549
Views
4
CrossRef citations to date
0
Altmetric
Review Articles

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

ORCID Icon, , , , , , & show all
Pages 6523-6541 | Received 12 Feb 2023, Accepted 03 Jul 2023, Published online: 11 Jul 2023

References

  • Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Van Esesn, B. C., Awwal, A. A. S., & Asari, V. K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches, arXiv preprint arXiv:1803.01164
  • Amendola, G., & Cosconati, S. (2021). PyRMD: A new fully automated ai-powered ligand-based virtual screening tool. Journal of Chemical Information and Modeling, 61(8), 3835–3845. https://doi.org/10.1021/acs.jcim.1c00653
  • Anwar, T., Kumar, P., & Khan, A. U. (2021). Modern tools and techniques in computer-aided drug design. In Molecular docking for computer-aided drug design (pp. 1–30). Elsevier.
  • Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M., & Sherlock, G. (2000). Gene ontology: Tool for the unification of biology. Nature Genetics, 25(1), 25–29. https://doi.org/10.1038/75556
  • Bagal, V., Aggarwal, R., Vinod, P. K., & Priyakumar, U. D. (2022). MolGPT: Molecular generation using a transformer-decoder model. Journal of Chemical Information and Modeling, 62(9), 2064–2076. https://doi.org/10.1021/acs.jcim.1c00600
  • Bagherian, M., Sabeti, E., Wang, K., Sartor, M. A., Nikolovska-Coleska, Z., & Najarian, K. (2021). Machine learning approaches and databases for prediction of drug–target interaction: A survey paper. Briefings in Bioinformatics, 22(1), 247–269. https://doi.org/10.1093/bib/bbz157
  • Balaji, K., Lavanya, K., & Mary, A. G. (2020). Machine learning algorithm for clustering of heart disease and chemoinformatics datasets. Computers & Chemical Engineering, 143, 107068. https://doi.org/10.1016/j.compchemeng.2020.107068
  • Banegas-Luna, A.-J., Cerón-Carrasco, J. P., & Pérez-Sánchez, H. (2018). A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data. Future Medicinal Chemistry, 10(22), 2641–2658. https://doi.org/10.4155/fmc-2018-0076
  • Baskaran, S. G., Sharp, T. P., & Sharp, K. A. (2021). Computational graphics software for interactive docking and visualization of ligand–protein complementarity. Journal of Chemical Information and Modeling, 61(3), 1427–1443. https://doi.org/10.1021/acs.jcim.0c01485
  • Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, IN., & Bourne, P. E. (2000). The protein data bank. Nucleic Acids Research, 28(1), 235–242. https://doi.org/10.1093/nar/28.1.235
  • Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., & Patronov, A. (2020). REINVENT 2.0: An AI tool for de novo drug design. Journal of Chemical Information and Modeling, 60(12), 5918–5922. https://doi.org/10.1021/acs.jcim.0c00915
  • Blaschke, T., Engkvist, O., Bajorath, J., & Chen, H. (2020). Memory-assisted reinforcement learning for diverse molecular de novo design. Journal of Cheminformatics, 12(1), 1–17. https://doi.org/10.1186/s13321-020-00473-0
  • Bobrowski, T. M., Korn, D. R., Muratov, E. N., & Tropsha, A. (2021). ZINC express: A virtual assistant for purchasing compounds annotated in the zinc database. Journal of Chemical Information and Modeling, 61(3), 1033–1036. https://doi.org/10.1021/acs.jcim.0c01419
  • Boyles, F., Deane, C. M., & Morris, G. M. (2022). Learning from docked ligands: Ligand-based features rescue structure-based scoring functions when trained on docked poses. Journal of Chemical Information and Modeling, 62(22), 5329–5341. https://doi.org/10.1021/acs.jcim.1c00096
  • Brown, B. P., Mendenhall, J., Geanes, A. R., & Meiler, J. (2021). General purpose structure-based drug discovery neural network score functions with human-interpretable pharmacophore maps. Journal of Chemical Information and Modeling, 61(2), 603–620. https://doi.org/10.1021/acs.jcim.0c01001
  • Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019). GuacaMol: Benchmarking models for de novo molecular design. Journal of Chemical Information and Modeling, 59(3), 1096–1108. https://doi.org/10.1021/acs.jcim.8b00839
  • Cabrera-Andrade, A., López-Cortés, A., Jaramillo-Koupermann, G., González-Díaz, H., Pazos, A., Munteanu, C. R., Pérez-Castillo, Y., & Tejera, E. (2020). A multi-objective approach for anti-osteosarcoma cancer agents discovery through drug repurposing. Pharmaceuticals, 13(11), 409. https://doi.org/10.3390/ph13110409
  • Cappel, D., Mozziconacci, J.-C., Braun, T., & Steinbrecher, T. (2021). Performance of Relative Binding Free Energy Calculations on an Automatically Generated Dataset of Halogen–Deshalogen Matched Molecular Pairs. Journal of Chemical Information and Modeling, 61(7), 3421–3430. https://doi.org/10.1021/acs.jcim.1c00290
  • Cheng, T., Li, Q., Zhou, Z., Wang, Y., & Bryant, S. H. (2012). Structure-based virtual screening for drug discovery: A problem-centric review. The AAPS Journal, 14(1), 133–141. https://doi.org/10.1208/s12248-012-9322-0
  • Cihan Sorkun, M., Mullaj, D., Koelman, J. M. V. A., & Er, S. (2022). ChemPlot, a Python library for chemical space visualization. Chemistry‐Methods, 2(7), e202200005.
