Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space.

Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacky University and University Hospital in Olomouc Hnevotinska 5 77900 Olomouc Czech Republic

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Polishchuk PG, Madzhidov TI, Varnek A. Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des. 2013;27:675–679. doi: 10.1007/s10822-013-9672-4. PubMed DOI

Schneider P, Schneider G. De novo design at the edge of chaos. J Med Chem. 2016;59:4077–4086. doi: 10.1021/acs.jmedchem.5b01849. PubMed DOI

Schneider G. Automating drug discovery. Nat Rev Drug Discovery. 2017;17:97. doi: 10.1038/nrd.2017.232. PubMed DOI

Böhm H-J. The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des. 1992;6:61–78. doi: 10.1007/bf00124387. PubMed DOI

Wang R, Gao Y, Lai L. LigBuilder: a multi-purpose program for structure-based drug design. Mol Model Annu. 2000;6:498–516. doi: 10.1007/s0089400060498. DOI

Brown N, McKay B, Gilardoni F, Gasteiger J. A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules. J Chem Inform Comput Sci. 2004;44:1079–1087. doi: 10.1021/ci034290p. PubMed DOI

Hartenfeller M, Zettl H, Walter M, Rupp M, Reisen F, Proschak E, Weggen S, Stark H, Schneider G. DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput Biol. 2012;8:e1002380. doi: 10.1371/journal.pcbi.1002380. PubMed DOI PMC

Firth NC, Atrash B, Brown N, Blagg J. MOARF, an integrated workflow for multiobjective optimization: implementation, synthesis, and biological evaluation. J Chem Inf Model. 2015;55:1169–1180. doi: 10.1021/acs.jcim.5b00073. PubMed DOI

Chéron N, Jasty N, Shakhnovich EI. OpenGrowth: an automated and rational algorithm for finding new protein ligands. J Med Chem. 2016;59:4171–4188. doi: 10.1021/acs.jmedchem.5b00886. PubMed DOI

Hoksza D, Škoda P, Voršilák M, Svozil D. Molpher: a software framework for systematic chemical space exploration. J Cheminform. 2014;6:7. doi: 10.1186/1758-2946-6-7. PubMed DOI PMC

Szymkuć S, Gajewska EP, Klucznik T, Molga K, Dittwald P, Startek M, Bajczyk M, Grzybowski BA. Computer-assisted synthetic planning: the end of the beginning. Angew Chem Int Ed. 2016;55:5904–5937. doi: 10.1002/anie.201506101. PubMed DOI

Batiste L, Unzue A, Dolbois A, Hassler F, Wang X, Deerain N, Zhu J, Spiliotopoulos D, Nevado C, Caflisch A. Chemical space expansion of bromodomain ligands guided by in silico virtual couplings (AutoCouple) ACS Cent Sci. 2018;4:180–188. doi: 10.1021/acscentsci.7b00401. PubMed DOI PMC

Merk D, Grisoni F, Friedrich L, Gelzinyte E, Schneider G. Computer-assisted discovery of retinoid X receptor modulating natural products and isofunctional mimetics. J Med Chem. 2018;61:5442–5447. doi: 10.1021/acs.jmedchem.8b00494. PubMed DOI

Kutchukian PS, Lou D, Shakhnovich EI. FOG: fragment optimized growth algorithm for the de novo generation of molecules occupying druglike chemical space. J Chem Inf Model. 2009;49:1630–1642. doi: 10.1021/ci9000458. PubMed DOI

Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. Break down in order to build up: decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model. 2017;57:627–631. doi: 10.1021/acs.jcim.6b00596. PubMed DOI PMC

Beccari AR, Cavazzoni C, Beato C, Costantino G. LiGen: a high performance workflow for chemistry driven de novo design. J Chem Inf Model. 2013;53:1518–1527. doi: 10.1021/ci400078g. PubMed DOI

Hartenfeller M, Schneider G. Enabling future drug discovery by de novo design. Wiley Interdiscip Rev. 2011;1:742–759. doi: 10.1002/wcms.49. DOI

Olivecrona M, Blaschke T, Engkvist O, Chen H. Molecular de-novo design through deep reinforcement learning. Journal of Cheminformatics. 2017;9:48. doi: 10.1186/s13321-017-0235-x. PubMed DOI PMC

Popova M, Isayev O, Tropsha A. Deep reinforcement learning for de-novo drug design. Scie Adv. 2018;4(7):eaap7885. doi: 10.1126/sciadv.aap7885. PubMed DOI PMC

Yuan W, Jiang D, Nambiar DK, Liew LP, Hay MP, Bloomstein J, Lu P, Turner B, Le Q-T, Tibshirani R, Khatri P, Moloney MG, Koong AC. Chemical space mimicry for drug discovery. J Chem Inf Model. 2017;57:875–882. doi: 10.1021/acs.jcim.6b00754. PubMed DOI PMC

Li Y, Zhang L, Liu Z. Multi-objective de novo drug design with conditional graph generative model. J Cheminform. 2018;10(1):33. doi: 10.1186/s13321-018-0287-6. PubMed DOI PMC

