Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Dionisio   A.   Olmedo; Armando   A.   Durant-Archibold; José   Luis   López-Pérez; José   Luis   Medina-Franco
Abstract

Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.
Keywords: Artificial intelligence, chemical libraries, chemoinformatics, chemical space, compound databases, natural products.
Graphical Abstract

[1]
Sarker, S.D.; Nahar, L. Application of computation in building dereplicated phytochemical libraries. In: Computational Phytochemistry; Elsevier, 2018. 

[2]
Walters, W.P. Virtual chemical libraries. J. Med. Chem.,  2019, 62(3), 1116-1124.
 [http://dx.doi.org/10.1021/acs.jmedchem.8b01048] [PMID: 30148631]

[3]
 Targetmol, New compounds. 2023. Available from: targetmol.com/all-compound-libraries.html

[4]
Van Hilten, N.; Chevillard, F.; Kolb, P. Virtual compound libraries in computer-assisted drug discovery. J. Chem. Inf. Model.,  2019, 59(2), 644-651.
 [http://dx.doi.org/10.1021/acs.jcim.8b00737] [PMID: 30624918]

[5]
Sauban Ghani, S. A comprehensive review of database resources in chemistry. Eclét. Quím.,  2020, 45(3), 57-68.
 [http://dx.doi.org/10.26850/1678-4618eqj.v45.3.2020.p57-68]

[6]
de la Vega de León, A.; Lounkine, E.; Vogt, M.; Bajorath, J. Design of diverse and focused compound libraries.Tutorials in Chemoinformatics, 1st ed; Varnek, A., Ed.; John Wiley and Sons Ltd: New Jersey, 2017, pp. 85-101.
 [http://dx.doi.org/10.1002/9781119161110.ch5]

[7]
Koutsoukas, A.; Paricharak, S.; Galloway, W.R.; Spring, D.R.; Ijzerman, A.P.; Glen, R.C.; Marcus, D.; Bender, A.A. How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space. J. Chem. Inf. Model.,  2014, 54(1), 230-242.

[8]
Petrone, P.M.; Wassermann, A.M.; Lounkine, E.; Kutchukian, P.; Simms, B.; Jenkins, J.; Selzer, P.; Glick, M. Biodiversity of small molecules--a new perspective in screening set selection. Drug Discov. Today,  2013, 18(13-14), 674-680.
 [http://dx.doi.org/10.1016/j.drudis.2013.02.005] [PMID: 23454345]

[9]
Shelat, A.A.; Guy, R.K. Scaffold composition and biological relevance of screening libraries. Nat. Chem. Biol.,  2007, 3(8), 442-446.
 [http://dx.doi.org/10.1038/nchembio0807-442] [PMID: 17637770]

[10]
Fitzgerald, S.H.; Sabat, M.; Geysen, H.M. Diversity space and its application to library selection and design. J. Chem. Inf. Model.,  2006, 46(4), 1588-1597.
 [http://dx.doi.org/10.1021/ci060066z] [PMID: 16859290]

[11]
Pascolutti, M.; Quinn, R.J. Natural products as lead structures: chemical transformations to create lead-like libraries. Drug Discov. Today,  2014, 19(3), 215-221.
 [http://dx.doi.org/10.1016/j.drudis.2013.10.013] [PMID: 24171951]

[12]
Camp, D.; Davis, R.A.; Campitelli, M.; Ebdon, J.; Quinn, R.J. Drug-like properties: guiding principles for the design of natural product libraries. J. Nat. Prod.,  2012, 75(1), 72-81.
 [http://dx.doi.org/10.1021/np200687v] [PMID: 22204643]

[13]
Butler, M.S.; Fontaine, F.; Cooper, M.A. Natural product libraries: assembly, maintenance, and screening. Planta Med.,  2014, 80(14), 1161-1170.
 [PMID: 24310213]

[14]
Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model.,  2010, 50(5), 742-754.
 [http://dx.doi.org/10.1021/ci100050t]

[15]
Díaz-Eufracio, B.I.; Palomino-Hernández, O.; Arredondo-Sánchez, A.; Medina-Franco, J.L. D-Peptide Builder: a web service to enumerate, analyze, and visualize the chemical space of combinatorial peptide libraries. Mol. Inform.,  2020, 39, e202000035.
 [http://dx.doi.org/10.1002/minf.202000035]

[16]
Saldívar-González, F.I.; Huerta-García, C.S.; Medina-Franco, J.L. Chemoinformatics-based enumeration of chemical libraries: a tutorial. J. Cheminform.,  2020, 12(1), 64.
 [http://dx.doi.org/10.1186/s13321-020-00466-z] [PMID: 33372622]

[17]
Medina-Franco, J.L.; Martinez-Mayorga, K.; Meurice, N. Balancing novelty with confined chemical space in modern drug discovery. Expert Opin. Drug Discov.,  2014, 9(2), 151-165.
 [http://dx.doi.org/10.1517/17460441.2014.872624] [PMID: 24350718]

[18]
Skalic, M.; Jiménez, J.; Sabbadin, D.; De Fabritiis, G. Shape-Based Generative Modeling for de Novo Drug Design. J. Chem. Inf. Model.,  2019, 59(3), 1205-1214.
 [http://dx.doi.org/10.1021/acs.jcim.8b00706] [PMID: 30762364]

[19]
Kumar, A.; Zhang, K.Y.J. Advances in the development of shape similarity methods and their application in drug discovery. Front Chem.,  2018, 6, 315.
 [http://dx.doi.org/10.3389/fchem.2018.00315] [PMID: 30090808]