  • Costa, R. P. O., Lucena, L. F., Silva, L. M. A., Zocolo, G. J., Herrera-Acevedo, C., Scotti, L., Da-Costa, F. B., Ionov, N., Poroikov, V., Muratov, E. N., & Scotti, M. T. (2021). The SistematX Web portal of natural products: An update. Journal of Chemical Information and Modeling, 61(6), 2516–2522. https://doi.org/10.1021/acs.jcim.1c00083
  • Cumming, J. G., Davis, A. M., Muresan, S., Haeberlein, M., & Chen, H. (2013). Chemical predictive modelling to improve compound quality. Nature Reviews. Drug Discovery, 12(12), 948–962. https://doi.org/10.1038/nrd4128
  • Cunningham, F., Allen, J. E., Allen, J., Alvarez-Jarreta, J., Amode, M. R., Armean, I. M., Austine-Orimoloye, O., Azov, A. G., Barnes, I., Bennett, R., Berry, A., Bhai, J., Bignell, A., Billis, K., Boddu, S., Brooks, L., Charkhchi, M., Cummins, C., Da Rin Fioretto, L., … Flicek, P. (2022). Ensembl 2022. Nucleic Acids Research, 50(D1), D988–D995. https://doi.org/10.1093/nar/gkab1049
  • Daina, A., & Zoete, V. (2019). Application of the SwissDrugDesign online resources in virtual screening. International Journal of Molecular Sciences, 20(18), 4612. https://doi.org/10.3390/ijms20184612
  • Davies, M., Nowotka, M., Papadatos, G., Dedman, N., Gaulton, A., Atkinson, F., Bellis, L., & Overington, J. P. (2015). ChEMBL web services: Streamlining access to drug discovery data and utilities. Nucleic Acids Research, 43(W1), W612–W620. https://doi.org/10.1093/nar/gkv352
  • Davis, A. P., Wiegers, T. C., Johnson, R. J., Sciaky, D., Wiegers, J., & Mattingly, C. J. (2023). Comparative Toxicogenomics database (CTD): Update 2023. Nucleic Acids Research, 51(D1), D1257–D1262. https://doi.org/10.1093/nar/gkac833
  • Diallo, B., Glenister, M., Musyoka, T. M., Lobb, K., & Tastan Bishop, Ö. (2021). SANCDB: An update on South African natural compounds and their readily available analogs. Journal of Cheminformatics, 13(1), 1–14. https://doi.org/10.1186/s13321-021-00514-2
  • Diéguez-Santana, K., Casañola-Martin, G. M., Green, J. R., Rasulev, B., & González-Díaz, H. (2021). Predicting metabolic reaction networks with perturbation-theory machine learning (PTML) models. Current Topics in Medicinal Chemistry, 21(9), 819–827. https://doi.org/10.2174/1568026621666210331161144
  • Eberhardt, J., Santos-Martins, D., Tillack, A. F., & Forli, S. (2021). AutoDock Vina 1.2. 0: New docking methods, expanded force field, and python bindings. Journal of Chemical Information and Modeling, 61(8), 3891–3898. https://doi.org/10.1021/acs.jcim.1c00203
  • Falaguera, M. J., & Mestres, J. (2021). Identification of the core chemical structure in SureChEMBL patents. Journal of Chemical Information and Modeling, 61(5), 2241–2247. https://doi.org/10.1021/acs.jcim.1c00151
  • Fan, J., Fu, A., & Zhang, L. (2019). Progress in molecular docking. Quantitative Biology, 7(2), 83–89. https://doi.org/10.1007/s40484-019-0172-y
  • Ferreira, L. L. G., & Andricopulo, A. D. (2018). Chemoinformatics approaches to structure-and ligand-based drug design. Frontiers Media SA, 9, 1416.
  • Filimonov, D. A., Lagunin, A. A., Gloriozova, T. A., Rudik, A. V., Druzhilovskii, D. S., Pogodin, P. V., & Poroikov, V. V. (2014). Prediction of the biological activity spectra of organic compounds using the PASS online web resource. Chemistry of Heterocyclic Compounds, 50(3), 444–457. https://doi.org/10.1007/s10593-014-1496-1
  • Francoeur, P. G., & Koes, D. R. (2021). SolTranNet–A machine learning tool for fast aqueous solubility prediction. Journal of Chemical Information and Modeling, 61(6), 2530–2536. https://doi.org/10.1021/acs.jcim.1c00331
  • Galgonek, J., & Vondrášek, J. (2021). IDSM ChemWebRDF: SPARQLing small-molecule datasets. Journal of Cheminformatics, 13(1), 1–19. https://doi.org/10.1186/s13321-021-00515-1
  • Gallo, K., Goede, A., Eckert, A., Moahamed, B., Preissner, R., & Gohlke, B.-O. (2021). PROMISCUOUS 2.0: A resource for drug-repositioning. Nucleic Acids Research, 49(D1), D1373–D1380. https://doi.org/10.1093/nar/gkaa1061
  • Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., & Overington, J. P. (2012). ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Research, 40(Database issue), D1100–D1107. https://doi.org/10.1093/nar/gkr777
  • Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J., Mendez, D., Mutowo, P., Atkinson, F., Bellis, L. J., Cibrián-Uhalte, E., Davies, M., Dedman, N., Karlsson, A., Magariños, M. P., Overington, J. P., Papadatos, G., Smit, I., & Leach, A. R. (2017). The ChEMBL database in 2017. Nucleic Acids Research, 45(D1), D945–D954. https://doi.org/10.1093/nar/gkw1074
  • Gilson, M. K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L., & Chong, J. (2016). BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Research, 44(D1), D1045–D1053. https://doi.org/10.1093/nar/gkv1072
  • Gimadiev, T., Nugmanov, R., Batyrshin, D., Madzhidov, T., Maeda, S., Sidorov, P., & Varnek, A. (2021). Combined graph/relational database management system for calculated chemical reaction pathway data. Journal of Chemical Information and Modeling, 61(2), 554–559. https://doi.org/10.1021/acs.jcim.0c01280
