Kobe, B. & Kemp, B. E. Principles of kinase regulation. Handb. Cell Signal. 2/e 2, 559–563 (2010).
Google Scholar
Bhullar, K. S. et al. Kinase-targeted cancer therapies: Progress, challenges and future directions. Mol. Cancer 17, 1–20. https://doi.org/10.1186/s12943-018-0804-2 (2018).
Google Scholar
Fagiani, F., Lanni, C., Racchi, M. & Govoni, S. Targeting dementias through cancer kinases inhibition. Alzheimer’s and dementia. Transl. Res. Clin. Interv. 6, e12044 (2020).
Cohen, P., Cross, D. & Jänne, P. A. Kinase drug discovery 20 years after imatinib: Progress and future directions. Nat. Rev. Drug Discov. 20, 551–569 (2021).
Google Scholar
Roskoski, R. Properties of FDA-approved small molecule protein kinase inhibitors: A 2023 update. Pharmacol. Res. 187, 106552 (2023).
Google Scholar
Ebied, A. M., Elmariah, H. & Cooper-DeHoff, R. M. New drugs approved in 2020. Am. J. Med. 134, 1096–1100 (2021).
Google Scholar
Ebied, A. M., Elmariah, H. & Cooper-DeHoff, R. M. New drugs approved in 2022. Am. J. Med. https://doi.org/10.1016/J.AMJMED.2023.02.019 (2023).
Google Scholar
Ebied, A. M., Elmariah, H. & Cooper-DeHoff, R. M. New drugs approved in 2021. Am. J. Med. 135, 836–839 (2022).
Google Scholar
Li, Y. H. et al. The human kinome targeted by FDA approved multi-target drugs and combination products: A comparative study from the drug-target interaction network perspective. PLoS ONE 11, e0165737 (2016).
Google Scholar
Csermely, P., Ágoston, V. & Pongor, S. The efficiency of multi-target drugs: The network approach might help drug design. Trends Pharmacol. Sci. 26, 178–182 (2005).
Google Scholar
Harrison, C. Analysing kinase inhibitor selectivity. Nat. Rev. Drug Discov. 11, 21–21 (2012).
White, P. T. & Cohen, M. S. The discovery and development of sorafenib for the treatment of thyroid cancer. Expert Opin. Drug Discov. 10, 427–439. https://doi.org/10.1517/17460441.2015.1006194 (2015).
Google Scholar
Li, X. et al. Deep learning enhancing kinome-wide polypharmacology profiling: Model construction and experiment validation. J. Med. Chem. 63, 8723–8737 (2020).
Google Scholar
Bao, L. et al. Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach. Acta Pharm. Sin. B 13, 54–67 (2022).
Google Scholar
Li, Z. et al. KinomeX: A web application for predicting kinome-wide polypharmacology effect of small molecules. Bioinformatics 35, 5354–5356 (2019).
Google Scholar
Abbasi, K. et al. DeepCDA: Deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks. Bioinformatics 36, 4633–4642 (2020).
Google Scholar
Merget, B., Turk, S., Eid, S., Rippmann, F. & Fulle, S. Profiling prediction of kinase inhibitors: Toward the virtual assay. J. Med. Chem. 60, 474–485 (2017).
Google Scholar
De Simone, G., Sardina, D. S., Gulotta, M. R. & Perricone, U. KUALA: A machine learning-driven framework for kinase inhibitors repositioning. Sci. Rep. 12, 1–16 (2022).
Google Scholar
Ponzoni, I. et al. QSAR classification models for predicting the activity of inhibitors of beta-secretase (BACE1) associated with Alzheimer’s disease. Sci. Rep. 9, 1–13 (2019).
Google Scholar
Blanco, J. L., Porto-Pazos, A. B., Pazos, A. & Fernandez-Lozano, C. Prediction of high anti-angiogenic activity peptides in silico using a generalized linear model and feature selection. Sci. Rep. 8, 1–11 (2018).
Google Scholar
Ma, X. H. et al. Virtual screening of selective multitarget kinase inhibitors by combinatorial support vector machines. Mol. Pharm. 7, 1545–1560 (2010).
