Treffer: SLICE (SMARTS and Logic In ChEmistry): fast generation of molecules using advanced chemical synthesis logic and modern coding style.
Title:
SLICE (SMARTS and Logic In ChEmistry): fast generation of molecules using advanced chemical synthesis logic and modern coding style.
Authors:
Ilemo SN; Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, P.O. Box B, Frederick, Maryland, 21702, USA., Delannée V; Laboratory of Chemical Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland, 21702, USA.; Deep Origin, San Diego, CA, USA., Grushin O; Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland, 21702, USA., Judson P; Heather Lea, Bland Hill, Norwood, Harrogate, HG3 1TE, England., Patel H; Laboratory of Chemical Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland, 21702, USA.; OpenEye, Cadence Molecular Sciences, Santa Fe, NM, USA., Nicklaus MC; Laboratory of Chemical Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland, 21702, USA., Tarasova NI; Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, P.O. Box B, Frederick, Maryland, 21702, USA. nadya.tarasova@nih.gov.
Source:
Journal of cheminformatics [J Cheminform] 2025 Dec 09. Date of Electronic Publication: 2025 Dec 09.
Publication Model:
Ahead of Print
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 101516718 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1758-2946 (Print) Linking ISSN: 17582946 NLM ISO Abbreviation: J Cheminform
Imprint Name(s):
Publication: [London, United Kingdom] : BioMed Central
Original Publication: [London] : Chemistry Central Ltd. in association with BioMed Central, 2009-
Original Publication: [London] : Chemistry Central Ltd. in association with BioMed Central, 2009-
References:
Hall BW, Tummino TA, Tang K, Mailhot O, Castanon M, Irwin JJ et al (2025) A database for large-scale docking and experimental results. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.5c00394.
Luttens A, Cabeza de Vaca I, Sparring L, Brea J, Martinez AL, Kahlous NA et al (2025) Rapid traversal of vast chemical space using machine learning-guided docking screens. Nat Comput Sci 5(4):301–312. https://doi.org/10.1038/s43588-025-00777-x.
Nada H, Meanwell NA, Gabr MT (2025) Virtual screening: hope, hype, and the fine line in between. Expert Opin Drug Discov 20(2):145–162. https://doi.org/10.1080/17460441.2025.2458666.
Sadybekov AV, Katritch V (2023) Computational approaches streamlining drug discovery. Nature 616(7958):673–685. https://doi.org/10.1038/s41586-023-05905-z.
Warr WA, Nicklaus MC, Nicolaou CA, Rarey M (2022) Exploration of ultralarge compound collections for drug discovery. J Chem Inf Model 62(9):2021–2034. https://doi.org/10.1021/acs.jcim.2c00224.
Bedart C, Simoben CV, Schapira M (2024) Emerging structure-based computational methods to screen the exploding accessible chemical space. Curr Opin Struct Biol 86:102812. https://doi.org/10.1016/j.sbi.2024.102812.
Gentile F, Yaacoub JC, Gleave J, Fernandez M, Ton AT, Ban F et al (2022) Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking. Nat Protoc 17(3):672–697. https://doi.org/10.1038/s41596-021-00659-2.
Zhou G, DiMaio F (2025) Protein ligand structure prediction: from empirical to deep learning approaches. Curr Opin Struct Biol 91:102998. https://doi.org/10.1016/j.sbi.2025.102998.
Zhou G, Rusnac DV, Park H, Canzani D, Nguyen HM, Stewart L et al (2024) An artificial intelligence accelerated virtual screening platform for drug discovery. Nat Commun 15(1):7761. https://doi.org/10.1038/s41467-024-52061-7.
Smaldone AM, Shee Y, Kyro GW, Xu C, Vu NP, Dutta R et al (2025) Quantum machine learning in drug discovery: applications in academia and pharmaceutical industries. Chem Rev 125(12):5436–5460. https://doi.org/10.1021/acs.chemrev.4c00678.
Polishchuk PG, Madzhidov TI, Varnek A (2013) Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des 27(8):675–679. https://doi.org/10.1007/s10822-013-9672-4.