[20]
Kaserer, T.; Beck, K.R.; Akram, M.; Odermatt, A.; Schuster, D. Pharmacophore models and pharmacophore-based virtual screening: concepts and applications exemplified on hydroxysteroid dehydrogenases. Molecules,  2015, 20(12), 22799-22832.
 [http://dx.doi.org/10.3390/molecules201219880] [PMID: 26703541]

[21]
Naderi, M.; Alvin, C.; Ding, Y.; Mukhopadhyay, S.; Brylinski, M. A graph-based approach to construct target-focused libraries for virtual screening. J. Cheminform.,  2016, 8(1), 14.
 [http://dx.doi.org/10.1186/s13321-016-0126-6] [PMID: 26981157]

[22]
Holth, T.A.D.; Walters, M.A.; Hutt, O.E.; Georg, G.I. Diversity oriented library synthesis from steviol and isosteviol-derived scaffolds. ACS Comb. Sci.,  2020, 22(3), 150-155.
 [http://dx.doi.org/10.1021/acscombsci.9b00186] [PMID: 32065745]

[23]
Arya, P.; Quevillon, S.; Joseph, R.; Wei, C.Q.; Gan, Z.; Parisien, M.; Sesmilo, E.; Reddy, P.T.; Chen, Z.X.; Durieux, P.; Laforce, D.; Campeau, L.C.; Khadem, S.; Couve-Bonnaire, S.; Kumar, R.; Sharma, U.; Leek, D.M.; Daroszewska, M.; Barnes, M.L. Toward the library generation of natural product-like polycyclic derivatives by stereocontrolled diversity-oriented synthesis. Pure Appl. Chem.,  2005, 77(1), 163-178.
 [http://dx.doi.org/10.1351/pac200577010163]

[24]
Bosc, N.; Muller, C.; Hoffer, L.; Lagorce, D.; Bourg, S.; Derviaux, C.; Gourdel, M.E.; Rain, J.C.; Miller, T.W.; Villoutreix, B.O.; Miteva, M.A.; Bonnet, P.; Morelli, X.; Sperandio, O.; Roche, P. Fr- PPIChem: an academic compound library dedicated to protein- protein interactions. ACS Chem. Biol.,  2020, 15(6), 1566-1574.
 [http://dx.doi.org/10.1021/acschembio.0c00179] [PMID: 32320205]

[25]
Zhang, X.; Betzi, S.; Morelli, X.; Roche, P. Focused chemical libraries--design and enrichment: an example of protein-protein interaction chemical space. Future Med. Chem.,  2014, 6(11), 1291-1307.
 [http://dx.doi.org/10.4155/fmc.14.57] [PMID: 24773599]

[26]
Olivecrona, M.; Blaschke, T.; Engkvist, O.; Chen, H. Molecular de-novo design through deep reinforcement learning. J. Cheminform.,  2017, 9(1), 48.
 [http://dx.doi.org/10.1186/s13321-017-0235-x] [PMID: 29086083]

[27]
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, 58(6), 1194-1204.
 [http://dx.doi.org/10.1021/acs.jcim.7b00690] [PMID: 29762023]

[28]
Gómez-Bombarelli, R.; Wei, J.N.; Duvenaud, D.; Hernández-Lobato, J.M.; Sánchez-Lengeling, B.; Sheberla, D. Aguilera- Iparraguirre, J.; Hirzel, T.D.; Adams, R.P.; Aspuru-Guzik, A. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci.,  2018, 4(2), 268-276.
 [http://dx.doi.org/10.1021/acscentsci.7b00572] [PMID: 29532027]

[29]
Lusher, S.J.; McGuire, R.; van Schaik, R.C.; Nicholson, C.D.; de Vlieg, J. Data-driven medicinal chemistry in the era of big data. Drug Discov. Today,  2014, 19(7), 859-868.
 [http://dx.doi.org/10.1016/j.drudis.2013.12.004] [PMID: 24361338]

[30]
Paricharak, S.; Méndez-Lucio, O.; Chavan Ravindranath, A.; Bender, A.; IJzerman, A.P.; van Westen, G.J.P. Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening. Brief. Bioinform.,  2018, 19(2), 277-285.
 [PMID: 27789427]

[31]
Segler, M.H.S.; Kogej, T.; Tyrchan, C.; Waller, M.P. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent. Sci.,  2018, 4(1), 120-131.
 [http://dx.doi.org/10.1021/acscentsci.7b00512]

[32]
Merk, D.; Friedrich, L.; Grisoni, F.; Schneider, G. De novo design of bioactive small molecules by artificial intelligence. Mol. Inform.,  2018, 37(1-2), 1700153.
 [http://dx.doi.org/10.1002/minf.201700153] [PMID: 29319225]

[33]
Lyu, J.; Wang, S.; Balius, T.E.; Singh, I.; Levit, A.; Moroz, Y.S.; O’Meara, M.J.; Che, T.; Algaa, E.; Tolmachova, K.; Tolmachev, A.A.; Shoichet, B.K.; Roth, B.L.; Irwin, J.J. Ultra-large library docking for discovering new chemotypes. Nature,  2019, 566(7743), 224-229.
 [http://dx.doi.org/10.1038/s41586-019-0917-9] [PMID: 30728502]

[34]
 Available from: nmrshiftdb.nmr.uni-koeln.de/html

[35]
 Interactive databases. Available from: sdbs.db.aist.go.jp/sdbs/cgibin/direct_frame_top.cgi.html