  • Gong, J., Cai, C., Liu, X., Ku, X., Jiang, H., Gao, D., & Li, H. (2013). ChemMapper: A versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method. Bioinformatics (Oxford, England), 29(14), 1827–1829. https://doi.org/10.1093/bioinformatics/btt270
  • Gonzalez-Diaz, H. (n.d.). PTML: Perturbation-Theory Machine Learning notes, in Proceedings of the MOL2NET'18, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 4th ed., 15 January 2018–20 January 2019, MDPI: Basel, Switzerland. https://doi.org/10.3390/mol2net-04-05463
  • Goodman, J. M., Pletnev, I., Thiessen, P., Bolton, E., & Heller, S. R. (2021). InChI version 1.06: Now more than 99.99% reliable. Journal of Cheminformatics, 13(1), 1–8. https://doi.org/10.1186/s13321-021-00517-z
  • Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018). Empirical scoring functions for structure-based virtual screening: Applications, critical aspects, and challenges. Frontiers in Pharmacology, 9, 1089. https://doi.org/10.3389/fphar.2018.01089
  • Harrington, R. A., Adhikari, V., Rayner, M., & Scarborough, P. (2019). Nutrient composition databases in the age of big data: FoodDB, a comprehensive, real-time database infrastructure. BMJ Open, 9(6), e026652. https://doi.org/10.1136/bmjopen-2018-026652
  • Hastings, J., Owen, G., Dekker, A., Ennis, M., Kale, N., Muthukrishnan, V., Turner, S., Swainston, N., Mendes, P., & Steinbeck, C. (2016). ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Research, 44(D1), D1214–D1219. https://doi.org/10.1093/nar/gkv1031
  • Hatherley, R., Brown, D. K., Musyoka, T. M., Penkler, D. L., Faya, N., Lobb, K. A., & Özlem Tastan Bishop, S. (2015). a South African natural compound database. Journal of Cheminformatics, 7, 29. https://doi.org/10.1186/s13321-015-0080-8
  • Hu, F., Jiang, J., Wang, D., Zhu, M., & Yin, P. (2021). Multi-PLI: Interpretable multi‐task deep learning model for unifying protein–ligand interaction datasets. Journal of Cheminformatics, 13(1), 1–14. https://doi.org/10.1186/s13321-021-00510-6
  • Humer, C., Heberle, H., Montanari, F., Wolf, T., Huber, F., Henderson, R., Heinrich, J., & Streit, M. (2022). ChemInformatics Model Explorer (CIME): Exploratory analysis of chemical model explanations. Journal of Cheminformatics, 14(1), 1–14. https://doi.org/10.1186/s13321-022-00600-z
  • Irwin, J. J., & Shoichet, B. K. (2005). ZINC − A free database of commercially available compounds for virtual screening. Journal of Chemical Information and Modeling, 45(1), 177–182. https://doi.org/10.1021/ci049714+
  • Jain, S., Siramshetty, V. B., Alves, V. M., Muratov, E. N., Kleinstreuer, N., Tropsha, A., Nicklaus, M. C., Simeonov, A., & Zakharov, A. V. (2021). Large-scale modeling of multispecies acute toxicity end points using consensus of multitask deep learning methods. Journal of Chemical Information and Modeling, 61(2), 653–663. https://doi.org/10.1021/acs.jcim.0c01164
  • Jiang, J., Wang, R., & Wei, G.-W. (2021). GGL-Tox: Geometric graph learning for toxicity prediction. Journal of Chemical Information and Modeling, 61(4), 1691–1700. https://doi.org/10.1021/acs.jcim.0c01294
  • Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
  • Keshavarzi Arshadi, A., Salem, M., Firouzbakht, A., & Yuan, J. S. (2022). MolData, a molecular benchmark for disease and target based machine learning. Journal of Cheminformatics, 14(1), 1–18. https://doi.org/10.1186/s13321-022-00590-y
  • Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E. E. (2021). PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Research, 49(D1), D1388–D1395. https://doi.org/10.1093/nar/gkaa971
  • Kleandrova, V. V., & Speck-Planche, A. (2020). PTML modeling for alzheimer’s disease: Design and prediction of virtual multi-target inhibitors of GSK3B, HDAC1, and HDAC6. Current Topics in Medicinal Chemistry, 20(19), 1661–1676. https://doi.org/10.2174/1568026620666200607190951
  • Kleandrova, V. V., & Speck-Planche, A. (2022). PTML modeling for pancreatic cancer research: In silico design of simultaneous multi-protein and multi-cell inhibitors. Biomedicines, 10(2), 491. https://doi.org/10.3390/biomedicines10020491
  • Kleandrova, V. V., Rojas-Vargas, J. A., Scotti, M. T., & Speck-Planche, A. (2022). PTML modeling for peptide discovery: In silico design of non-hemolytic peptides with antihypertensive activity. Molecular Diversity, 26(5), 2523–2534. https://doi.org/10.1007/s11030-021-10350-z
  • Kleandrova, V. V., Scotti, L., Bezerra Mendonça Junior, F. J., Muratov, E., Scotti, M. T., & Speck-Planche, A. (2021). QSAR modeling for multi-target drug discovery: Designing simultaneous inhibitors of proteins in diverse pathogenic parasites. Frontiers in Chemistry, 9, 634663. https://doi.org/10.3389/fchem.2021.634663
  • Kleandrova, V. V., Scotti, M. T., & Speck-Planche, A. (2021). Computational drug repurposing for antituberculosis therapy: Discovery of multi-strain inhibitors. Antibiotics, 10(8), 1005. https://doi.org/10.3390/antibiotics10081005