Google Scholar
Jiang, Y. et al. Developing a Naïve Bayesian classification model with PI3Kγ structural features for virtual screening against PI3Kγ: Combining molecular docking and pharmacophore based on multiple PI3Kγ conformations. Eur. J. Med. Chem. 244, 114824 (2022).
Google Scholar
Hao, M., Li, Y., Wang, Y. & Zhang, S. Prediction of PKCθ inhibitory activity using the random forest algorithm. Int. J. Mol. Sci. 11, 3413–3433 (2010).
Google Scholar
Rohani, N. & Eslahchi, C. Drug–drug interaction predicting by neural network using integrated similarity. Sci. Rep. 9, 1–11 (2019).
Google Scholar
Vijay, S. & Gujral, T. S. Non-linear deep neural network for rapid and accurate prediction of phenotypic responses to kinase inhibitors. iScience 23, 101129 (2020).
Google Scholar
Yang, M. et al. Machine learning models based on molecular fingerprints and an extreme gradient boosting method lead to the discovery of JAK2 inhibitors. J. Chem. Inf. Model 59, 5002–5012 (2019).
Google Scholar
Born, J., Huynh, T., Stroobants, A., Cornell, W. D. & Manica, M. Active site sequence representations of human kinases outperform full sequence representations for affinity prediction and inhibitor generation: 3D effects in a 1D model. J. Chem. Inf. Model 62, 240–257 (2022).
Google Scholar
Shim, J., Hong, Z.-Y., Sohn, I. & Hwang, C. Prediction of drug–target binding affinity using similarity-based convolutional neural network. Sci. Rep. 11, 4416 (2021).
Google Scholar
Lin, X.-Y. et al. Identification of pan-kinase-family inhibitors using graph convolutional networks to reveal family-sensitive pre-moieties. BMC Bioinform. 23, 247 (2022).
Google Scholar
Sakai, M. et al. Prediction of pharmacological activities from chemical structures with graph convolutional neural networks. Sci. Rep. 11, 1–14 (2021).
Google Scholar
Karimi, M., Wu, D., Wang, Z. & Shen, Y. DeepAffinity: Interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks. Bioinformatics 35, 3329–3338 (2019).
Google Scholar
Deng, L., Zeng, Y., Liu, H., Liu, Z. & Liu, X. DeepMHADTA: Prediction of drug-target binding affinity using multi-head self-attention and convolutional neural network. Curr. Issues Mol. Biol. 44, 2287–2299 (2022).
Google Scholar
Park, H., Brahma, R., Shin, J. M. & Cho, K. H. Prediction of human cytochrome P450 inhibition using bio-selectivity induced deep neural network. Bull. Korean Chem. Soc. 43, 261–269 (2022).
Google Scholar
Lee, I., Keum, J. & Nam, H. DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput. Biol. 15, e1007129–e1007129 (2019).
Google Scholar
Soh, J., Park, S. & Lee, H. HIDTI: Integration of heterogeneous information to predict drug-target interactions. Sci. Rep. 12, 1–12 (2022).
Google Scholar
Li, Q. et al. PLA-MoRe: A protein-ligand binding affinity prediction model via comprehensive molecular representations. J. Chem. Inf. Model 62, 4380–4390 (2022).
Google Scholar
Modi, V. & Dunbrack, R. L. A structurally-validated multiple sequence alignment of 497 human protein kinase domains. Sci. Rep. 9, 1–16 (2019).
Google Scholar
Liu, T., Lin, Y., Wen, X., Jorissen, R. N. & Gilson, M. K. BindingDB: A web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res. 35, D198–D201 (2007).
Google Scholar
Tanoli, Z. R. et al. Drug target commons 2.0: A community platform for systematic analysis of drug–target interaction profiles. Database 2018, 1–13 (2018).
Google Scholar
Metz, J. T. et al. Navigating the kinome. Nat. Chem. Biol. 7(4), 200–202 (2011).
Google Scholar
Liu, T., Lin, Y., Wen, X., Jorissen, R. N. & Gilson, M. K. BindingDB: A web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res 35, D198–D201 (2007).
Google Scholar
RDKit. Preprint at https://www.rdkit.org/.
Ghose, A. K., Pritchett, A. & Crippen, G. M. Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships III: Modeling hydrophobic interactions. J. Comput. Chem. 9, 80–90 (1988).