[36]
 Available from: np-mrd.org/html

[37]
 Available from: c13.materia-medica.net/html

[38]
Perez-Riverol, Y.; Wang, R.; Hermjakob, H.; Müller, M.; Vesada, V.; Vizcaíno, J.A. Open source libraries and frameworks for mass spectrometry based proteomics: a developer’s perspective. Biochim. Biophys. Acta,  2014, 1844(1 Pt A), 63-76.
 [http://dx.doi.org/10.1016/j.bbapap.2013.02.032] [PMID: 23467006]

[39]
Gabriel, J.; Höfner, G.; Wanner, K.T. A library screening strategy combining the concepts of ms binding assays and affinity selection mass spectrometry. Front Chem.,  2019, 7, 665.
 [http://dx.doi.org/10.3389/fchem.2019.00665] [PMID: 31637233]

[40]
McLaren, D.G.; Shah, V.; Wisniewski, T.; Ghislain, L.; Liu, C.; Zhang, H.; Saldanha, S.A. High-throughput mass spectrometry for hit identification: current landscape and future perspectives. SLAS Discov. Adv. Life Sci.,  2021, 26(2), 168-191.

[41]
Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci.,  1988, 28(1), 31-36.
 [http://dx.doi.org/10.1021/ci00057a005]

[42]
Weininger, D.; Weininger, A.; Weininger, J.L. SMILES. 2. Algorithm for generation of unique SMILES notation. J. Chem. Inf. Comput. Sci.,  1989, 29(2), 97-101.
 [http://dx.doi.org/10.1021/ci00062a008]

[43]
Weininger, D. SMILES a language for molecules and reactions. Handbook of chemoinformatics; Gasteiger, J., Ed.; Wiley-VCH Verlag GmbH & Co. KgaA: Germany, 2003, pp. 80-102.
 [http://dx.doi.org/10.1002/9783527618279.ch5]

[44]
Hanson, R.M. Jmol SMILES and Jmol SMARTS: specifications and applications. J. Cheminform.,  2016, 8(1), 50.
 [http://dx.doi.org/10.1186/s13321-016-0160-4] [PMID: 28316648]

[45]
Winter, R.; Montanari, F.; Noé, F.; Clevert, D.A. Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations. Chem. Sci. (Camb.),  2018, 10(6), 1692-1701.
 [http://dx.doi.org/10.1039/C8SC04175J] [PMID: 30842833]

[46]
Arús-Pous, J.; Patronov, A.; Bjerrum, E.J.; Tyrchan, C.; Reymond, J.L.; Chen, H.; Engkvist, O. SMILES-based deep generative Scaffold decorator for de-novo drug design. J. Cheminform.,  2020, 12(1), 38.
 [http://dx.doi.org/10.1186/s13321-020-00441-8] [PMID: 33431013]

[47]
Weininger, D. SMILES. 3. DEPICT. Graphical depiction of chemical structures. J. Chem. Inf. Model.,  1990, 30(3), 237-243.

[48]
O’Boyle, N.M. Towards a Universal SMILES representation - A standard method to generate canonical SMILES based on the InChI. J. Cheminform.,  2012, 4, 22.

[49]
Schmidt, R.; Ehmki, E.S.R.; Ohm, F.; Ehrlich, H.C.; Mashychev, A.; Rarey, M. Comparing molecular patterns using the example of SMARTS: theory and algorithms. J. Chem. Inf. Model.,  2019, 59(6), 2560-2571.
 [http://dx.doi.org/10.1021/acs.jcim.9b00250] [PMID: 31120751]

[50]
McNaught, A. The IUPAC international chemical identifier. Chem. Int.,  2006, 28(6), 12-15.

[51]
Heller, S.R.; McNaught, A.; Pletnev, I.; Stein, S.; Tchekhovskoi, D. InChI, the IUPAC international chemical identifier. J. Cheminform.,  2015, 7(1), 23.
 [http://dx.doi.org/10.1186/s13321-015-0068-4] [PMID: 26136848]

[52]
 Inchi-trust.org/html  Available from: https://www.inchi-trust.org/html

[53]
Ullmann, J.R. An Algorithm for subgraph isomorphism. J. Assoc. Comput. Mach.,  1976, 23(1), 31-42.
 [http://dx.doi.org/10.1145/321921.321925]

[54]
Mahmood, O.; Mansimov, E.; Bonneau, R.; Cho, K. Masked graph modeling for molecule generation. Nat. Commun.,  2021, 12(1), 3156.
 [http://dx.doi.org/10.1038/s41467-021-23415-2] [PMID: 34039973]

[55]
Yirik, M.A.; Steinbeck, C. Chemical graph generators. PLOS Comput. Biol.,  2021, 17(1), e1008504.
 [http://dx.doi.org/10.1371/journal.pcbi.1008504] [PMID: 33400699]

[56]
Butina, D. Unsupervised data base clustering based on daylight’s fingerprint and tanimoto similarity: a fast and automated way to cluster small and large data sets. J. Chem. Inf. Comput. Sci.,  1999, 39(4), 747-750.
 [http://dx.doi.org/10.1021/ci9803381]

[57]
Lim, J.; Hwang, S.Y.; Moon, S.; Kim, S.; Kim, W.Y. Scaffold-based molecular design with a graph generative model. Chem. Sci. (Camb.),  2019, 11(4), 1153-1164.
 [http://dx.doi.org/10.1039/C9SC04503A] [PMID: 34084372]

[58]
Shampa Raghunathan, S.; Priyakumar, U.D. Molecular representations for machine learning applications in chemistry. Int. J. Quantum Chem.,  2021, 2021, e26870.
 [http://dx.doi.org/10.1002/qua.26870]

[59]
Mercado, R.; Rastemo, T.; Lindelöf, E.; Klambauer, G.; Engkvist, O.; Chen, H.; Bjerrum, E.J. Graph networks for molecular design. Mach. Learn.: Sci. Technol.,  2020, 2, 025023.