Polykovskiy D, Zhebrak A, Vetrov D, Ivanenkov Y, Aladinskiy V, Mamoshina P, Bozdaganyan M, Aliper A, Zhavoronkov A, Kadurin A. Entangled conditional adversarial autoencoder for de novo drug discovery. Mol Pharm. 2018;15:4398–4405. doi: 10.1021/acs.molpharmaceut.8b00839. PubMed DOI

Putin E, Asadulaev A, Ivanenkov Y, Aladinskiy V, Sanchez-Lengeling B, Aspuru-Guzik A, Zhavoronkov A. Reinforced adversarial neural computer for de novo molecular design. J Chem Inf Model. 2018 doi: 10.1021/acs.jcim.7b00690. PubMed DOI

Segler MHS, Kogej T, Tyrchan C, Waller MP. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci. 2018;4:120–131. doi: 10.1021/acscentsci.7b00512. PubMed DOI PMC

Skvortsova MI, Baskin II, Slovokhotova OL, Palyulin VA, Zefirov NS. Inverse problem in QSAR/QSPR studies for the case of topological indexes characterizing molecular shape (Kier indices) J Chem Inform Comput Sci. 1993;33:630–634. doi: 10.1021/ci00014a017. DOI

Faulon J-L, Churchwell CJ, Visco DP. The signature molecular descriptor. 2. enumerating molecules from their extended valence sequences. J Chem Inform Comput Sci. 2003;43:721–734. doi: 10.1021/ci020346o. PubMed DOI

Faulon J-L, Visco DP, Pophale RS. The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies. J Chem Inform Comput Sci. 2003;43:707–720. doi: 10.1021/ci020345w. PubMed DOI

Miyao T, Arakawa M, Funatsu K. Exhaustive structure generation for inverse-QSPR/QSAR. Mol Inform. 2010;29:111–125. doi: 10.1002/minf.200900038. PubMed DOI

Miyao T, Kaneko H, Funatsu K. Inverse QSPR/QSAR analysis for chemical structure generation (from y to x) J Chem Inf Model. 2016;56:286–299. doi: 10.1021/acs.jcim.5b00628. PubMed DOI

Miyao T, Funatsu K. Finding chemical structures corresponding to a set of coordinates in chemical descriptor space. Mol Inform. 2017;36:1700030. doi: 10.1002/minf.201700030. PubMed DOI

Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci. 2018;4:268–276. doi: 10.1021/acscentsci.7b00572. PubMed DOI PMC

Elton DC, Boukouvalas Z, Fuge MD, Chung PW. Deep learning for molecular design—a review of the state of the art. Mol Syst Des Eng. 2019;4:828–849. doi: 10.1039/C9ME00039A. DOI

Dalke A, Hert J, Kramer C. mmpdb: an Open-Source matched molecular pair platform for large multiproperty data sets. J Chem Inf Model. 2018;58:902–910. doi: 10.1021/acs.jcim.8b00173. PubMed DOI

Hussain J, Rea C. Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model. 2010;50:339–348. doi: 10.1021/ci900450m. PubMed DOI

Ertl P, Schuffenhauer A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminform. 2009;1:8. doi: 10.1186/1758-2946-1-8. PubMed DOI PMC

Coley CW, Rogers L, Green WH, Jensen KF. SCScore: synthetic complexity learned from a reaction corpus. J Chem Inf Model. 2018;58:252–261. doi: 10.1021/acs.jcim.7b00622. PubMed DOI

Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem. 2010;53:2719–2740. doi: 10.1021/jm901137j. PubMed DOI

Brown N, Fiscato M, Segler MHS, Vaucher AC. GuacaMol: benchmarking models for de novo molecular design. J Chem Inf Model. 2019 doi: 10.1021/acs.jcim.8b00839. PubMed DOI

Polykovskiy D, Zhebrak A, Sanchez-Lengeling B, Golovanov S, Tatanov O, Belyaev S, Kurbanov R, Artamonov A, Aladinskiy V, Veselov M, Kadurin A (2019) Molecular Sets (MOSES): a benchmarking platform for molecular generation models. arxiv PubMed PMC

Structure sanitization workflow (2019). https://bitbucket.imtm.cz/projects/STD/repos/std/browse

JChem 19.2.0 (2019). ChemAxon http://www.chemaxon.com

RDKit: Open-Source Cheminformatics Software 2017.09 (2017). http://rdkit.org/

Schomburg K, Ehrlich H-C, Stierand K, Rarey M. From structure diagrams to visual chemical patterns. J Chem Inf Model. 2010;50:1529–1535. doi: 10.1021/ci100209a. PubMed DOI

Lovering F, Bikker J, Humblet C. Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem. 2009;52:6752–6756. doi: 10.1021/jm901241e. PubMed DOI

Yang Y, Chen H, Nilsson I, Muresan S, Engkvist O. Investigation of the relationship between topology and selectivity for druglike molecules. J Med Chem. 2010;53:7709–7714. doi: 10.1021/jm1008456. PubMed DOI

Benchmarks for interpretation of QSAR models