Approaches for enhancing the analysis of chemical space for drug discovery

References

• Ruddigkeit L, Blum LC, Reymond J-L. Visualization and virtual screening of the chemical universe database GDB-17. J Chem Inf Model. 2013;53:56–65.
   PubMed Web of Science ®Google Scholar
• Varnek A, Baskin II. Chemoinformatics as a theoretical chemistry discipline. Mol Inform. 2011;30:20–32.
   PubMed Web of Science ®Google Scholar
• Chuang KV, Gunsalus LM, Keiser MJ. Learning molecular representations for medicinal chemistry. J Med Chem. 2020;63:8705–8722.
   PubMed Web of Science ®Google Scholar
• Grygorenko OO, Volochnyuk DM, Ryabukhin SV, et al. The symbiotic relationship between drug discovery and organic chemistry. Chemistry. 2020;26:1196–1237.
   PubMed Web of Science ®Google Scholar
• Naveja JJ, Rico-Hidalgo MP, Medina-Franco JL. Analysis of a large food chemical database: chemical space, diversity, and complexity. F1000Res. 2018. DOI:https://doi.org/10.12688/f1000research.15440.2.
   Google Scholar
• Pollice R, Dos Passos Gomes G, Aldeghi M, et al. Data-driven strategies for accelerated materials design. Acc Chem Res. 2021;54:849–860.
   PubMed Web of Science ®Google Scholar
• Meggers E. Exploring biologically relevant chemical space with metal complexes. Curr Opin Chem Biol. 2007;11:287–292.
   PubMed Web of Science ®Google Scholar
• Lipinski C, Hopkins A. Navigating chemical space for biology and medicine. Nature. 2004;432:855–861.
   PubMed Web of Science ®Google Scholar
• Paolini GV, Shapland RHB, van Hoorn WP, et al. Global mapping of pharmacological space. Nat Biotechnol. 2006;24:805–815.
   PubMed Web of Science ®Google Scholar
• Medina-Franco JL, Sánchez-Cruz N, López-López E, et al. Progress on open chemoinformatic tools for expanding and exploring the chemical space. J Comput Aided Mol Des. 2022. doi:https://doi.org/10.1007/s10822-021-00399-1.
   Google Scholar
• Walters WP. Virtual chemical libraries. J Med Chem. 2019;62:1116–1124.
   PubMed Web of Science ®Google Scholar
• Hoffmann T, Gastreich M. The next level in chemical space navigation: going far beyond enumerable compound libraries. Drug Discov Today. 2019;24:1148–1156.
   PubMed Web of Science ®Google Scholar
• Miljković F, Rodríguez-Pérez R, Bajorath J. Impact of artificial intelligence on compound discovery, design, and synthesis. ACS Omega. 2021;49:33293–33299.
   Google Scholar
• Arús-Pous J, Awale M, Probst D, et al. Exploring chemical space with machine learning. Chimia (Aarau). 2019;73:1018–1023.
   PubMedGoogle Scholar
• Schneider G. Analysis of chemical space. Austin, Texas, USA: Landes Bioscience; 2013.
   Google Scholar
• Osolodkin DI, Radchenko EV, Orlov AA, et al. Progress in visual representations of chemical space. Expert Opin Drug Discov. 2015;10:959–973.
   PubMed Web of Science ®Google Scholar
• Cumming JG, Davis AM, Muresan S, et al. Chemical predictive modelling to improve compound quality. Nat Rev Drug Discov. 2013;12:948–962.
   PubMed Web of Science ®Google Scholar
• Durant JL, Leland BA, Henry DR, et al. Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci. 2002;42:1273–1280.
   PubMedGoogle Scholar
• Rogers D, Hahn M. Extended-connectivity fingerprints. J Chem Inf Model. 2010;50:742–754.
   PubMed Web of Science ®Google Scholar
• Capecchi A, Probst D, Reymond J-L. One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. J Cheminform. 2020;12:43.
