Generic placeholder image

Combinatorial Chemistry & High Throughput Screening


ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Research Article

Machine Learning Assisted Discovery of Novel p38α Inhibitors from Natural Products

Author(s): Tianze Shen, Yongxing Tao, Biaoqi Liu, Deliang Kong, Ruihan Zhang* and Weilie Xiao*

Volume 26, Issue 6, 2023

Published on: 26 August, 2022

Page: [1214 - 1223] Pages: 10

DOI: 10.2174/1386207325666220630154917

Price: $65


Background: P38α, emerging as a hot spot for drug discovery, is a member of the mitogen- activated protein kinase (MAPK) family and plays a crucial role in regulating the production of inflammatory mediators. However, despite a massive number of highly potent molecules being reported and several under clinical trials, no p38α inhibitor has been approved yet. There is still demand to discover novel p38α to deal with the safety issue induced by off-target effects.

Objective: In this study, we performed a machine learning-based virtual screening to identify p38α inhibitors from a natural products library, expecting to find novel drug lead scaffolds.

Methods: Firstly, the training dataset was processed with similarity screening to fit the chemical space of the natural products library. Then, six classifiers were constructed by combing two sets of molecular features with three different machine learning algorithms. After model evaluation, the three best classifiers were used for virtual screening.

Results: Among the 15 compounds selected for experimental validation, picrasidine S was identified as a p38α inhibitor with the IC50 as 34.14 μM. Molecular docking was performed to predict the interaction mode of picrasidine S and p38α, indicating a specific hydrogen bond with Met109.

Conclusion: This work provides a protocol and example for machine learning-assisted discovery of p38α inhibitor from natural products, as well as a novel lead scaffold represented by picrasidine S for further optimization and investigation.

Keywords: p38α inhibitor, MAPK14, bioactivity prediction, Picrasma quassioides, picrasidine S, virtual screening.

