Ring system-based chemical graph generation for de novo molecular design
- 377 Downloads
Generating chemical graphs in silico by combining building blocks is important and fundamental in virtual combinatorial chemistry. A premise in this area is that generated structures should be irredundant as well as exhaustive. In this study, we develop structure generation algorithms regarding combining ring systems as well as atom fragments. The proposed algorithms consist of three parts. First, chemical structures are generated through a canonical construction path. During structure generation, ring systems can be treated as reduced graphs having fewer vertices than those in the original ones. Second, diversified structures are generated by a simple rule-based generation algorithm. Third, the number of structures to be generated can be estimated with adequate accuracy without actual exhaustive generation. The proposed algorithms were implemented in structure generator Molgilla. As a practical application, Molgilla generated chemical structures mimicking rosiglitazone in terms of a two dimensional pharmacophore pattern. The strength of the algorithms lies in simplicity and flexibility. Therefore, they may be applied to various computer programs regarding structure generation by combining building blocks.
KeywordsRing systems Structure generator Inverse QSPR/QSAR De novo design
The authors are grateful to G. Schneider and D. Reker at the Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zurich. G. Schneider supported the authors by giving valuable advice for the improvement of our structure generation algorithms, particularly the descriptor calculation and how to generate feasible structures in a chemistry point of view. D. Reker and the authors have discussed how to develop diversity-oriented generation algorithms. The authors also acknowledge the support of the Core Research for Evolutionary Science and Technology (CREST) Project ‘Development of a knowledge-generating platform driven by big data in drug discovery through production processes’ of the Japan Science and Technology Agency (JST). T.M. is a JSPS Research Fellow.
- 4.Gugisch R, Kerber A, Laue R, Meringer M, Weidinger J (2000) MOLGEN-COMB, a software package for combinatorial chemistry. MATCH 41:189–203Google Scholar
- 11.Grüner T, Laue R, Meringer M (1997) Algorithms for group actions: homomorphism principle and orderly generation applied to graphs. In: DIMACS Series in Discrete Mathematics and Theoretical Computer Science; American Mathematical Society, vol 28, pp 113–122Google Scholar
- 19.Rella M (2011) Software review of FTrees and FTrees-FS in pipeline pilot FTrees and FTrees-FS in pipeline pilot. BioSolveIT GmbH. An Der Zieglei 79, 53757 Sankt Augustin, Germany. http://www.biosolveit.de/FTrees. See Web Site for Pricing Information. J Am Chem Soc, vol 133, pp 17101–17102
- 26.Jaworska J, Nikolova-Jeliazkova N, Aldenberg T (2005) QSAR applicability domain estimation by projection of the training set descriptor space: a review. ATLA 33:445–459Google Scholar
- 27.Miyao T, Kaneko H, Funatsu K (2014) Ring-system-based exhaustive structure generation for inverse-QSPR/QSAR. Mol Inform 33:764–778Google Scholar
- 32.McKay BD, Royle G F (1985) Constructing the cubic graphs on up to 20 vertices. Department of Mathematics, University of Western AustraliaGoogle Scholar
- 33.Fink T, Reymond JL (2007) Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discove. J Chem Inf Model 47:342–353CrossRefGoogle Scholar
- 40.Landrum G RDKit (2016) Open-source cheminformatics http://www.rdkit.org. Accessed 12 Mar 2016
- 41.Berthold MR, Cebron N, Dill F, Gabriel TR, Koetter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2008) KNIME: the Konstanz information miner. In: Preisach C, Burkhardt H, Schmidt-Thieme L, Decker R (eds) Data analysis, machine learning and applications. Springer, Berlin, pp 319–326CrossRefGoogle Scholar
- 44.Chemish: Chemometorics Software (2016) http://www.cheminfonavi.co.jp/chemish. Accessed 12 Mar 2016