Liu F, Mailhot O, Glenn IS, Vigneron SF, Bassim V, Xu X et al (2025) The impact of library size and scale of testing on virtual screening. Nat Chem Biol. https://doi.org/10.1038/s41589-024-01797-w.
Luttens A, Vo DD, Scaletti ER, Wiita E, Almlof I, Wallner O et al (2025) Virtual fragment screening for DNA repair inhibitors in vast chemical space. Nat Commun 16(1):1741. https://doi.org/10.1038/s41467-025-56893-9.
Sadybekov AA, Sadybekov AV, Liu Y, Iliopoulos-Tsoutsouvas C, Huang X-P, Pickett J et al (2022) Synthon-based ligand discovery in virtual libraries of over 11 billion compounds. Nature 601(7893):452–459. https://doi.org/10.1038/s41586-021-04220-9.
Korn M, Judson P, Klein R, Lemmen C, Nicklaus MC, Rarey M (2025) Savi space-combinatorial encoding of the billion-size synthetically accessible virtual inventory. Sci Data 12(1):1064. https://doi.org/10.1038/s41597-025-05384-z.
Kim H, Ryu S, Jung N, Yang J, Seok C (2024) Csearch: chemical space search via virtual synthesis and global optimization. J Cheminform 16(1):137. https://doi.org/10.1186/s13321-024-00936-8.
Corey EJ, Wipke WT (1969) Computer-assisted design of complex organic syntheses. Science 166(3902):178–192. https://doi.org/10.1126/science.166.3902.178.
Judson PN, Ihlenfeldt WD, Patel H, Delannee V, Tarasova N, Nicklaus MC (2020) Adapting CHMTRN (CHeMistry TRaNslator) for a new use. J Chem Inf Model 60(7):3336–3341. https://doi.org/10.1021/acs.jcim.0c00448.
Patel H, Ihlenfeldt WD, Judson PN, Moroz YS, Pevzner Y, Peach ML et al (2020) SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules. Sci Data 7(1):384. https://doi.org/10.1038/s41597-020-00727-4.
RDKit: Open-Source Cheminformatics Software. https://www.rdkit.org/.
OpenEye Generative Chemistry for Libraries Enumeration. https://www.eyesopen.com/generative-chemistry.
KNIME: Chemical library enumeration. https://hub.knime.com/knime/spaces/Examples/50_Applications/_01_Chemical_Library_Enumeration/_01_ChemicalLibraryEnumeration~2rM_ffTdX3uIuJ_E/current-state.
Indigo Toolkit. https://lifescience.opensource.epam.com/indigo/.
Chemaxon Reactor. https://chemaxon.com/reactor.
Arthor: high-performance chemical database searching. https://www.nextmovesoftware.com/arthor.html.
Yosef T. SMARTS - A language for describing molecular patterns. https://www.daylight.com/dayhtml/doc/theory/theory.smarts.html.
JSDraw: a javascript framework for cheminformatics and bioinformatics for developers. https://scilligence.com/jsdraw/.
Jiang C, Jin X, Dong Y, Chen M (2016) Kekule.js: an open source JavaScript chemoinformatics toolkit. J Chem Inf Model 56(6):1132–1138. https://doi.org/10.1021/acs.jcim.6b00167.
Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N et al (2017) The chemistry development kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminform 9(1):33. https://doi.org/10.1186/s13321-017-0220-4.
Bonilla PA, Hoop CL, Stefanisko K, Tarasov SG, Sinha S, Nicklaus MC et al (2023) Virtual screening of ultra-large chemical libraries identifies cell-permeable small-molecule inhibitors of a “non-druggable” target, STAT3 N-terminal domain. Front Oncol 13:1144153. https://doi.org/10.3389/fonc.2023.1144153.
Eugene Raush MT, Hoop C, Knowlan K, Bianchi R, Tarasova N (2025) Novel GPU engines for virtual screening of giga-sized libraries identify inhibitors of challenging targets. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.5c01335.