  • Kleandrova, V. V., Scotti, M. T., Scotti, L., & Speck-Planche, A. (2021). Multi-target drug discovery via ptml modeling: Applications to the design of virtual dual inhibitors of cdk4 and her2. Current Topics in Medicinal Chemistry, 21(7), 661–675. https://doi.org/10.2174/1568026621666210119112845
  • Kleandrova, V. V., Scotti, M. T., Scotti, L., Nayarisseri, A., & Speck-Planche, A. (2020). Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines. SAR and QSAR in Environmental Research, 31(11), 815–836. https://doi.org/10.1080/1062936X.2020.1818617
  • Konc, J., Lesnik, S., Skrlj, B., & Janezic, D. (2021). ProBiS-Dock Database: A Web Server and interactive web repository of small ligand–protein binding sites for drug design. Journal of Chemical Information and Modeling, 61(8), 4097–4107. https://doi.org/10.1021/acs.jcim.1c00454
  • Korshunova, M., Ginsburg, B., Tropsha, A., & Isayev, O. (2021). OpenChem: A deep learning toolkit for computational chemistry and drug design. Journal of Chemical Information and Modeling, 61(1), 7–13. https://doi.org/10.1021/acs.jcim.0c00971
  • Kuhn, M., Szklarczyk, D., Pletscher-Frankild, S., Blicher, T. H., Von Mering, C., Jensen, L. J., & Bork, P. (2014). STITCH 4: Integration of protein–chemical interactions with user data. Nucleic Acids Research, 42(Database issue), D401–D407. https://doi.org/10.1093/nar/gkt1207
  • Kumar, S., & Kim, M-h (2021). SMPLIP-Score: Predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors. Journal of Cheminformatics, 13(1), 1–17. https://doi.org/10.1186/s13321-021-00507-1
  • Kwon, Y., & Lee, J. (2021). MolFinder: An evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES. Journal of Cheminformatics, 13(1), 14. https://doi.org/10.1186/s13321-021-00501-7
  • Lee, J. W., Maria-Solano, M. A., Vu, T. N. L., Yoon, S., & Choi, S. (2022). Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD). Biochemical Society Transactions, 50(1), 241–252. https://doi.org/10.1042/BST20211240
  • Lim, H., & Jung, Y. (2021). MLSolvA: Solvation free energy prediction from pairwise atomistic interactions by machine learning. Journal of Cheminformatics, 13(1), 1–10. https://doi.org/10.1186/s13321-021-00533-z
  • Lo, Y.-C., Rensi, S. E., Torng, W., & Altman, R. B. (2018). Machine learning in chemoinformatics and drug discovery. Drug Discovery Today. 23(8), 1538–1546. https://doi.org/10.1016/j.drudis.2018.05.010
  • López-López, E., Cerda-García-Rojas, C. M., & Medina-Franco, J. L. (2021). Tubulin inhibitors: A chemoinformatic analysis using cell-based data. Molecules, 26(9), 2483. https://doi.org/10.3390/molecules26092483
  • Lowe, C. N., & Williams, A. J. (2021). Enabling high-throughput searches for multiple chemical data using the US-EPA CompTox chemicals dashboard. Journal of Chemical Information and Modeling, 61(2), 565–570. https://doi.org/10.1021/acs.jcim.0c01273
  • Lu, J., Xia, S., Lu, J., & Zhang, Y. (2021). Dataset Construction to Explore Chemical Space with 3D Geometry and Deep Learning. Journal of Chemical Information and Modeling, 61(3), 1095–1104. https://doi.org/10.1021/acs.jcim.1c00007
  • Lyu, C., Chen, T., Qiang, B., Liu, N., Wang, H., Zhang, L., & Liu, Z. (2021). CMNPD: A comprehensive marine natural products database towards facilitating drug discovery from the ocean. Nucleic Acids Research, 49(D1), D509–D515. https://doi.org/10.1093/nar/gkaa763
  • Lyu, S., Zhao, Y., Zeng, X., Chen, X., Meng, Q., Ding, Z., Zhao, W., Qi, Y., Gao, Y., & Du, J. (2021). Identification of phelligridin-based compounds as novel human CD73 inhibitors. Journal of Chemical Information and Modeling, 61(3), 1275–1286. https://doi.org/10.1021/acs.jcim.0c00961
  • Ma, B., Terayama, K., Matsumoto, S., Isaka, Y., Sasakura, Y., Iwata, H., Araki, M., & Okuno, Y. (2021). Structure-based de novo molecular generator combined with artificial intelligence and docking simulations. Journal of Chemical Information and Modeling, 61(7), 3304–3313. https://doi.org/10.1021/acs.jcim.1c00679
  • Mahanta, P., Ahmed, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2012). An effective method for network module extraction from microarray data. BMC Bioinformatics, 13(S13), 1–11. https://doi.org/10.1186/1471-2105-13-S13-S4
  • Mamada, H., Nomura, Y., & Uesawa, Y. (2021). Prediction model of clearance by a Novel quantitative structure–Activity relationship approach, combination deepsnap-deep learning and conventional machine learning. ACS Omega,.6(36), 23570–23577. https://doi.org/10.1021/acsomega.1c03689
  • Mariia, M., & Pavel, P. (2021). Benchmarks for interpretation of QSAR models. Journal of Cheminformatics, 13, 41. https://doi.org/10.1186/s13321-021-00519-x
  • Martinez-Mayorga, K., Madariaga-Mazon, A., Medina-Franco, J. L., & Maggiora, G. (2020). The impact of chemoinformatics on drug discovery in the pharmaceutical industry. Expert Opinion on Drug Discovery, 15(3), 293–306. https://doi.org/10.1080/17460441.2020.1696307
  • Massarotti, A. (2021). Investigation of the click-chemical space for drug design using ZINClick. In Protein-ligand interactions and drug design (pp. 3–10). Springer.
