecular mass less than 500 daltons
• An octanol-water partition coefficient log P not greater than 5
• Molecular weight should be smaller than 500 is
very good for computational chemistry
– For routine calculations without experimental data
other than molecular formula
– If larger than 500, secondary or higher structure
becomes important. E.g., protein

34. Molecular Weight distribution at
PubChem
Lipinski limit MW=500
We are still here
30,000,000 molecules
(excluding mixtures)

35. How long it will take to finish?
• For drug design, we need to calculate all
molecules of MW < 500
• Total 30,000,000 molecules
– This number may increase in the future
• Current (2014/12/4) 1,100,000 molecules
– Only 3%
• 10,000 molecules/day -> 8.2years

36. How long it will take to finish?
• 10+ years? No, maybe far less.
• 25 years ago (1990) computers are so slow
– Even ab initio calculations are very difficult on
486DX@25MHz or
68000@10MHz

37. Outlook, prospect, hope…
• Far better in silico screening
– Less or no experiment is necessary
• Even more faster calculation using machine learning
– 10,000 molecules / second ?
– Using our data as learning set.
– Not difficult for bio or organic molecules
– Far better initial guess
• Database for chemical reaction
– Precise calculation is required
– GRRM method + machine learning (?)
• Geometry optimization for Protein (PDB)
– Only X ray crystal structures are available
http://pubchemqc.riken.jp/

38. Difficulties in this project
• Parameters needed for calculations varies by
molecules
• Properties can be different by initial guess
• Computer Resources
– Raspberry Pi? NVIDIA Jetson? Bonic?
• Molecular encoding never ends
– SMILES or InChI is not complete
– Some corner cases may be chemically interesting.