   PubMed Web of Science ®Google Scholar
• Pearlman RS, Smith KM. Novel software tools for chemical diversity. 3D QSAR in drug design. Dordrecht: Kluwer Academic Publishers; 2005. p. 339–353.
   Google Scholar
• Maggiora GM, Bajorath J. Chemical space networks: a powerful new paradigm for the description of chemical space. J Comput Aided Mol Des. 2014;28:795–802.
   PubMed Web of Science ®Google Scholar
• Vogt M, Stumpfe D, Maggiora GM, et al. Lessons learned from the design of chemical space networks and opportunities for new applications. J Comput Aided Mol Des. 2016;30:191–208.
   PubMed Web of Science ®Google Scholar
• van der Maaten L. Visualizing data using t-SNE. J Mach Learn Res. 2008;1:1–48.
   Google Scholar
• Digles D, Ecker GF. Self-organizing maps for in silico screening and data visualization. Mol Inform. 2011;30:838–846.
   PubMed Web of Science ®Google Scholar
• Kragh H. Contemporary history of cosmology and the controversy over the multiverse. Ann Sci. 2009;66:529–551.
   Web of Science ®Google Scholar
• Oprea TI, Gottfries J. Chemography: the art of navigating in chemical space. J Comb Chem. 2001;3:157–166.
   PubMed Web of Science ®Google Scholar
• Larsson J, Gottfries J, Muresan S, et al. ChemGPS-NP: tuned for navigation in biologically relevant chemical space. J Nat Prod. 2007;70:789–794.
   PubMed Web of Science ®Google Scholar
• McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv [stat.ML]. 2018 [Cited 2022 Jan 20]. Available from: http://arxiv.org/abs/1802.03426
   Google Scholar
• Kohonen T. Exploration of very large databases by self-organizing maps. Proceedings of International Conference on Neural Networks (ICNN’97). 12 June 1997. Houston, Texas. USA; 1997. vol.1. p. PL1PL6.PL1PL6.
   Google Scholar
• Reker D, Rodrigues T, Schneider P, et al. Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proc Natl Acad Sci U S A. 2014;111:4067–4072.
   PubMed Web of Science ®Google Scholar
• Schneider P, Schneider G. De-orphaning the marine natural product (±)-marinopyrrole A by computational target prediction and biochemical validation. Chem Commun. 2017;53:2272–2274.
   PubMedGoogle Scholar
• Bishop CM, Svensén M, Williams CKI. GTM: the generative topographic mapping. Neural Comput. 1998;10:215–234.
   Web of Science ®Google Scholar
• Kireeva N, Baskin II, Gaspar HA, et al. Generative topographic mapping (GTM): universal tool for data visualization, structure-activity modeling and dataset comparison. Mol Inform. 2012;31:301–312.
   PubMed Web of Science ®Google Scholar
• Probst D, Reymond J-L. Visualization of very large high-dimensional data sets as minimum spanning trees. J Cheminform. 2020;12:12.
   PubMedGoogle Scholar
• Schuffenhauer A, Ertl P, Roggo S, et al. The scaffold tree–visualization of the scaffold universe by hierarchical scaffold classification. J Chem Inf Model. 2007;47:47–58.
   PubMed Web of Science ®Google Scholar
• Hu Y, Stumpfe D, Bajorath J. Lessons learned from molecular scaffold analysis. J Chem Inf Model. 2011;51:1742–1753.
   PubMed Web of Science ®Google Scholar
• Naveja JJ, Medina-Franco JL. Finding constellations in chemical space through core analysis. Front Chem. 2019;7:510.
   PubMed Web of Science ®Google Scholar
• López-Vallejo F, Giulianotti MA, Houghten RA, et al. Expanding the medicinally relevant chemical space with compound libraries. Drug Discov Today. 2012;17:718–726.
   PubMed Web of Science ®Google Scholar
• Jiménez-Luna J, Grisoni F, Weskamp N, et al. Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opin Drug Discov. 2021;16:949–959.