  • Massarotti, A., Brunco, A., Sorba, G., & Tron, G. C. (2014). ZINClick: A database of 16 million novel, patentable, and readily synthesizable 1, 4-disubstituted triazoles. Journal of Chemical Information and Modeling, 54(2), 396–406. https://doi.org/10.1021/ci400529h
  • Masters, L., Eagon, S., & Heying, M. (2020). Evaluation of consensus scoring methods for AutoDock Vina, smina and idock. Journal of Molecular Graphics & Modelling, 96, 107532. https://doi.org/10.1016/j.jmgm.2020.107532
  • McNutt, A. T., Francoeur, P., Aggarwal, R., Masuda, T., Meli, R., Ragoza, M., Sunseri, J., & Koes, D. R. (2021). GNINA 1.0: Molecular docking with deep learning. Journal of Cheminformatics, 13(1), 1–20. https://doi.org/10.1186/s13321-021-00522-2
  • Mehta, S., Laghuvarapu, S., Pathak, Y., Sethi, A., Alvala, M., & Priyakumar, U. D. (2021). MEMES: Machine learning framework for Enhanced MolEcular Screening. Chemical Science, 12(35), 11710–11721. https://doi.org/10.1039/d1sc02783b
  • Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2022). ColabFold: Making protein folding accessible to all. Nature Methods, 19(6), 679–682. https://doi.org/10.1038/s41592-022-01488-1
  • Mitchell, J. B. O. (2014). Machine learning methods in chemoinformatics. Wiley Interdisciplinary Reviews. Computational Molecular Science, 4(5), 468–481. https://doi.org/10.1002/wcms.1183
  • Mysinger, M. M., Carchia, M., Irwin, J. J., & Shoichet, B. K. (2012). Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. Journal of Medicinal Chemistry, 55(14), 6582–6594. https://doi.org/10.1021/jm300687e
  • Nayarisseri, A., Khandelwal, R., Tanwar, P., Madhavi, M., Sharma, D., Thakur, G., Speck-Planche, A., & Singh, S. K. (2021). Artificial intelligence, big data and machine learning approaches in precision medicine & drug discovery. Current Drug Targets, 22(6), 631–655. https://doi.org/10.2174/1389450122999210104205732
  • Neveu, V., Moussy, A., Rouaix, H., Wedekind, R., Pon, A., Knox, C., Wishart, D. S., & Scalbert, A. (2016). Exposome-Explorer: A manually-curated database on biomarkers of exposure to dietary and environmental factors. Nucleic Acids Research, 45(D1), gkw980.
  • Ntie-Kang, F., Nwodo, J. N., Ibezim, A., Simoben, C. V., Karaman, B., Ngwa, V. F., Sippl, W., Adikwu, M. U., & Mbaze, L. (2014). Molecular modeling of potential anticancer agents from African medicinal plants. Journal of Chemical Information and Modeling, 54(9), 2433–2450. https://doi.org/10.1021/ci5003697
  • Ntie-Kang, F., Zofou, D., Babiaka, S. B., Meudom, R., Scharfe, M., Lifongo, L. L., Mbah, J. A., Mbaze, L., Sippl, W., & Efange, S. M. N. (2013). AfroDb: A select highly potent and diverse natural product library from African medicinal plants. PLoS One, 8(10), e78085. https://doi.org/10.1371/journal.pone.0078085
  • Onguéné, P. A., Ntie-Kang, F., Mbah, J. A., Lifongo, L. L., Ndom, J. C., Sippl, W., & Mbaze, L. M. (2014). The potential of anti-malarial compounds derived from African medicinal plants, part III: An in silico evaluation of drug metabolism and pharmacokinetics profiling. Organic and Medicinal Chemistry Letters, 4(1), 1–9. https://doi.org/10.1186/s13588-014-0006-x
  • Ortega-Tenezaca, B., & González-Díaz, H. (2021). IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks. Nanoscale, 13(2), 1318–1330. https://doi.org/10.1039/d0nr07588d
  • Papadatos, G., Davies, M., Dedman, N., Chambers, J., Gaulton, A., Siddle, J., Koks, R., Irvine, S. A., Pettersson, J., Goncharoff, N., Hersey, A., & Overington, J. P. (2016). SureChEMBL: A large-scale, chemically annotated patent document database. Nucleic Acids Research, 44(D1), D1220–D1228. https://doi.org/10.1093/nar/gkv1253
  • Pastor, M., Gómez-Tamayo, J. C., & Sanz, F. (2021). Flame: An open source framework for model development, hosting, and usage in production environments. Journal of Cheminformatics, 13(1), 1–15. https://doi.org/10.1186/s13321-021-00509-z
  • Pérez-Sánchez, H., den-Haan, H., Peña-García, J., Lozano-Sánchez, J., Martínez Moreno, M. E., Sánchez-Pérez, A., Muñoz, A., Ruiz-Espinosa, P., Pereira, A. S. P., Katsikoudi, A., Gabaldón Hernández, J. A., Stojanovic, I., Carretero, A. S., & Tzakos, A. G. (2020). DIA-DB: A database and web server for the prediction of diabetes drugs. Journal of Chemical Information and Modeling, 60(9), 4124–4130. https://doi.org/10.1021/acs.jcim.0c00107
  • Pires, D. E. V., Blundell, T. L., & Ascher, D. B. (2015). pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 58(9), 4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104
  • Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., Kadurin, A., Johansson, S., Chen, H., Nikolenko, S., Aspuru-Guzik, A., & Zhavoronkov, A. (2020). Molecular sets (MOSES): A benchmarking platform for molecular generation models. Frontiers in Pharmacology, 11, 565644. https://doi.org/10.3389/fphar.2020.565644
  • Puertas-Martín, S., Banegas-Luna, A. J., Paredes-Ramos, M., Redondo, J. L., Ortigosa, P. M., Brovarets, O., & Pérez-Sánchez, H. (2020). Is high performance computing a requirement for novel drug discovery and how will this impact academic efforts? Expert Opinion on Drug Discovery, 15(9), 981–986. https://doi.org/10.1080/17460441.2020.1758664
  • Rajan, K., Zielesny, A., & Steinbeck, C. (2021). DECIMER 1.0: Deep learning for chemical image recognition using transformers. Journal of Cheminformatics, 13(1), 1–16. https://doi.org/10.1186/s13321-021-00538-8
  • Rajkishan, T., Rachana, A., Shruti, S., Bhumi, P., & Patel, D. (2021). Computer-aided drug designing. In Advances in bioinformatics (pp. 151–182). Springer.
