Transcript Exploring chemical space for drug discovery

• http://ich.vscht.cz/~svozil/teaching.html
EXPLORING CHEMICAL
SPACE FOR DRUG
DISCOVERY
Daniel Svozil
[email protected]
Laboratory of Informatics and Chemistry
University of Chemistry and Technology Prague
What is chemical space?
size: 3 × 1023 stars, 1080 atoms
Dokkum & Conroy, Nature, 2010, 468, 940–942
http://www.universetoday.com/21528/sweet-galacticmolecule-could-point-to-alien-life/
Size of chemical space
• mono- to 14-substitute n-hexanes … 1029
Weininger, In Encyclopedia of Computational Chemistry, 1998, Vol. 1, 425-530
• estimates vary wildly, commonly given … 1060 (MW<500,
stable, not all synthetically available)
Bohacek et al., Med. Res. Rev., 1996, 16, 3-50
• CAS … 1.0 × 108
• ZINC … 2.3 × 107
• DrugBank … 7 759 drugs
all numbers as of 1. 9. 2015
Why we need to explore chemical space
administered drugs
chemical space
gene family
Lipinski & Hopkins, Nature. 2004, 432, 855-61
Methods of its exploration
• experimental
• synthesis – combinatorial chemistry
• related biological data – high-throughput screening (HTS)
• computational
http://www.cam-com.com/images/comchem.jpg
http://chemgen.cz/
Computational exploration of chemical space
This is basically de novo design. It means that new
chemotypes with desired effects are proposed.
Two major approaches
1. Exhaustive enumeration
2. Molecular evolution
Once you have a virtual library generated, you can apply
any of possible virtual screening methods to prioritize your
compounds.
Exhaustive enumeration
• prof. Reymond, Bern
• GDB databases, all molecules that can exist up to a
certain number of heavy atoms
• GDB-11 (2.64 × 107 compounds with C, N, O, F)
• GDB-13 (9.7 × 108 compounds with C, N, O, S, Cl)
• GDB-17 (1.7 × 1011 compounds with C, N, O, S, halogens)
Molecular evolution
• systematicaly explore chemical space by clever generation of new
compounds
Kawai et al., J. Chem. Inf. Model. 2014, 54, 49-56.
Virtual screening ‘funnel’
Filters
Pharmacophore models
GENERATED DATABASE
(Q)SAR
Docking
VIRTUAL
SCREENING
HITS
–
molecules
~106
109
From the presentation by A. Varnek, University of Strasbourg
~101 – 103
molecules
INACTIVES
Molpher
• Molecular morphing
Svozil et al., J. Cheminform., 2014 , 6, 7.
Morphing between two molecules
Svozil et al., J. Cheminform., 2014 , 6, 7.
Morphing operators
Svozil et al., J. Cheminform., 2014 , 6, 7.
Molpher – what is it good for?
• Idea: start and target molecules are active against the
same target
• In a systematic way, generate chemical subspace
between a start/target pair
• Explore this subspace for molecules with high potency
GR ligands
IMG
library
297 GR ligands
Data cleaning and
consolidation
87 912 paths
~ 2 000 000 morphs
Molpher
ChEMBL
Experimenta
l verification
Pool of posible
ligands
Purchase
compounds
ZINC
ACKNOWLEDGMENT
LICH group: Ctibor Škuta, Milan Voršilák, Ivan Čmelo,
Martin Šícho, Jiří Novotný
IMG group of chemical biology: Petr Bartůněk, David
Sedlák and others