   PubMed Web of Science ®Google Scholar
• Saldívar-González FI, Pilón-Jiménez BA, Medina-Franco JL. Chemical space of naturally occurring compounds. Phys Sci Rev. 2018;1:525.
   Google Scholar
• Ertl P, Schuffenhauer A. Cheminformatics analysis of natural products: lessons from nature inspiring the design of new drugs. Prog Drug Res. 2008;66(217):219–235.
   Google Scholar
• Rodrigues T. Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point. Org Biomol Chem. 2017;15:9275–9282.
   PubMedGoogle Scholar
• Newman DJ, Cragg GM. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. J Nat Prod. 2020;83:770–803.
   PubMed Web of Science ®Google Scholar
• Atanasov AG, Zotchev SB, Dirsch VM, et al. Natural products in drug discovery: advances and opportunities. Nat Rev Drug Discov. 2021;20:200–216.
   PubMed Web of Science ®Google Scholar
• Bayer S, Mayer AI, Borgonovo G, et al. Chemoinformatics view on bitter taste receptor agonists in food. J Agric Food Chem. 2021;69:13916–13924.
   PubMedGoogle Scholar
• Capecchi A, Reymond J-L. Peptides in chemical space. Medicine in Drug Discovery. 2021;9:100081.
   Google Scholar
• Flores-Padilla A, Juárez-Mercado EK, Naveja JJ, et al. Chemoinformatic characterization of synthetic screening libraries focused on epigenetic targets. Mol Inf. 2022. DOI:https://doi.org/10.1002/minf.202100285.
   Google Scholar
• Arús-Pous J, Blaschke T, Ulander S, et al. Exploring the GDB-13 chemical space using deep generative models. J Cheminform. 2019;11:20.
   PubMed Web of Science ®Google Scholar
• Zabolotna Y, Volochnyuk DM, Ryabukhin SV, et al. A close-up look at the chemical space of commercially available building blocks for medicinal chemistry. J Chem Inf Model. 2022;62:2171–2185.
   PubMedGoogle Scholar
• Naveja JJ, Medina-Franco JL. ChemMaps: towards an approach for visualizing the chemical space based on adaptive satellite compounds. F1000Res. 2017;6(Chem Inf Sci):1134.
   Google Scholar
• Borrel A, Kleinstreuer NC, Fourches D. Exploring drug space with ChemMaps.com. Bioinformatics. 2018;34:3773–3775.
   PubMedGoogle Scholar
• Capecchi A, Reymond J-L. Assigning the origin of microbial natural products by chemical space map and machine learning. Biomolecules. 2020;10:1385.
   Google Scholar
• Dunn TB, Seabra GM, Kim TD, et al. Diversity and chemical library networks of large data sets. J Chem Inf Model. 2022;62:2186–2201.
   PubMedGoogle Scholar
• Wawer M, Lounkine E, Wassermann AM, et al. Data structures and computational tools for the extraction of SAR information from large compound sets. Drug Discov Today. 2010;15(15–16):630–639.
   PubMed Web of Science ®Google Scholar
• Naveja JJ, Medina-Franco JL. Consistent cell-selective analog series as constellation luminaries in chemical space. Mol Inform. 2020;39:e2000061.
   PubMedGoogle Scholar
• López-López E, Cerda-García-Rojas CM, Medina-Franco JL. Tubulin inhibitors: a chemoinformatic analysis using cell-based data. Molecules. 2021;26:2483.
   PubMedGoogle Scholar
• Sander T, Freyss J, von Korff M, et al. DataWarrior: an open-source program for chemistry aware data visualization and analysis. J Chem Inf Model. 2015;55:460–473.
   PubMed Web of Science ®Google Scholar
• Bohacek RS, McMartin C, Guida WC. The art and practice of structure-based drug design: a molecular modeling perspective. Med Res Rev. 1996;16:3–50.
   PubMed Web of Science ®Google Scholar
• Zdrazil B, Richter L, Brown N, et al. Moving targets in drug discovery. Sci Rep. 2020;10:20213.