  • Reimers, J. R., Sajid, A., Kobayashi, R., & Ford, M. J. (2018). Understanding and calibrating density-functional-theory calculations describing the energy and spectroscopy of defect sites in hexagonal boron nitride. Journal of Chemical Theory and Computation, 14(3), 1602–1613. https://doi.org/10.1021/acs.jctc.7b01072
  • Richard, A. M., Huang, R., Waidyanatha, S., Shinn, P., Collins, B. J., Thillainadarajah, I., Grulke, C. M., Williams, A. J., Lougee, R. R., Judson, R. S., Houck, K. A., Shobair, M., Yang, C., Rathman, J. F., Yasgar, A., Fitzpatrick, S. C., Simeonov, A., Thomas, R. S., Crofton, K. M., … Tice, R. R. (2021). The Tox21 10K compound library: Collaborative chemistry advancing toxicology. Chemical Research in Toxicology, 34(2), 189–216. https://doi.org/10.1021/acs.chemrestox.0c00264
  • Rozemberczki, B., Hoyt, C. T., Gogleva, A., Grabowski, P., Karis, K., Lamov, A., Nikolov, A., Nilsson, S., Ughetto, M., & Wang, Y. (2022). ChemicalX: A deep learning library for drug pair scoring, arXiv preprint arXiv:2202.05240 (pp. 3819–3828). https://doi.org/10.1145/3534678.3539023
  • Sabe, V. T., Ntombela, T., Jhamba, L. A., Maguire, G. E. M., Govender, T., Naicker, T., & Kruger, H. G. (2021). Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. European Journal of Medicinal Chemistry, 224, 113705. https://doi.org/10.1016/j.ejmech.2021.113705
  • Sacha, M., Błaż, M., Byrski, P., Dąbrowski-Tumański, P., Chromiński, M., Loska, R., Włodarczyk-Pruszyński, P., & Jastrzębski, S. (2021). Molecule edit graph attention network: Modeling chemical reactions as sequences of graph edits. Journal of Chemical Information and Modeling, 61(7), 3273–3284. https://doi.org/10.1021/acs.jcim.1c00537
  • Saldívar-González, F. I., Huerta-García, C. S., & Medina-Franco, J. L. (2020). Chemoinformatics-based enumeration of chemical libraries: A tutorial. Journal of Cheminformatics, 12(1), 1–25. https://doi.org/10.1186/s13321-020-00466-z
  • Sam, E., & Athri, P. (2019). Web-based drug repurposing tools: A survey. Briefings in Bioinformatics, 20(1), 299–316. https://doi.org/10.1093/bib/bbx125
  • Sampaio-Dias, I. E., Rodríguez-Borges, J. E., Yáñez-Pérez, V., Arrasate, S., Llorente, J., Brea, J. M., Bediaga, H., Viña, D., Loza, M. I., Caamaño, O., García-Mera, X., & González-Díaz, H. (2021). Synthesis, pharmacological, and biological evaluation of 2-furoyl-based MIF-1 peptidomimetics and the development of a general-purpose model for allosteric modulators (ALLOPTML). ACS Chemical Neuroscience, 12(1), 203–215. https://doi.org/10.1021/acschemneuro.0c00687
  • Santana, R., Zuluaga, R., Ganan, P., Arrasate, S., Onieva Caracuel, E., & Gonzalez-Diaz, H. (2020). PTML model of ChEMBL compounds assays for vitamin derivatives. ACS Combinatorial Science, 22(3), 129–141. https://doi.org/10.1021/acscombsci.9b00166
  • Scotti, M. T., Herrera-Acevedo, C., Oliveira, T. B., Costa, R. P. O., Santos, S., Rodrigues, R. P., Scotti, L., & Da-Costa, F. B. (2018). SistematX, an online web-based cheminformatics tool for data management of secondary metabolites. Molecules, 23(1), 103. https://doi.org/10.3390/molecules23010103
  • Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., Penedones, H., Petersen, S., Simonyan, K., Crossan, S., Kohli, P., Jones, D. T., Silver, D., Kavukcuoglu, K., & Hassabis, D. (2019). Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13. Proteins, 87(12), 1141–1148. https://doi.org/10.1002/prot.25834
  • Shah, P., Kendall, F., Khozin, S., Goosen, R., Hu, J., Laramie, J., Ringel, M., & Schork, N. (2019). Artificial intelligence and machine learning in clinical development: A translational perspective. NPJ Digital Medicine, 2(1), 69. https://doi.org/10.1038/s41746-019-0148-3
  • Sharma, A., Kumar, R., Ranjta, S., & Varadwaj, P. K. (2021). SMILES to smell: Decoding the structure–odor relationship of chemical compounds using the deep neural network approach. Journal of Chemical Information and Modeling, 61(2), 676–688. https://doi.org/10.1021/acs.jcim.0c01288
  • Shukla, A., Upadhyai, P., Shah, J., Neethukrishna, K., Bielas, S., & Girisha, K. M. (2017). Autosomal recessive spinocerebellar ataxia 20: Report of a new patient and review of literature. European Journal of Medical Genetics, 60(2), 118–123. https://doi.org/10.1016/j.ejmg.2016.11.006
  • Shuvo, M. H., Gulfam, M., & Bhattacharya, D. (2021). DeepRefiner: High-accuracy protein structure refinement by deep network calibration. Nucleic Acids Research, 49(W1), W147–W152. https://doi.org/10.1093/nar/gkab361
  • Singh, D. B. (2020). Computer-aided drug design. Springer.