[60]
David, L.; Thakkar, A.; Mercado, R.; Engkvist, O. Molecular representations in AI-driven drug discovery: a review and practical guide. J. Cheminform.,  2020, 12(1), 56.
 [http://dx.doi.org/10.1186/s13321-020-00460-5] [PMID: 33431035]

[61]
Warr, W.A.; Nicklaus, M.C.; Nicolaou, C.A.; Rarey, M. Exploration of ultralarge compound collections for drug discovery. J. Chem. Inf. Model.,  2022, 62(9), 2021-2034.
 [http://dx.doi.org/10.1021/acs.jcim.2c00224]

[62]
Kawabata, T. Build-up algorithm for atomic correspondence between chemical structures. J. Chem. Info. Model.,  2011, 51, 1775-1787.
 [http://dx.doi.org/10.1021/ci2001023] [PMID: 21736325]

[63]
Schneider, P.; Schneider, G. De Novo design at the edge of chaos. J. Med. Chem.,  2016, 59(9), 4077-4086.
 [http://dx.doi.org/10.1021/acs.jmedchem.5b01849] [PMID: 26881908]

[64]
Schneider, G.; Clark, D.E. automated de novo drug design: are we nearly there yet? Angew. Chem. Int. Ed. Engl.,  2019, 58(32), 10792-10803.
 [http://dx.doi.org/10.1002/anie.201814681] [PMID: 30730601]

[65]
Mouchlis, V.D.; Afantitis, A.; Serra, A.; Fratello, M.; Papadiamantis, A.G.; Aidinis, V.; Lynch, I.; Greco, D.; Melagraki, G. Advances in de novo drug design: from conventional to machine learning methods. Int. J. Mol. Sci.,  2021, 22(4), 1676.
 [http://dx.doi.org/10.3390/ijms22041676] [PMID: 33562347]

[66]
Popova, M.; Isayev, O.; Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv.,  2018, 4(7), eaap7885.
 [http://dx.doi.org/10.1126/sciadv.aap7885] [PMID: 30050984]

[67]
Li, Y.; Pei, J.; Lai, L. Structure-based de novo drug design using 3D deep generative models. Chem. Sci. (Camb.),  2021, 12(41), 13664-13675.
 [http://dx.doi.org/10.1039/D1SC04444C] [PMID: 34760151]

[68]
Spiegel, J.O.; Durrant, J.D. AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization. J. Cheminform.,  2020, 12(1), 25.
 [http://dx.doi.org/10.1186/s13321-020-00429-4] [PMID: 33431021]

[69]
Domenico, A.; Nicola, G.; Daniela, T.; Fulvio, C.; Nicola, A.; Orazio, N. De novo drug design of targeted chemical libraries based on artificial intelligence and pair-based multiobjective optimization. J. Chem. Inf. Model.,  2020, 60(10), 4582-4593.
 [http://dx.doi.org/10.1021/acs.jcim.0c00517] [PMID: 32845150]

[70]
Ghiandoni, G.M.; Bodkin, M.J.; Chen, B.; Hristozov, D.; Wallace, J.E.A.; Webster, J.; Gillet, V.J. Enhancing reaction-based de novo design using a multi-label reaction class recommender. J. Comput. Aided Mol. Des.,  2020, 34(7), 783-803.
 [http://dx.doi.org/10.1007/s10822-020-00300-6] [PMID: 32112286]

[71]
Kidd, S.L.; Osberger, T.J.; Mateu, N.; Sore, H.F.; Spring, D.R. Recent applications of diversity-oriented synthesis toward novel, 3-dimensional fragment collections. Front Chem.,  2018, 6, 460.
 [http://dx.doi.org/10.3389/fchem.2018.00460] [PMID: 30386766]

[72]
Janes, J.; Young, M.E.; Chen, E.; Rogers, N.H.; Burgstaller-Muehlbacher, S.; Hughes, L.D.; Love, M.S.; Hull, M.V.; Kuhen, K.L.; Woods, A.K.; Joseph, S.B.; Petrassi, H.M.; McNamara, C.W.; Tremblay, M.S.; Su, A.I.; Schultz, P.G.; Chatterjee, A.K. The ReFRAME library as a comprehensive drug repurposing library and its application to the treatment of cryptosporidiosis. Proc. Natl. Acad. Sci. USA,  2018, 115(42), 10750-10755.
 [http://dx.doi.org/10.1073/pnas.1810137115] [PMID: 30282735]

[73]
Meyers, J.; Fabian, B.; Brown, N. De novo molecular design and generative models. Drug Discov. Today,  2021, 26(11), 2707-2715.
 [http://dx.doi.org/10.1016/j.drudis.2021.05.019] [PMID: 34082136]

[74]
Dunn, T.B.; Seabra, G.M.; Kim, T.D.; Juárez-Mercado, K.E.; Li, C.; Medina-Franco, J.L.; Miranda-Quintana, R.A. Diversity and chemical library networks of large data sets. J. Chem. Inf. Model.,  2022, 62(9), 2186-2201.
 [http://dx.doi.org/10.1021/acs.jcim.1c01013] [PMID: 34723537]