   PubMed Web of Science ®Google Scholar
• Medina-Franco JL. Grand challenges of computer-aided drug design: the road ahead. Front Drug Discov. 2021;1:728551.
   Google Scholar
• Takeda S, Kaneko H, Funatsu K. Chemical-space-based de novo design method to generate drug-like molecules. J Chem Inf Model. 2016;56:1885–1893.
   PubMedGoogle Scholar
• Capecchi A, Zhang A, Reymond J-L. Populating chemical space with peptides using a genetic algorithm. J Chem Inf Model. 2020;60:121–132.
   PubMedGoogle Scholar
• Nigam A, Pollice R, Krenn M, et al. Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES. Chem Sci. 2021;12:7079–7090.
   PubMed Web of Science ®Google Scholar
• Krenn M, Häse F, Nigam A, et al. SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry. Mach Learn Sci Technol. 2020;1:045024.
   Google Scholar
• Meyers J, Fabian B, Brown N. De novo molecular design and generative models. Drug Discov Today. 2021;26:2707–2715.
   PubMed Web of Science ®Google Scholar
• Elton DC, Boukouvalas Z, Fuge MD, et al. Deep learning for molecular design—a review of the state of the art. Mol Syst Des Eng. 2019;4:828–849.
   Web of Science ®Google Scholar
• Schneider G, Clark DE. Automated de novo drug design: are we nearly there yet? Angew Chem Int Ed Engl. 2019;58(32):10792–10803.
   PubMed Web of Science ®Google Scholar
• Perianes-Rodriguez A, Waltman L, van Eck NJ. Constructing bibliometric networks: a comparison between full and fractional counting. J Informetr. 2016;10:1178–1195.
   Web of Science ®Google Scholar
• Orlov AA, Berishvili VP, Nikitina AA, et al. Chapter 13 - analysis of chemical spaces: implications for drug repurposing.In: Roy K, editor. Silico drug design. Academic Press; 2019. p. 359–395.
   Google Scholar
• Kumar A, Loharch S, Kumar S, et al. Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2. Comput Struct Biotechnol J. 2021;19:424–438.
   PubMed Web of Science ®Google Scholar
• Horvath D, Orlov A, Osolodkin D, et al. A chemographic audit of anti-coronavirus structure-activity information from public databases (ChEMBL). Mol Inform. 2020;39:e2000080.
   PubMed Web of Science ®Google Scholar
• Chakraborti S, Bheemireddy S, Srinivasan N. Repurposing drugs against the main protease of SARS-CoV-2: mechanism-based insights supported by available laboratory and clinical data. Mol Omics. 2020;16:474–491.
   PubMedGoogle Scholar
• Santana MVS, Silva-Jr FP. De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning. BMC Chem Biol. 2021;15:8.
   Google Scholar
• Virshup AM, Contreras-García J, Wipf P, et al. Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds. J Am Chem Soc. 2013;135:7296–7303.
   PubMed Web of Science ®Google Scholar
• Henault ES, Rasmussen MH, Jensen JH. Chemical space exploration: how genetic algorithms find the needle in the haystack. PeerJ Phy Chem. 2020;2:e11.
   Google Scholar
• Gómez-Bombarelli R, Wei JN, Duvenaud D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci. 2018;4:268–276.
   PubMed Web of Science ®Google Scholar
• Winter R, Montanari F, Steffen A, et al. Efficient multi-objective molecular optimization in a continuous latent space. Chem Sci. 2019;10:8016–8024.
   PubMedGoogle Scholar
• Li X, Xu Y, Yao H, et al. Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors. J Cheminform. 2020;12:42.
   PubMedGoogle Scholar
• Brown N, Fiscato M, Segler MHS, et al. GuacaMol: benchmarking models for de novo molecular design. J Chem Inf Model. 2019;59:1096–1108.
   PubMed Web of Science ®Google Scholar
• Guimaraes GL, Sanchez-Lengeling B, Outeiral C, et al. Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models. arXiv [stat.ML]. 2017. [cited 2022 Jun 1]. arXiv [stat.ML]: http://arxiv.org/abs/1705.10843
   Google Scholar
• Prykhodko O, Johansson SV, Kotsias P-C, et al. A de novo molecular generation method using latent vector based generative adversarial network. J Cheminform. 2019;11:74.