  • Sorokina, M., Merseburger, P., Rajan, K., Yirik, M. A., & Steinbeck, C. (2021). COCONUT online: Collection of open natural products database. Journal of Cheminformatics, 13(1), 2. https://doi.org/10.1186/s13321-020-00478-9
  • Speck-Planche, A., & Cordeiro, M. N. D. S. (2017). De novo computational design of compounds virtually displaying potent antibacterial activity and desirable in vitro ADMET profiles. Medicinal Chemistry Research, 26(10), 2345–2356. https://doi.org/10.1007/s00044-017-1936-4
  • Speck-Planche, A., & Kleandrova, V. V. (2022). Multi-condition QSAR model for the virtual design of chemicals with dual pan-antiviral and anti-cytokine storm profiles. ACS Omega,.7(36), 32119–32130. https://doi.org/10.1021/acsomega.2c03363
  • Speck-Planche, A., & Scotti, M. T. (2019). BET bromodomain inhibitors: Fragment-based in silico design using multi-target QSAR models. Molecular Diversity, 23(3), 555–572. https://doi.org/10.1007/s11030-018-9890-8
  • Speck-Planche, A., Kleandrova, V. V., & Scotti, M. T. (2021). In Silico drug repurposing for anti-inflammatory therapy: Virtual search for dual inhibitors of Caspase-1 and TNF-Alpha. Biomolecules, 11(12), 1832. https://doi.org/10.3390/biom11121832
  • Stein, R. M., Yang, Y., Balius, T. E., O’Meara, M. J., Lyu, J., Young, J., Tang, K., Shoichet, B. K., & Irwin, J. J. (2021). Property-unmatched decoys in docking benchmarks. Journal of Chemical Information and Modeling, 61(2), 699–714. https://doi.org/10.1021/acs.jcim.0c00598
  • Su, M., Yang, Q., Du, Y., Feng, G., Liu, Z., Li, Y., & Wang, R. (2019). Comparative assessment of scoring functions: The CASF-2016 update. Journal of Chemical Information and Modeling, 59(2), 895–913. https://doi.org/10.1021/acs.jcim.8b00545
  • Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., Lahr, D. L., Hirschman, J. E., Liu, Z., Donahue, M., Julian, B., Khan, M., Wadden, D., Smith, I. C., Lam, D., … Golub, T. R. (2017). A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell, 171(6), 1437–1452.e17. https://doi.org/10.1016/j.cell.2017.10.049
  • Tanchuk, V. Y., Tanin, V. O., Vovk, A. I., & Poda, G. (2016). A new, improved hybrid scoring function for molecular docking and scoring based on AutoDock and AutoDock Vina. Chemical Biology & Drug Design, 87(4), 618–625. https://doi.org/10.1111/cbdd.12697
  • Tang, J., Tanoli, Z.-U.-R., Ravikumar, B., Alam, Z., Rebane, A., Vähä-Koskela, M., Peddinti, G., van Adrichem, A. J., Wakkinen, J., Jaiswal, A., Karjalainen, E., Gautam, P., He, L., Parri, E., Khan, S., Gupta, A., Ali, M., Yetukuri, L., Gustavsson, A.-L., … Aittokallio, T. (2018). Drug target commons: A community effort to build a consensus knowledge base for drug-target interactions. Cell Chemical Biology, 25(2), 224–229.e2. https://doi.org/10.1016/j.chembiol.2017.11.009
  • Tanoli, Z., Alam, Z., Ianevski, A., Wennerberg, K., Vähä-Koskela, M., & Aittokallio, T. (2020). Interactive visual analysis of drug–target interaction networks using drug target profiler, with applications to precision medicine and drug repurposing. Briefings in Bioinformatics, 21(1), 211–220.
  • Tanoli, Z., Aldahdooh, J., Alam, F., Wang, Y., Seemab, U., Fratelli, M., Pavlis, P., Hajduch, M., Bietrix, F., Gribbon, P., Zaliani, A., Hall, M. D., Shen, M., Brimacombe, K., Kulesskiy, E., Saarela, J., Wennerberg, K., Vähä-Koskela, M., & Tang, J. (2022). Minimal information for chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments. Briefings in Bioinformatics, 23(1), bbab350. https://doi.org/10.1093/bib/bbab350
  • Tanoli, Z., Seemab, U., Scherer, A., Wennerberg, K., Tang, J., & Vähä-Koskela, M. (2021). Exploration of databases and methods supporting drug repurposing: A comprehensive survey. Briefings in Bioinformatics, 22(2), 1656–1678. https://doi.org/10.1093/bib/bbaa003
  • Terfloth, L., Spycher, S., & Gasteiger, J. (2018). Drug discovery: An overview. In E. Thomas & J. Gasteiger (Eds.), Applied chemoinformatics: Achievements and future opportunities. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA.
  • Tetko, I. V., & Engkvist, O. (2020). From big data to artificial intelligence: Chemoinformatics meets new challenges (pp. 1–3). Springer.