Luttens A, Cabeza de Vaca I, Sparring L, Brea J, Martinez AL, Kahlous NA et al (2025) Rapid traversal of vast chemical space using machine learning-guided docking screens. Nat Comput Sci 5(4):301–312. https://doi.org/10.1038/s43588-025-00777-x.
Nada H, Meanwell NA, Gabr MT (2025) Virtual screening: hope, hype, and the fine line in between. Expert Opin Drug Discov 20(2):145–162. https://doi.org/10.1080/17460441.2025.2458666.
Sadybekov AV, Katritch V (2023) Computational approaches streamlining drug discovery. Nature 616(7958):673–685. https://doi.org/10.1038/s41586-023-05905-z.
Warr WA, Nicklaus MC, Nicolaou CA, Rarey M (2022) Exploration of ultralarge compound collections for drug discovery. J Chem Inf Model 62(9):2021–2034. https://doi.org/10.1021/acs.jcim.2c00224.
Bedart C, Simoben CV, Schapira M (2024) Emerging structure-based computational methods to screen the exploding accessible chemical space. Curr Opin Struct Biol 86:102812. https://doi.org/10.1016/j.sbi.2024.102812.
Gentile F, Yaacoub JC, Gleave J, Fernandez M, Ton AT, Ban F et al (2022) Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking. Nat Protoc 17(3):672–697. https://doi.org/10.1038/s41596-021-00659-2.
Zhou G, DiMaio F (2025) Protein ligand structure prediction: from empirical to deep learning approaches. Curr Opin Struct Biol 91:102998. https://doi.org/10.1016/j.sbi.2025.102998.
Zhou G, Rusnac DV, Park H, Canzani D, Nguyen HM, Stewart L et al (2024) An artificial intelligence accelerated virtual screening platform for drug discovery. Nat Commun 15(1):7761. https://doi.org/10.1038/s41467-024-52061-7.
Smaldone AM, Shee Y, Kyro GW, Xu C, Vu NP, Dutta R et al (2025) Quantum machine learning in drug discovery: applications in academia and pharmaceutical industries. Chem Rev 125(12):5436–5460. https://doi.org/10.1021/acs.chemrev.4c00678.
Polishchuk PG, Madzhidov TI, Varnek A (2013) Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des 27(8):675–679. https://doi.org/10.1007/s10822-013-9672-4.
Liu F, Mailhot O, Glenn IS, Vigneron SF, Bassim V, Xu X et al (2025) The impact of library size and scale of testing on virtual screening. Nat Chem Biol. https://doi.org/10.1038/s41589-024-01797-w.
Luttens A, Vo DD, Scaletti ER, Wiita E, Almlof I, Wallner O et al (2025) Virtual fragment screening for DNA repair inhibitors in vast chemical space. Nat Commun 16(1):1741. https://doi.org/10.1038/s41467-025-56893-9.
Sadybekov AA, Sadybekov AV, Liu Y, Iliopoulos-Tsoutsouvas C, Huang X-P, Pickett J et al (2022) Synthon-based ligand discovery in virtual libraries of over 11 billion compounds. Nature 601(7893):452–459. https://doi.org/10.1038/s41586-021-04220-9.
Korn M, Judson P, Klein R, Lemmen C, Nicklaus MC, Rarey M (2025) Savi space-combinatorial encoding of the billion-size synthetically accessible virtual inventory. Sci Data 12(1):1064. https://doi.org/10.1038/s41597-025-05384-z.
Kim H, Ryu S, Jung N, Yang J, Seok C (2024) Csearch: chemical space search via virtual synthesis and global optimization. J Cheminform 16(1):137. https://doi.org/10.1186/s13321-024-00936-8.
Corey EJ, Wipke WT (1969) Computer-assisted design of complex organic syntheses. Science 166(3902):178–192. https://doi.org/10.1126/science.166.3902.178.
Judson PN, Ihlenfeldt WD, Patel H, Delannee V, Tarasova N, Nicklaus MC (2020) Adapting CHMTRN (CHeMistry TRaNslator) for a new use. J Chem Inf Model 60(7):3336–3341. https://doi.org/10.1021/acs.jcim.0c00448.