[75]
Medina-Franco, J.L.; Sánchez-Cruz, N.; López-López, E. Díaz- Eufracio, B.I. Progress on open chemoinformatic tools for expanding and exploring the chemical space. J. Comput. Aided Mol. Des.,  2022, 36(5), 341-354.
 [http://dx.doi.org/10.1007/s10822-021-00399-1] [PMID: 34143323]

[76]
Quartararo, A.J.; Gates, Z.P.; Somsen, B.A.; Hartrampf, N.; Ye, X.; Shimada, A.; Kajihara, Y.; Ottmann, C.; Pentelute, B.L. Ultra-large chemical libraries for the discovery of high-affinity peptide binders. Nat. Commun.,  2020, 11(1), 3183.
 [http://dx.doi.org/10.1038/s41467-020-16920-3] [PMID: 32576815]

[77]
Grygorenko, O.O.; Radchenko, D.S.; Dziuba, I.; Chuprina, A.; Gubina, K.E.; Moroz, Y.S. Generating multibillion chemical space of readily accessible screening compounds. iScience,  2020, 23(11), 101681.
 [http://dx.doi.org/10.1016/j.isci.2020.101681] [PMID: 33145486]

[78]
Varnek, A.; Baskin, I.I. Chemoinformatics as a theoretical chemistry Discipline. Mol. Inform.,  2011, 30(1), 20-32.
 [http://dx.doi.org/10.1002/minf.201000100] [PMID: 27467875]

[79]
Meggers, E. Exploring biologically relevant chemical space with metal complexes. Curr. Opin. Chem. Biol.,  2007, 11(3), 287-292.
 [http://dx.doi.org/10.1016/j.cbpa.2007.05.013] [PMID: 17548234]

[80]
Saldívar-González, F.I.; Medina-Franco, J.L. Chemoinformatics approaches to assess chemical diversity and complexity of small molecules; Small Mol. Drug Discov, 2020, pp. 83-102.
 [http://dx.doi.org/10.1016/B978-0-12-818349-6.00003-0]

[81]
Medina-Franco, J.L. Grand challenges of computer-aided drug design: the road ahead. Front. Drug Discov.,  2021, 17, 28551.
 [http://dx.doi.org/10.3389/fddsv.2021.728551]

[82]
Núñez, M.J.; Díaz-Eufracio, B.I.; Medina-Franco, J.L.; Olmedo, D.A. Latin American databases of natural products: biodiversity and drug discovery against SARS-CoV-2. RSC Advances,  2021, 11(26), 16051-16064.
 [http://dx.doi.org/10.1039/D1RA01507A] [PMID: 35481202]

[83]
Ruddigkeit, L.; Blum, L.C.; Reymond, J.L. Visualization and virtual screening of the chemical universe database GDB-17. J. Chem. Inf. Model.,  2013, 53(1), 56-65.
 [http://dx.doi.org/10.1021/ci300535x] [PMID: 23259841]

[84]
Arús-Pous, J.; Blaschke, T.; Ulander, S.; Reymond, J.L.; Chen, H.; Engkvist, O. Exploring the GDB-13 chemical space using deep generative models. J. Cheminform.,  2019, 11(1), 20.
 [http://dx.doi.org/10.1186/s13321-019-0341-z] [PMID: 30868314]

[85]
Olmedo, D.A.; González-Medina, M.; Gupta, M.P.; Medina-Franco, J.L. Cheminformatic characterization of natural products from Panama. Mol. Divers.,  2017, 21(4), 779-789.
 [http://dx.doi.org/10.1007/s11030-017-9781-4] [PMID: 28831697]

[86]
Saldívar-González, F.I.; Pilón-Jiménez, B.A.; Medina-Franco, J.L. Chemical space of naturally occurring compounds. Phys. Sci. Rev.,  2018, 4, 20180103.

[87]
Rodrígues, T. Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point. Org. Biomol. Chem.,  2017, 15(44), 9275-9282.
 [http://dx.doi.org/10.1039/C7OB02193C] [PMID: 29085945]

[88]
Osolodkin, D.I.; Radchenko, E.V.; Orlov, A.A.; Voronkov, A.E.; Palyulin, V.A.; Zefirov, N.S. Progress in visual representations of chemical space. Expert Opin. Drug Discov.,  2015, 10(9), 959-973.
 [http://dx.doi.org/10.1517/17460441.2015.1060216] [PMID: 26094796]

[89]
Capecchi, A.; Reymond, J.L. Peptides in chemical space. Med. Drug Discov.,  2021, 9, 100081.
 [http://dx.doi.org/10.1016/j.medidd.2021.100081]

[90]
Naveja, J.J.; Rico-Hidalgo, M.P.; Medina-Franco, J.L. Analysis of a large food chemical database: chemical space, diversity, and complexity. F1000 Res.,  2018, 2018, 7.