  • Thakkar, A., Johansson, S., Jorner, K., Buttar, D., Reymond, J.-L., & Engkvist, O. (2021). Artificial intelligence and automation in computer aided synthesis planning. Reaction Chemistry & Engineering, 6(1), 27–51. https://doi.org/10.1039/D0RE00340A
  • Thomas, M., Smith, R. T., O’Boyle, N. M., de Graaf, C., & Bender, A. (2021). Comparison of structure-and ligand-based scoring functions for deep generative models: A GPCR case study. Journal of Cheminformatics, 13(1), 1–20. https://doi.org/10.1186/s13321-021-00516-0
  • Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva, F. P. Jr, (2019). Key topics in molecular docking for drug design. International Journal of Molecular Sciences, 20(18), 4574. https://doi.org/10.3390/ijms20184574
  • Trott, O., & Olson, A. J. (2010). Vina AutoDock, Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem., 31(2), 455-61. https://doi.org/10.1002/jcc.21334. PMID: 19499576; PMCID: PMC3041641.
  • Ucak, U. V., Kang, T., Ko, J., & Lee, J. (2021). Substructure-based neural machine translation for retrosynthetic prediction. Journal of Cheminformatics, 13(1), 1–15. https://doi.org/10.1186/s13321-020-00482-z
  • UniProt. (2021). UniProt: The universal protein knowledgebase in 2021. Nucleic Acids Research, 49(D1), D480–D489.
  • Vásquez-Domínguez, E., Armijos-Jaramillo, V. D., Tejera, E., & González-Díaz, H. (2019). Multioutput perturbation-theory machine learning (PTML) model of ChEMBL data for antiretroviral compounds. Molecular Pharmaceutics, 16(10), 4200–4212. https://doi.org/10.1021/acs.molpharmaceut.9b00538
  • Von Eichborn, J., Murgueitio, M. S., Dunkel, M., Koerner, S., Bourne, P. E., & Preissner, R. (2011). PROMISCUOUS: A database for network-based drug-repositioning. Nucleic Acids Research, 39(Database issue), D1060–D1066. https://doi.org/10.1093/nar/gkq1037
  • Wang, Y., Aldahdooh, J., Hu, Y., Yang, H., Vähä-Koskela, M., Tang, J., & Tanoli, Z. (2022). DrugRepo: A novel approach to repurposing drugs based on chemical and genomic features. Scientific Reports, 12(1), 21116. https://doi.org/10.1038/s41598-022-24980-2
  • Weston, A. D., & Hood, L. (2004). Systems biology, proteomics, and the future of health care: Toward predictive, preventative, and personalized medicine. Journal of Proteome Research, 3(2), 179–196. https://doi.org/10.1021/pr0499693
  • Williams, A. J., Grulke, C. M., Edwards, J., McEachran, A. D., Mansouri, K., Baker, N. C., Patlewicz, G., Shah, I., Wambaugh, J. F., Judson, R. S., & Richard, A. M. (2017). The CompTox Chemistry Dashboard: A community data resource for environmental chemistry. Journal of Cheminformatics, 9(1), 1–27. https://doi.org/10.1186/s13321-017-0247-6
  • Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., Assempour, N., Iynkkaran, I., Liu, Y., Maciejewski, A., Gale, N., Wilson, A., Chin, L., Cummings, R., Le, D., … Wilson, M. (2018). Z. Sayeeda, DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082. https://doi.org/10.1093/nar/gkx1037
  • Xiao, X., Min, J.-L., Lin, W.-Z., Liu, Z., Cheng, X., & Chou, K.-C. (2015). iDrug-target: Predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. Journal of Biomolecular Structure & Dynamics, 33(10), 2221–2233. https://doi.org/10.1080/07391102.2014.998710
  • 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. (2021). ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research, 49(W1), W5–W14. https://doi.org/10.1093/nar/gkab255
  • Yang, C., & Zhang, Y. (2021). Lin_F9: A linear empirical scoring function for protein–ligand docking. Journal of Chemical Information and Modeling, 61(9), 4630–4644. https://doi.org/10.1021/acs.jcim.1c00737
  • Ye, H., Ye, L., Kang, H., Zhang, D., Tao, L., Tang, K., Liu, X., Zhu, R., Liu, Q., Chen, Y. Z., Li, Y., & Cao, Z. (2011). HIT: Linking herbal active ingredients to targets. Nucleic Acids Research, 39(Database issue), D1055–D1059. https://doi.org/10.1093/nar/gkq1165
  • Yevtushenko, P., Goubergrits, L., Gundelwein, L., Setio, A., Ramm, H., Lamecker, H., Heimann, T., Meyer, A., Kuehne, T., & Schafstedde, M. (2022). Deep learning based centerline-aggregated aortic hemodynamics: An efficient alternative to numerical modeling of hemodynamics. IEEE Journal of Biomedical and Health Informatics, 26(4), 1815–1825. https://doi.org/10.1109/JBHI.2021.3116764
  • Zabolotna, Y., Ertl, P., Horvath, D., Bonachera, F., Marcou, G., & Varnek, A. (2021). NP navigator: A new look at the natural product chemical space. Molecular Informatics, 40(9), 2100068. https://doi.org/10.1002/minf.202100068
  • Zhang, D., Tian, Y., Tian, Y., Xing, H., Liu, S., Zhang, H., Ding, S., Cai, P., Sun, D., Zhang, T., Hong, Y., Dai, H., Tu, W., Chen, J., Wu, A., & Hu, Q.-N. (2021). A data-driven integrative platform for computational prediction of toxin biotransformation with a case study. Journal of Hazardous Materials, 408, 124810. https://doi.org/10.1016/j.jhazmat.2020.124810
  • Zhu, Z., Shi, C., Zhang, Z., Liu, S., Xu, M., Yuan, X., Zhang, Y., Chen, J., Cai, H., & Lu, J. (2022). Torchdrug: A powerful and flexible machine learning platform for drug discovery, arXiv preprint arXiv:2202.08320.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.