Google Scholar
Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 46, 3–26 (2001).
Google Scholar
Durant, J. L., Leland, B. A., Henry, D. R. & Nourse, J. G. Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42, 1273–1280 (2002).
Google Scholar
Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model 50, 742–754 (2010).
Google Scholar
Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings. J. Chem. Inf. Model 61, 3891–3898 (2021).
Google Scholar
Xu, M., Shen, C., Yang, J., Wang, Q. & Huang, N. Systematic investigation of docking failures in large-scale structure-based virtual screening. ACS Omega 7, 39417–39428 (2022).
Google Scholar
Forli, S. et al. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc. 11, 905 (2016).
Google Scholar
Bland, J. M. & Altman, D. G. The odds ratio. BMJ 320, 1468 (2000).
Google Scholar
Eid, S., Turk, S., Volkamer, A., Rippmann, F. & Fulle, S. Kinmap: A web-based tool for interactive navigation through human kinome data. BMC Bioinform. 18, 1–6 (2017).
Google Scholar
Nguyen, N.-Q., Jang, G., Kim, H. & Kang, J. Perceiver CPI: A nested cross-attention network for compound–protein interaction prediction. Bioinformatics 39, btac731 (2023).
Google Scholar
Yang, Z., Zhong, W., Zhao, L. & Yu-Chian Chen, C. MGraphDTA: Deep multiscale graph neural network for explainable drug–target binding affinity prediction. Chem. Sci. 13, 816–833 (2022).
Google Scholar
Tang, J. et al. Making sense of large-scale kinase inhibitor bioactivity data sets: A comparative and integrative analysis. J. Chem. Inf. Model 54, 735–743 (2014).
Google Scholar
Davis, M. I. et al. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1046–1051 (2011).
Google Scholar
Avram, S., Bora, A., Halip, L. & Curpan, R. Modeling kinase inhibition using highly confident data sets. J. Chem. Inf. Model 58, 957–967 (2018).
Google Scholar
Chen, L. et al. TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments. Bioinformatics 36, 4406–4414 (2020).
Google Scholar
Hu, R., Xu, H., Jia, P. & Zhao, Z. KinaseMD: Kinase mutations and drug response database. Nucleic Acids Res. 49, D552–D561 (2021).
Google Scholar
Zhang, H. et al. An integrated deep learning and molecular dynamics simulation-based screening pipeline identifies inhibitors of a new cancer drug target TIPE2. Front Pharmacol. 12, 3297 (2021).
Anwaar, M. U. et al. Combined deep learning and molecular docking simulations approach identifies potentially effective FDA approved drugs for repurposing against SARS-CoV-2. Comput. Biol. Med. 141, 105049 (2022).
Google Scholar
Lim, M. A., Yang, S., Mai, H. & Cheng, A. C. Exploring deep learning of quantum chemical properties for absorption, distribution, metabolism, and excretion predictions. J. Chem. Inf. Model 62, 6336–6341 (2022).
Google Scholar
Sun, Y., Jiao, Y., Shi, C. & Zhang, Y. Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2. Comput. Struct. Biotechnol. J. 20, 5014–5027 (2022).
Google Scholar
Morrone, J. A., Weber, J. K., Huynh, T., Luo, H. & Cornell, W. D. Combining docking pose rank and structure with deep learning improves protein-ligand binding mode prediction over a baseline docking approach. J. Chem. Inf. Model 60, 4170–4179 (2020).
Google Scholar
Rodríguez-Pérez, R., Miljković, F. & Bajorath, J. Assessing the information content of structural and protein-ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning. J. Cheminform. 12, 1–14 (2020).
Google Scholar
Xue, M. et al. Knowledge-based scoring functions in drug design. 1. Developing a target-specific method for kinase-ligand interactions. J. Chem. Inf. Model 50, 1378–1386 (2010).
Google Scholar
Caffrey, D. R., Lunney, E. A. & Moshinsky, D. J. Prediction of specificity-determining residues for small-molecule kinase inhibitors. BMC Bioinform. 9, 1–15 (2008).
Google Scholar
Lo, Y.-C. et al. Computational analysis of kinase inhibitor selectivity using structural knowledge. Bioinformatics 35, 235–242 (2019).
Google Scholar