Graphical Abstract
Zarubin, T.; Han, J. Activation and signaling of the p38 MAP kinase pathway. Cell Res., 2005, 15(1), 11-18.
[] [PMID: 15686620]
Canovas, B.; Nebreda, A.R. Diversity and versatility of p38 kinase signalling in health and disease. Nat. Rev. Mol. Cell Biol., 2021, 22(5), 346-366.
[] [PMID: 33504982]
Tokunaga, Y.; Takeuchi, K.; Takahashi, H.; Shimada, I. Allosteric enhancement of MAP kinase p38α’s activity and substrate selectivity by docking interactions. Nat. Struct. Mol. Biol., 2014, 21(8), 704-711.
[] [PMID: 25038803]
Schieven, G.L. The p38alpha kinase plays a central role in inflammation. Curr. Top. Med. Chem., 2009, 9(11), 1038-1048.
[] [PMID: 19747121]
Westra, J.; Limburg, P.C. p38 mitogen-activated protein kinase (MAPK) in rheumatoid arthritis. Mini Rev. Med. Chem., 2006, 6(8), 867-874.
[] [PMID: 16918493]
Igea, A.; Nebreda, A.R. The stress kinase p38 alpha as a target for cancer therapy. Cancer Res., 2015, 75(19), 3997-4002.
[] [PMID: 26377941]
Munoz, L.; Ammit, A.J. Targeting p38 MAPK pathway for the treatment of Alzheimer’s disease. Neuropharmacology, 2010, 58(3), 561-568.
[] [PMID: 19951717]
Denise Martin, E.; De Nicola, G.F.; Marber, M.S. New therapeutic targets in cardiology: p38 alpha mitogen-activated protein kinase for ischemic heart disease. Circulation, 2012, 126(3), 357-368.
[] [PMID: 22801653]
Wang, S.; Ding, L.; Ji, H.; Xu, Z.; Liu, Q.; Zheng, Y. The role of p38 mAPK in the development of diabetic cardiomyopathy. Int. J. Mol. Sci., 2016, 17(7), 17.
[] [PMID: 27376265]
Madkour, M.M.; Anbar, H.S.; El-Gamal, M.I. Current status and future prospects of p38α/MAPK14 kinase and its inhibitors. Eur. J. Med. Chem., 2021, 213, 113216.
[] [PMID: 33524689]
Liu, J.; Hu, Y.; Waller, D.L.; Wang, J.; Liu, Q. Natural products as kinase inhibitors. Nat. Prod. Rep., 2012, 29(3), 392-403.
[] [PMID: 22231144]
Yin, B.; Fang, D.M.; Zhou, X.L.; Gao, F. Natural products as important tyrosine kinase inhibitors. Eur. J. Med. Chem., 2019, 182, 111664.
[] [PMID: 31494475]
Swann, S.L.; Merta, P.J.; Kifle, L.; Groebe, D.; Sarris, K.; Hajduk, P.J.; Sun, C. Biochemical and biophysical characterization of unique switch pocket inhibitors of p38α. Bioorg. Med. Chem. Lett., 2010, 20(19), 5787-5792.
[] [PMID: 20471255]
Machado, T.R.; Machado, T.R.; Pascutti, P.G. The p38 MAPK Inhibitors and their role in inflammatory diseases. ChemistrySelect, 2021, 6(23), 5729-5742.
Astolfi, A.; Manfroni, G.; Cecchetti, V.; Barreca, M.L. A comprehensive structural overview of p38α mitogen-activated protein kinase in complex with ATP-site and non-ATP-site binders. ChemMedChem, 2018, 13(1), 7-14.
[] [PMID: 29210532]
Fitzgerald, C.E.; Patel, S.B.; Becker, J.W.; Cameron, P.M.; Zaller, D.; Pikounis, V.B.; O’Keefe, S.J.; Scapin, G. Structural basis for p38alpha MAP kinase quinazolinone and pyridol-pyrimidine inhibitor specificity. Nat. Struct. Biol., 2003, 10(9), 764-769.
[] [PMID: 12897767]
Millan, D.S.; Bunnage, M.E.; Burrows, J.L.; Butcher, K.J.; Dodd, P.G.; Evans, T.J.; Fairman, D.A.; Hughes, S.J.; Kilty, I.C.; Lemaitre, A.; Lewthwaite, R.A.; Mahnke, A.; Mathias, J.P.; Philip, J.; Smith, R.T.; Stefaniak, M.H.; Yeadon, M.; Phillips, C. Design and synthesis of inhaled p38 inhibitors for the treatment of chronic obstructive pulmonary disease. J. Med. Chem., 2011, 54(22), 7797-7814.
[] [PMID: 21888439]
Cheeseright, T.J.; Holm, M.; Lehmann, F.; Luik, S.; Göttert, M.; Melville, J.L.; Laufer, S. Novel lead structures for p38 MAP kinase via FieldScreen virtual screening. J. Med. Chem., 2009, 52(14), 4200-4209.
[] [PMID: 19489590]
Choi, H.; Park, H.J.; Shin, J.C.; Ko, H.S.; Lee, J.K.; Lee, S.; Park, H.; Hong, S. Structure-based virtual screening approach to the discovery of p38 MAP kinase inhibitors. Bioorg. Med. Chem. Lett., 2012, 22(6), 2195-2199.
[] [PMID: 22342625]
Badrinarayan, P.; Sastry, G.N. Virtual screening filters for the design of type II p38 MAP kinase inhibitors: A fragment based library generation approach. J. Mol. Graph. Model., 2012, 34, 89-100.
[] [PMID: 22306417]
Gangwal, R.P.; Das, N.R.; Thanki, K.; Damre, M.V.; Dhoke, G.V.; Sharma, S.S.; Jain, S.; Sangamwar, A.T. Identification of p38α MAP kinase inhibitors by pharmacophore based virtual screening. J. Mol. Graph. Model., 2014, 49, 18-24.