[91]
Bayer, S.; Mayer, A.I.; Borgonovo, G.; Morini, G.; Di Pizio, A.; Bassoli, A. Chemoinformatics view on bitter taste receptor agonists in food. J. Agric. Food Chem.,  2021, 69(46), 13916-13924.
 [http://dx.doi.org/10.1021/acs.jafc.1c05057] [PMID: 34762411]

[92]
Schuffenhauer, A.; Varin, T. Rule-based classification of chemical structures by scaffold. Mol. Inform.,  2011, 30(8), 646-664.
 [http://dx.doi.org/10.1002/minf.201100078] [PMID: 27467257]

[93]
Medina-Franco, J.L.; Martínez-Mayorga, K.; Bender, A.; Scior, T. Scaffold diversity analysis of compound data sets using an entropy-based measure. QSAR Comb. Sci.,  2009, 28, 1551-1560.
 [http://dx.doi.org/10.1002/qsar.200960069]

[94]
Bhurta, D.; Bharate, S.B. Analyzing the scaffold diversity of cyclin- dependent kinase inhibitors and revisiting the clinical and preclinical pipeline. Med. Res. Rev.,  2022, 42(2), 654-709.
 [http://dx.doi.org/10.1002/med.21856] [PMID: 34605036]

[95]
Maldonado, A.G.; Doucet, J.P.; Petitjean, M.; Fan, B.T. Molecular similarity and diversity in chemoinformatics: from theory to applications. Mol. Divers.,  2006, 10(1), 39-79.
 [http://dx.doi.org/10.1007/s11030-006-8697-1] [PMID: 16404528]

[96]
Bajusz, D.; Rácz, A.; Héberger, K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J. Cheminform.,  2015, 7(1), 20.
 [http://dx.doi.org/10.1186/s13321-015-0069-3] [PMID: 26052348]

[97]
Yongye, A.B.; Byler, K.; Santos, R.; Martínez-Mayorga, K.; Maggiora, G.M.; Medina-Franco, J.L. Consensus models of activity landscapes with multiple chemical, conformer, and property representations. J. Chem. Inf. Model.,  2011, 51(6), 1259-1270.
 [http://dx.doi.org/10.1021/ci200081k] [PMID: 21609014]

[98]
Medina-Franco, J.L.; Saldívar-González, F.I. Cheminformatics to characterize pharmacologically active natural products. Biomolecules,  2020, 10(11), 1566.
 [http://dx.doi.org/10.3390/biom10111566] [PMID: 33213003]

[99]
Zagidullin, B.; Wang, Z.; Guan, Y.; Pitkänen, E.; Tang, J. Comparative analysis of molecular fingerprints in prediction of drug combination effects. Brief. Bioinform.,  2021, 22(6), bbab291.
 [http://dx.doi.org/10.1093/bib/bbab291] [PMID: 34401895]

[100]
Tran, T.D.; Ogbourne, S.M.; Brooks, P.R.; Sánchez-Cruz, N.; Medina-Franco, J.L.; Quinn, R.J. Lessons from exploring chemical space and chemical diversity of propolis components. Int. J. Mol. Sci.,  2020, 21(14), 4988.
 [http://dx.doi.org/10.3390/ijms21144988] [PMID: 32679731]

[101]
Olmedo, D.A.; Medina-Franco, J.L. Chemoinformatic Approach: The Case of Natural Products of Panama. Cheminformatics and its Applications; Stefaniu, A.; Rasul, A; Hussain, G., Ed.; Intechopen Ltd.: The United Kingdom, 2020, pp. 83-106.
 [http://dx.doi.org/10.5772/intechopen.87779]

[102]
Amberg, A.; Riefke, B.; Schlotterbeck, G.; Ross, A.; Senn, H.; Dieterle, F.; Keck, M. NMR and MS methods for metabolomics; Drug Saf. Eval, 2017, pp. 229-259.
 [http://dx.doi.org/10.1007/978-1-4939-7172-5_13]

[103]
Schlotterbeck, G.; Ross, A.; Dieterle, F.; Senn, H. Metabolic profiling technologies for biomarker discovery in biomedicine and drug development. Pharmacogenomics,  2006, 7(7), 1055-1075.
 [http://dx.doi.org/10.2217/14622416.7.7.1055] [PMID: 17054416]

[104]
Yang, X.; Parker, D.; Whitehead, L.; Ryder, N.S.; Weidmann, B.; Stabile-Harris, M.; Kizer, D.; McKinnon, M.; Smellie, A.; Powers, D. A collaborative hit-to-lead investigation leveraging medicinal chemistry expertise with high throughput library design, synthesis and purification capabilities. Comb. Chem. High Throughput Screen.,  2006, 9(2), 123-130.
 [http://dx.doi.org/10.2174/138620706775541891] [PMID: 16475970]

[105]
Wishart, D.S.; Knox, C.; Guo, A.C.; Shrivastava, S.; Hassanali, M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res.,  2006, 34(Database issue), D668-D672.
 [http://dx.doi.org/10.1093/nar/gkj067] [PMID: 16381955]

[106]
Xia, X. Bioinformatics and drug discovery. Curr. Top. Med. Chem.,  2017, 17(15), 1709-1726.
 [http://dx.doi.org/10.2174/1568026617666161116143440] [PMID: 27848897]

[107]
Romano, J.D.; Tatonetti, N.P. Informatics and computational methods in natural product drug discovery: a review and perspectives. Front. Genet.,  2019, 10, 368.
 [http://dx.doi.org/10.3389/fgene.2019.00368] [PMID: 31114606]

[108]
Behl, T.; Kaur, I.; Sehgal, A.; Singh, S.; Bhatia, S.; Al-Harrasi, A.; Zengin, G.; Babes, E.E.; Brisc, C.; Stoicescu, M.; Toma, M.M.; Sava, C.; Bungau, S.G. Bioinformatics accelerates the major tetrad: a real boost for the pharmaceutical industry. Int. J. Mol. Sci.,  2021, 22(12), 6184.
 [http://dx.doi.org/10.3390/ijms22126184] [PMID: 34201152]

[109]
Wooller, S.K.; Benstead-Hume, G.; Chen, X.; Ali, Y.; Pearl, F.M.G. Bioinformatics in translational drug discovery. Biosci. Rep.,  2017, 37(4), BSR20160180.
 [http://dx.doi.org/10.1042/BSR20160180] [PMID: 28487472]

[110]
Yan, Q. Translational Bioinformatics and Systems Biology Methods for Personalized Medicine. Methods Mol. Biol.,  2010, 662, 167-168.