Patel H, Ihlenfeldt WD, Judson PN, Moroz YS, Pevzner Y, Peach ML et al (2020) SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules. Sci Data 7(1):384. https://doi.org/10.1038/s41597-020-00727-4.
RDKit: Open-Source Cheminformatics Software. https://www.rdkit.org/.
OpenEye Generative Chemistry for Libraries Enumeration. https://www.eyesopen.com/generative-chemistry.
KNIME: Chemical library enumeration. https://hub.knime.com/knime/spaces/Examples/50_Applications/_01_Chemical_Library_Enumeration/_01_ChemicalLibraryEnumeration~2rM_ffTdX3uIuJ_E/current-state.
Indigo Toolkit. https://lifescience.opensource.epam.com/indigo/.
Chemaxon Reactor. https://chemaxon.com/reactor.
Arthor: high-performance chemical database searching. https://www.nextmovesoftware.com/arthor.html.
Yosef T. SMARTS - A language for describing molecular patterns. https://www.daylight.com/dayhtml/doc/theory/theory.smarts.html.
JSDraw: a javascript framework for cheminformatics and bioinformatics for developers. https://scilligence.com/jsdraw/.
Jiang C, Jin X, Dong Y, Chen M (2016) Kekule.js: an open source JavaScript chemoinformatics toolkit. J Chem Inf Model 56(6):1132–1138. https://doi.org/10.1021/acs.jcim.6b00167.
Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N et al (2017) The chemistry development kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminform 9(1):33. https://doi.org/10.1186/s13321-017-0220-4.
Bonilla PA, Hoop CL, Stefanisko K, Tarasov SG, Sinha S, Nicklaus MC et al (2023) Virtual screening of ultra-large chemical libraries identifies cell-permeable small-molecule inhibitors of a “non-druggable” target, STAT3 N-terminal domain. Front Oncol 13:1144153. https://doi.org/10.3389/fonc.2023.1144153.
Eugene Raush MT, Hoop C, Knowlan K, Bianchi R, Tarasova N (2025) Novel GPU engines for virtual screening of giga-sized libraries identify inhibitors of challenging targets. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.5c01335.
Grant Information:
Contract No. 75N91024F00011 Federal funds from the National Cancer Institute, National Institutes of Health
Contributed Indexing:
Keywords: Chemical spaces; Structure-based drug discovery; Virtual libraries; Virtual ligand screening
Entry Date(s):
Date Created: 20251209 Latest Revision: 20251209
Update Code:
20251210
DOI:
10.1186/s13321-025-01119-9
PMID:
41366467
Database:
MEDLINE
Weitere Informationen
While virtual libraries of synthetically accessible compounds have exploded in size to many billions, our capacity to extract valuable drug leads from these vast databases remains limited by computational resources. To overcome this, we developed SLICE SMARTS and Logic In ChEmistry), a powerful new tool designed for the agile exploration of massive chemical spaces. SLICE enables the fast, "à la carte" generation of virtual compound libraries through chemist-defined reaction chemistries and readily available building blocks. Its user-friendly, no-code graphical interface, the SLICE Designer, allows chemists to easily define SMARTS patterns, configure atom and bond properties, and establish chemical constraints and logic. The resulting XML files are then fed into the SLICE Engine, which generates diverse virtual libraries from specified building blocks at speeds of 0.6-2.5 million compounds per hour. SLICE provides the agility and performance needed to support efficient lead generation within discovery workflows.
(© 2025. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)
Declarations. Ethics approval and consent to participate: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Competing interests: The authors declare no competing interests. Availability and requirements: Project name: SLICE Engine. Project home page: https://github.com/tarasovan/SLICE-public/tree/main/slice-engine. Operating system: Platform-independent. Programming language: Java. Other requirements: No other requirements are needed beyond a Java Runtime Environment (JRE 8 or higher). Dependencies: Apache Commons (CLI/IO/Math/Lang), Guava, BEAM, JNI-InChI, JAMA, Vecmath, ANTLR 4, univocity-parsers, jopt-simple, Gson. License: MIT license. Any restriction to use by non-academics: none.