[] [PMID: 24473068]
Astolfi, A.; Kudolo, M.; Brea, J.; Manni, G.; Manfroni, G.; Palazzotti, D.; Sabatini, S.; Cecchetti, F.; Felicetti, T.; Cannalire, R.; Massari, S.; Tabarrini, O.; Loza, M.I.; Fallarino, F.; Cecchetti, V.; Laufer, S.A.; Barreca, M.L. Discovery of potent p38α MAPK inhibitors through a funnel like workflow combining in silico screening and in vitro validation. Eur. J. Med. Chem., 2019, 182, 111624.
[] [PMID: 31445234]
Astolfi, A.; Iraci, N.; Sabatini, S.; Barreca, M.L.; Cecchetti, V. p38 alpha MAPK and Type I inhibitors: Binding site analysis and use of target ensembles in virtual screening. Molecules, 2015, 20(9), 15842-15861.
[] [PMID: 26334265]
Vermani, A.; Kouznetsova, V.; Tsigelny, I. New inhibitors of the p38 mitogen-activated protein kinase: Repurposing existing drugs with deep learning. Biointerface Res. Appl. Chem., 2021, 12(4), 5384-5404.
Zhang, R.; Li, X.; Zhang, X.; Qin, H.; Xiao, W. Machine learning approaches for elucidating the biological effects of natural products. Nat. Prod. Rep., 2021, 38(2), 346-361.
[] [PMID: 32869826]
Yang, X.; Wang, Y.; Byrne, R.; Schneider, G.; Yang, S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem. Rev., 2019, 119(18), 10520-10594.
[] [PMID: 31294972]
Li, H.; Sze, K.H.; Lu, G.; Ballester, P.J. Machine‐learning scoring functions for structure‐based drug lead optimization. WIREs Comput. Mol. Sci.,, 2020, 10(5), e1465.
Hunter, J.D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng., 2007, 9(3), 90-95.
Gnuplot 5.4.. Available from:
The PyMOL Molecular Graphics System, Version 2.0; Schrödinger, LLC.,; , 2007.
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. ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Res., 2012, 40(Database issue), D1100-D1107.
[] [PMID: 21948594]
Mysinger, M.M.; Carchia, M.; Irwin, J.J.; Shoichet, B.K. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J. Med. Chem., 2012, 55(14), 6582-6594.
[] [PMID: 22716043]
O’Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open Babel: An open chemical toolbox; J. Cheminformatics, 2011, p. 3.
RDKit; Open-Source Cheminformatics Software. Available from:
Bajusz, D.; Racz, A.; Heberger, K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations; J. Cheminformatics, 2015, p. 7.
Yap, C.W. PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem., 2011, 32(7), 1466-1474.
[] [PMID: 21425294]
Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model., 2010, 50(5), 742-754.
[] [PMID: 20426451]
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, E. Scikit-learn: Machine learning in python. J. Mach. Learn. Res., 2011, 12, 2825-2830.
Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol., 1996, 261(3), 470-489.
[] [PMID: 8780787]
Murali Dhar, T.G.; Wrobleski, S.T.; Lin, S.; Furch, J.A.; Nirschl, D.S.; Fan, Y.; Todderud, G.; Pitt, S.; Doweyko, A.M.; Sack, J.S.; Mathur, A.; McKinnon, M.; Barrish, J.C.; Dodd, J.H.; Schieven, G.L.; Leftheris, K. Synthesis and SAR of p38alpha MAP kinase inhibitors based on heterobicyclic scaffolds. Bioorg. Med. Chem. Lett., 2007, 17(18), 5019-5024.
[] [PMID: 17664068]
Zhang, X.; Liu, T.; Fan, X.; Ai, N. In silico modeling on ADME properties of natural products: Classification models for blood-brain barrier permeability, its application to traditional Chinese medicine and in vitro experimental validation. J. Mol. Graph. Model., 2017, 75, 347-354.
[] [PMID: 28628860]
Mohd Jamil, M.D.H.; Taher, M.; Susanti, D.; Rahman, M.A.; Zakaria, Z.A. Phytochemistry, traditional use and pharmacological activity of Picrasma quassioides: A critical reviews. Nutrients, 2020, 12(9), 12.
[] [PMID: 32858812]
Qian-Wen, C.; Xiao, Y.; Xiao-Qian, L.; Yao-Hua, L.; Wei-Hong, F.; Chun, L.; Zhi-Min, W. Alkaloids from Picrasma quassioides: An overview of their NMR data, biosynthetic pathways and pharmacological effects. Phytochemistry, 2022, 193, 112987.
[] [PMID: 34768188]
Stefanoska, K.; Bertz, J.; Volkerling, A.M.; van der Hoven, J.; Ittner, L.M.; Ittner, A. Neuronal MAP kinase p38α inhibits c-Jun N-terminal kinase to modulate anxiety-related behaviour. Sci. Rep., 2018, 8(1), 14296.
[] [PMID: 30250211]

Rights & Permissions Print Export Cite as
© 2023 Bentham Science Publishers | Privacy Policy