[111]
Wu, F.; Zhou, Y.; Li, L.; Shen, X.; Chen, G.; Wang, X.; Liang, X.; Tan, M.; Huang, Z. Computational approaches in preclinical studies on drug discovery and development. Front Chem.,  2020, 8, 726.
 [http://dx.doi.org/10.3389/fchem.2020.00726] [PMID: 33062633]

[112]
Pérez Santín, E.; Rodríguez Solana, R.; González García, M.; García Suárez, M.D.M.; Blanco Díaz, G.D.; Cima Cabal, M.D.; Moreno Rojas, J.M.; López Sánchez, J.I. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. Wiley Interdiscip. Rev. Comput. Mol. Sci.,  2021, 11(5)
 [http://dx.doi.org/10.1002/wcms.1516]

[113]
Jia, L.; Gao, H. Machine Learning for in silico ADMET prediction. Methods Mol. Biol.,  2022, 2390, 447-460.
 [http://dx.doi.org/10.1007/978-1-0716-1787-8_20] [PMID: 34731482]

[114]
de Souza Neto, L.R.; Moreira-Filho, J.T.; Neves, B.J., Jr; Maidana, R.L.B.R.; Guimarães, A.C.R.; Furnham, N.; Andrade, C.H.; Silva, F.P., Jr In silico strategies to support fragment-to-lead optimization in drug discovery. Front Chem.,  2020, 8, 93.
 [http://dx.doi.org/10.3389/fchem.2020.00093] [PMID: 32133344]

[115]
Durán-Iturbide, N.A.; Díaz-Eufracio, B.I.; Medina-Franco, J.L. In silico ADME/Tox profiling of natural products: a focus on BIOFACQUIM. ACS Omega,  2020, 5(26), 16076-16084.
 [http://dx.doi.org/10.1021/acsomega.0c01581] [PMID: 32656429]

[116]
Lagorce, D.; Bouslama, L.; Becot, J.; Miteva, M.A.; Villoutreix, B.O. FAF-Drugs4: free ADME-tox filtering computations for chemical biology and early stages drug discovery. Bioinformatics,  2017, 33(22), 3658-3660.
 [http://dx.doi.org/10.1093/bioinformatics/btx491] [PMID: 28961788]

[117]
Pires, D.E.V.; Blundell, T.L.; Ascher, D.B. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem.,  2015, 58(9), 4066-4072.
 [http://dx.doi.org/10.1021/acs.jmedchem.5b00104] [PMID: 25860834]

[118]
Kar, S.; Leszczynski, J. Open access in silico tools to predict the ADMET profiling of drug candidates. Expert Opin. Drug Discov.,  2020, 15(12), 1473-1487.
 [http://dx.doi.org/10.1080/17460441.2020.1798926] [PMID: 32735147]

[119]
Xiong, G.; Wu, Z.; Yi, J.; Fu, L.; Yang, Z.; Hsieh, C.; Yin, M.; Zeng, X.; Wu, C.; Lu, A.; Chen, X.; Hou, T.; Cao, D. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res.,  2021, 49(W1), W5-W14.
 [http://dx.doi.org/10.1093/nar/gkab255] [PMID: 33893803]

[120]
Venkatraman, V. FP-ADMET: a compendium of fingerprint-based ADMET prediction models. J. Cheminform.,  2021, 13(1), 75.
 [http://dx.doi.org/10.1186/s13321-021-00557-5] [PMID: 34583740]

[121]
Banerjee, P.; Eckert, A.O.; Schrey, A.K.; Preissner, R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res.,  2018, 46(W1), W257-W263.
 [http://dx.doi.org/10.1093/nar/gky318] [PMID: 29718510]

[122]
Baell, J.B.; Holloway, G.A. 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(7), 2719-2740.
 [http://dx.doi.org/10.1021/jm901137j] [PMID: 20131845]

[123]
Baell, J.; Walters, M.A. Chemistry: Chemical con artists foil drug discovery. Nature,  2014, 513(7519), 481-483.
 [http://dx.doi.org/10.1038/513481a] [PMID: 25254460]

[124]
Gilberg, E.; Jasial, S.; Stumpfe, D.; Dimova, D.; Bajorath, J. Highly promiscuous small molecules from biological screening assays include many pan-assay interference compounds but also candidates for polypharmacology. J. Med. Chem.,  2016, 59(22), 10285-10290.
 [http://dx.doi.org/10.1021/acs.jmedchem.6b01314] [PMID: 27809519]

[125]
Jasial, S.; Hu, Y.; Bajorath, J. Determining the degree of promiscuity of extensively assayed compounds. PLoS One,  2016, 11(4), e0153873.
 [http://dx.doi.org/10.1371/journal.pone.0153873] [PMID: 27082988]

[126]
Sun, J.; Zhong, H.; Wang, K.; Li, N.; Chen, L. Gains from no real PAINS: Where ‘Fair Trial Strategy’ stands in the development of multi-target ligands. Acta Pharm. Sin. B,  2021, 11(11), 3417-3432.
 [http://dx.doi.org/10.1016/j.apsb.2021.02.023] [PMID: 34900527]

[127]
Baell, J.B.; Ferrins, L.; Falk, H.; Nikolakopoulos, G. PAINS: Relevance to tool compound discovery and fragment-based screening. Aust. J. Chem.,  2013, 66(12), 1483-1494.
 [http://dx.doi.org/10.1071/CH13551]

[128]
dos Santos, J.L.; Chin, C.M. Pan-assay interference compounds (pa1ins): warning signs in biochemical-pharmacological evaluations. Biochem. Pharmacol.,  2015, 4, e173.

