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CHEMriya is a virtual combinatorial chemical space for make-on-demand small molecules[1], comprising approximately 12 billion tangible compounds for research and drug discovery[2]. This innovative chemical space is a result of a collaborative effort between Otava and BioSolveIT[3]. The name "CHEMriya" is composed of words ‘chemistry’ and ‘mriya' (Ukrainian: мрія, lit. 'dream' or 'inspiration'); also pays homage to the Antonov An-225 Mriya, the world's largest cargo aircraft, reflecting the vast and expansive nature of this chemical space.
Overview edit
The first iteration of CHEMriya was introduced in 2021. It was constructed using a foundation of approximately 30,000 chemical building blocks and involved around 45 chemical reactions[1] to systematically combine two to four building blocks into complete drug-like (obeying Lipinski's rule of five) as well as beyond rule of five[4] molecules.[3][5] As of 2023, CHEMriya is composed of 35,000 chemical building blocks and 370 in-house reactions.[1]
CHEMriya is available for structural exploration through the infiniSee platform, offering scientists an access to its extensive variety of chemicals.
Significance edit
CHEMriya represents one of the on-demand ultra-large combinatorial spaces that have emerged in recent decades[3][2]. With the advancement of machine learning and data science approaches, coupled with immense growth of computer power[6][7], these ultra-large combinatorial spaces have become a focal point in early-phase drug discovery.[8][9]
Characteristics and Lipinski's rule of five edit
Comparative analysis of CHEMriya and other existing chemical spaces such as eXplore, REAL, and GalaXi, have shown that they exhibit minimal overlap [1][10]. Several studies have explored the distribution of Lipinski's rule of five properties in various chemical spaces, including CHEMriya. For a comprehensive analysis of Lipinski's rule of five property distributions, please refer to sources such as Bellmann et al.[1] and the accompanying references.[5]
References edit
- ^ a b c d Bellmann, L.; Penner, P.; Gastreich, M.; Rarey, M. (2022). "Comparison of Combinatorial Fragment Spaces and Its Application to Ultralarge Make-on-Demand Compound Catalogs". J. Chem. Inf. Model. 62 (3): 553–566. doi:10.1021/acs.jcim.1c01378. PMID 35050621. S2CID 246155272.
- ^ a b Gorgulla, C. (2022). "Recent Developments in Structure-Based Virtual Screening Approaches". p. 22. arXiv:2211.03208 [q-bio.BM].
- ^ a b c Perebyinis, M.; Rognan, D. (2023). "Overlap of On-demand Ultra-large Combinatorial Spaces with On-the-shelf Drug-like Libraries". Mol. Inf. 42 (1): 2200163. doi:10.1002/minf.202200163. PMID 36072995. S2CID 252121296.
- ^ Egbert, M.; Whitty, A.; Keserű, G. M.; Vajda, S. (2019). "Why Some Targets Benefit from beyond Rule of Five Drugs". J. Med. Chem. 62 (22): 10005–10025. doi:10.1021/acs.jmedchem.8b01732. PMC 7102492. PMID 31188592.
- ^ a b Bellmann, L.; Klein, R.; Rarey, M. (2022). "Calculating and Optimizing Physicochemical Property Distributions of Large Combinatorial Fragment Spaces". J. Chem. Inf. Model. 62 (11): 2800–2810. doi:10.1021/acs.jcim.2c00334. PMID 35653228. S2CID 249277388.
- ^ Schmidt, R.; Klein, R.; Rarey, M. (2021). "Comparison of Combinatorial Fragment Spaces and Its Application to Ultralarge Make-on-Demand Compound Catalogs". J. Chem. Inf. Model. 62 (9): 2133–2150. doi:10.1021/acs.jcim.1c00640. PMID 34478299. S2CID 237410648.
- ^ Sadybekov, A.V.; Katritch, V. (2023). "Computational approaches streamlining drug discovery". Nature. 616 (7958): 673–685. Bibcode:2023Natur.616..673S. doi:10.1038/s41586-023-05905-z. PMID 37100941.
- ^ Warr, W. A.; Nicklaus, M. C.; Nicolaou, C. A.; Rarey, M. (2022). "Exploration of Ultralarge Compound Collections for Drug Discovery". J. Chem. Inf. Model. 62 (9): 2021–2034. doi:10.1021/acs.jcim.2c00224. PMID 35421301. S2CID 248181161.
- ^ Korn, M.; Ehrt, C.; Ruggiu, F.; Gastreich, M.; Rarey, M. (2023). "Navigating large chemical spaces in early-phase drug discovery". Curr. Opin. Struct. Biol. 80: 102578. doi:10.1016/j.sbi.2023.102578. PMID 37019067.
- ^ Neumann, A.; Marrison, L.; Klein, R. (2023). "Relevance of the Trillion-Sized Chemical Space "eXplore" as a Source for Drug Discovery". ACS Med. Chem. Lett. 14 (4): 466–472. doi:10.1021/acsmedchemlett.3c00021. PMC 10108389. PMID 37077402. S2CID 257598609.
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