EXPLORING CHEMICAL SPACE FOR DRUG DISCOVERY Daniel Svozil Laboratory of Informatics and Chemistry University of Chemistry and Technology Prague

What is chemical space? size: 3 × stars, atoms Dokkum & Conroy, Nature, 2010, 468, 940–942 molecule-could-point-to-alien-life/

Size of chemical space mono- to 14-substitute n-hexanes … Weininger, In Encyclopedia of Computational Chemistry, 1998, Vol. 1, estimates vary wildly, commonly given … (MW<500, stable, not all synthetically available) Bohacek et al., Med. Res. Rev., 1996, 16, 3-50 CAS … 1.0 × 10 8 ZINC … 2.3 × 10 7 DrugBank … drugs all numbers as of

Why we need to explore chemical space Lipinski & Hopkins, Nature. 2004, 432, chemical space gene family administered drugs

Methods of its exploration experimental synthesis – combinatorial chemistry related biological data – high-throughput screening (HTS) computational

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

Molecular evolution systematicaly explore chemical space by clever generation of new compounds Kawai et al., J. Chem. Inf. Model. 2014, 54,

Virtual screening ‘funnel’ ~10 6 – 10 9 molecules VIRTUAL SCREENING INACTIVES HITS GENERATED DATABASE (Q)SAR Docking Pharmacophore models ~10 1 – 10 3 molecules From the presentation by A. Varnek, University of Strasbourg Filters

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

IMG library ChEMBL GR ligands Data cleaning and consolidation Molpher Pool of posible ligands ZINC Purchase compounds Experimenta l verification 297 GR ligands paths ~ morphs

ACKNOWLEDGMENT LICH group: Ctibor Škuta, Milan Voršilák, Ivan Čmelo, Martin Šícho, Jiří Novotný IMG group of chemical biology: Petr Bartůněk, David Sedlák and others