[129]
Baell, J.B. Feeling Nature’s PAINS: Natural Products, Natural Product Drugs, and Pan Assay Interference Compounds (PAINS). J. Nat. Prod.,  2016, 79(3), 616-628.
 [http://dx.doi.org/10.1021/acs.jnatprod.5b00947] [PMID: 26900761]

[130]
Lagorce, D.; Oliveira, N.; Miteva, M.A.; Villoutreix, B.O. Panassay interference compounds (PAINS) that may not be too painful for chemical biology projects. Drug Discov. Today,  2017, 22(8), 1131-1133.
 [http://dx.doi.org/10.1016/j.drudis.2017.05.017] [PMID: 28676405]

[131]
Baell, J.B.; Nissink, J.W.M. Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017-Utility and Limitations. ACS Chem. Biol.,  2018, 13(1), 36-44.
 [http://dx.doi.org/10.1021/acschembio.7b00903] [PMID: 29202222]

[132]
Gilberg, E.; Stumpfe, D.; Bajorath, J. Towards a systematic assessment of assay interference: Identification of extensively tested compounds with high assay promiscuity. F1000 Res.,  2017, 6, 1505.

[133]
Vidler, L.R.; Watson, I.A.; Margolis, B.J.; Cummins, D.J.; Brunavs, M. Investigating the behavior of published PAINS alerts using a pharmaceutical company data set. ACS Med. Chem. Lett.,  2018, 9(8), 792-796.
 [http://dx.doi.org/10.1021/acsmedchemlett.8b00097] [PMID: 30128069]

[134]
Gilberg, E.; Stumpfe, D.; Bajorath, J. Activity profiles of analog series containing pan assay interference compounds. RSC Advances,  2017, 7(57), 35638-35647.
 [http://dx.doi.org/10.1039/C7RA06736D]

[135]
Jasial, S.; Hu, Y.; Bajorath, J. How Frequently Are Pan-Assay Interference Compounds Active? Large-Scale Analysis of Screening Data Reveals Diverse Activity Profiles, Low Global Hit Frequency, and Many Consistently Inactive Compounds. J. Med. Chem.,  2017, 60(9), 3879-3886.
 [http://dx.doi.org/10.1021/acs.jmedchem.7b00154] [PMID: 28421750]

[136]
Capuzzi, S.J.; Muratov, E.N.; Tropsha, A. Phantom PAINS: problems with the utility of alerts for pan-assay interference compounds. J. Chem. Inf. Model.,  2017, 57(3), 417-427.
 [http://dx.doi.org/10.1021/acs.jcim.6b00465] [PMID: 28165734]

[137]
Chakravorty, S.J.; Chan, J.; Greenwood, M.N.; Popa-Burke, I.; Remlinger, K.S.; Pickett, S.D.; Green, D.; Fillmore, M.C.; Dean, T.W.; Luengo, J.I.; Macarrón, R. Nuisance compounds, PAINS filters, and dark chemical matter in the GSK HTS collection. SLAS Disc. Adv. Life Sci.,  2018, 35(6), 532-545.

[138]
Koptelov, M.; Zimmermann, A. PrePeP: A Tool for the Identification and Characterization of Pan Assay Interference Compounds. 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining,  Londres, United Kingdom2018, pp. 462-471.
 [http://dx.doi.org/10.1145/3219819.3219849]

[139]
Berthold, M.R.; Cebron, N.; Dill, F.; Gabriel, T.R.; Kötter, T.; Meinl, T.; Ohl, P.; Sieb, C.; Thiel, K.; Wiswedel, B. Data Analysis, Machine Learning and Applications. Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation, GermanyMarch 7-9, 2007

[140]
 OEChem TK. Programming library for chemistry and cheminformatics. 2012. Available from: https://www.eyesopen.com/oechemtk

[141]
Studio Team, R. RStudio: Integrated Development for R. RStudio, Inc.: Boston, MA , 2016. 

[142]
Bajorath, J. Evolution of assay interference concepts in drug discovery. Expert Opin. Drug Discov.,  2021, 16(7), 719-721.
 [http://dx.doi.org/10.1080/17460441.2021.1902983] [PMID: 33733961]

[143]
Magalhães, P.R.; Reis, P.B.P.S.; Vila-Viçosa, D.; Machuqueiro, M.; Victor, B.L. Identification of pan-assay interference compounds (PAINS) using an MD-based protocol. Methods Mol. Biol.,  2021, 2315, 263-271.
 [http://dx.doi.org/10.1007/978-1-0716-1468-6_15] [PMID: 34302681]

[144]
Matlock, M.K.; Hughes, T.B.; Dahlin, J.L.; Swamidass, S.J. Modeling small-molecule reactivity identifies promiscuous bioactive compounds. J. Chem. Inf. Model.,  2018, 58(8), 1483-1500.
 [http://dx.doi.org/10.1021/acs.jcim.8b00104] [PMID: 29990427]
Cite As
Combinatorial Chemistry & High Throughput Screening

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Abstract

Graphical Abstract