   PubMed Web of Science ®Google Scholar
• Yasonik J. Multiobjective de novo drug design with recurrent neural networks and nondominated sorting. J Cheminform. 2020;12:14.
   PubMed Web of Science ®Google Scholar
• Nicolaou CA, Brown N. Multi-objective optimization methods in drug design. Drug Discov Today Technol. 2013;10:e427–e435.
   PubMedGoogle Scholar
• Coley CW. Defining and exploring chemical spaces. Trends in Chemistry. 2021;3:133–145.
   Google Scholar
• Mendez D, Gaulton A, Bento AP, et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 2019;47:D930–D940.
   PubMed Web of Science ®Google Scholar
• Ertl P. Magic rings: navigation in the ring chemical space guided by the bioactive rings. J Chem Inf Model. 2022;62:2164–2170.
   PubMedGoogle Scholar
• Zabolotna Y, Ertl P, Horvath D, et al. NP navigator: a new look at the natural product chemical space. Mol Inf. 2021;40:e2100068.
   PubMedGoogle Scholar
• Chen Y, Kirchmair J. Cheminformatics in natural product-based drug discovery. Mol Inf. 2020;39:e2000171.
   PubMed Web of Science ®Google Scholar
• Medina-Franco JL, Saldívar-González FI. Cheminformatics to characterize pharmacologically active natural products. Biomolecules. 2020;10:1566.
   Google Scholar
• Sorokina M, Merseburger P, Rajan K, et al. COCONUT online: collection of open natural products database. J Cheminform. 2021;13:2.
   PubMedGoogle Scholar
• Irwin JJ, Tang KG, Young J, et al. ZINC20—A free ultralarge-scale chemical database for ligand discovery. J Chem Inf Model. 2020;60:6065–6073.
   PubMed Web of Science ®Google Scholar
• Karageorgis G, Foley DJ, Laraia L, et al. Principle and design of pseudo-natural products. Nat Chem. 2020;12:227–235.
   PubMedGoogle Scholar
• Madariaga-Mazón A, Naveja JJ, Medina-Franco JL, et al. DiaNat-DB: a molecular database of antidiabetic compounds from medicinal plants. RSC Adv. 2021;11:5172–5178.
   PubMedGoogle Scholar
• Domínguez-Mendoza EA, Galván-Ciprés Y, Martínez-Miranda J, et al. Design, synthesis, and in silico multitarget pharmacological simulations of acid bioisosteres with a validated in vivo antihyperglycemic effect. Molecules. 2021;26(4):799.
   PubMedGoogle Scholar
• Colín-Lozano B, Estrada-Soto S, Chávez-Silva F, et al. Design, synthesis and in combo antidiabetic bioevaluation of multitarget phenylpropanoic acids. Molecules. 2018;23:340.
   Google Scholar
• Nava-Molina L, Uchida-Fuentes T, Ramos-Tovar H, et al. Novel CB1 receptor antagonist BAR-1 modifies pancreatic islet function and clinical parameters in prediabetic and diabetic mice. Nutr Diabetes. 2020;10:7.
   PubMedGoogle Scholar
• Gutierréz-Hernández A, Galván-Ciprés Y, Domínguez-Mendoza EA, et al. Design, synthesis, antihyperglycemic studies, and docking simulations of benzimidazole-thiazolidinedione hybrids. J Chem Chem Eng. 2019 Oct 15. DOI:https://doi.org/10.1155/2019/1650145.
   Google Scholar
• Herrera-Rueda MÁ, Tlahuext H, Paoli P, et al. Design, synthesis, in vitro, in vivo and in silico pharmacological characterization of antidiabetic N-Boc-l-tyrosine-based compounds. Biomed Pharmacother. 2018;108:670–678.
   PubMedGoogle Scholar