Skip to content
2000
image of Current Updates on Recent Developments in Artificial Intelligence in QSAR Modelling for Drug Discovery against Lung Cancer

Abstract

Lung cancer continues to be a leading cause of cancer-related mortality worldwide, underscoring the urgency for innovative and targeted drug discovery strategies. This review critically explores the role of Quantitative Structure-Activity Relationship (QSAR) modelling, particularly its integration with artificial intelligence (AI), in accelerating the identification and optimization of lung cancer therapeutics. Recent progress in multi-target approaches, machine learning (ML) algorithms with mathematical representations, and molecular descriptor engineering has been analyzed, with a special focus on clinical translations. Rather than offering a generic overview, we evaluate how AI-powered QSAR addresses key bottlenecks in drug development, such as data imbalance, model interpretability, and ADMET prediction failures. Notable case studies are examined to highlight translational success stories in lung cancer-specific pathways. This review offers a cohesive synthesis of current advancements, identifies critical gaps and limitations, and proposes future directions for enhancing the real-world applications of QSAR methodologies in oncological drug discovery.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/ctmc/10.2174/0115680266391548250930172321
2025-10-21
2025-12-16

  • From This Site

    /content/journals/ctmc/10.2174/0115680266391548250930172321
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Huang H.J. Chetyrkina M. Wong C.W. Kraevaya O.A. Zhilenkov A.V. Voronov I.I. Wang P.H. Troshin P.A. Hsu S. Identification of potential descriptors of water-soluble fullerene derivatives responsible for antitumor effects on lung cancer cells via QSAR analysis. Comput. Struct. Biotechnol. J. 2021 19 812 825 10.1016/j.csbj.2021.01.012 33598097
    [Google Scholar]
  2. Iksen I. Sinsook S. Wattanathamsan O. Buaban K. Chamni S. Pongrakhananon V. Target identification of 22-(4-Pyridinecarbonyl) Jorunnamycin A, a tetrahydroisoquinoline derivative from the Sponge Xestospongia sp., in mediating non-small-cell lung cancer cell apoptosis. Molecules 2022 27 24 8948 10.3390/molecules27248948 36558080
    [Google Scholar]
  3. Chheang S. Brown K. Lung cancer staging: Clinical and radiologic perspectives. Semin Intervent Radiol 2013 30 (2) 099 113 10.1055/s‑0033‑1342950 24436525
    [Google Scholar]
  4. Elrayess R. Abdel Aziz Y.M. Elgawish M.S. Elewa M. Elshihawy H.A. Said M.M. Pharmacophore modeling, 3D‐QSAR, synthesis, and anti‐lung cancer evaluation of novel thieno[2,3‐ d][1,2,3]triazines targeting EGFR. Arch. Pharm. 2020 353 2 1900108 10.1002/ardp.201900108 31894866
    [Google Scholar]
  5. Ibrahim M.T. Uzairu A. Uba S. Shallangwa G.A. Quantitative structure-activity relationship, molecular docking, drug-likeness, and pharmacokinetic studies of some non-small cell lung cancer therapeutic agents. Beni. Suef Univ. J. Basic Appl. Sci. 2020 9 1 49 10.1186/s43088‑020‑00077‑5
    [Google Scholar]
  6. Gerstberger S. Jiang Q. Ganesh K. Metastasis. Cell 2023 186 8 1564 1579 10.1016/j.cell.2023.03.003
    [Google Scholar]
  7. Putra G.S. Sulistyowaty M.I. Yuniarta T.A. Yahmin Y. Sumari S. Saechan C. Yamauchi T. QSAR study of benzylidene hydrazine benzamides derivatives with in vitro anticancer activity against human lung cancer cell line A459. J. Pharm. Pharmacogn. Res. 2023 11 6 1123 1136 10.56499/jppres23.1718_11.6.1123
    [Google Scholar]
  8. Mkhayar K. Daoui O. Elkhattabi S. Chtita S. Elkhalabi R. QSAR study and molecular docking assisted design of novel cyclohexane- 1,3-dione derivatives as anticancer agents for non-small cell lung cancer. Biointerface Res. Appl. Chem. 2023 13 6 524 10.33263/BRIAC136.524
    [Google Scholar]
  9. Khaled D.M. Elshakre M.E. Noamaan M.A. Butt H. Abdel Fattah M.M. Gaber D.A. A Computational QSAR, molecular docking and in vitro cytotoxicity study of novel thiouracil-based drugs with anticancer activity against human-dna topoisomerase ii. Int. J. Mol. Sci. 2022 23 19 11799 10.3390/ijms231911799 36233102
    [Google Scholar]
  10. Huang H.J. Kraevaya O.A. Voronov I.I. Troshin P.A. Hsu S. Fullerene derivatives as lung cancer cell inhibitors: Investigation of potential descriptors using qsar approaches. Int. J. Nanomedicine 2020 15 2485 2499 10.2147/IJN.S243463 32368036
    [Google Scholar]
  11. Mishra N. Maurya A.K. Novel drug discovery against breast and lung cancer using pharmacophore based 2H-1-benzopyran-2-one derivative. Res. Square 2022 1 5 10.21203/rs.3.rs‑1541154/v1
    [Google Scholar]
  12. Chunarkar-Patil P. Anticancer drug discovery based on natural products: From computational approaches to clinical studies. Biomedicines 2024 12 1 201 10.3390/biomedicines12010201
    [Google Scholar]
  13. Aljanabi R. Alsous L. Sabbah D.A. Gul H.I. Gul M. Bardaweel S.K. Monoamine oxidase (MAO) as a potential target for anticancer drug design and development. Molecules 2021 26 19 6019 10.3390/molecules26196019
    [Google Scholar]
  14. Das A.P. Agarwal S.M. Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches. Mol. Divers. 2024 28 2 901 925 10.1007/s11030‑022‑10590‑7
    [Google Scholar]
  15. Fereidoonnezhad M. Tabaei S.M.H. Sakhteman A. Seradj H. Faghih Z. Faghih Z. Mojaddami A. Sadeghian B. Rezaei Z. Design, synthesis, molecular docking, biological evaluations and QSAR studies of novel dichloroacetate analogues as anticancer agent. J. Mol. Struct. 2020 1221 128689 10.1016/j.molstruc.2020.128689
    [Google Scholar]
  16. Tylińska B. Wiatrak B. Czyżnikowska Ż. Cieśla-Niechwiadowicz A. Gębarowska E. Janicka-Kłos A. Novel pyrimidine derivatives as potential anticancer agents: Synthesis, biological evaluation and molecular docking study. Int. J. Mol. Sci. 2021 22 8 3825 10.3390/ijms22083825 33917090
    [Google Scholar]
  17. Hu Z.H. Zhao T.S. Liu H.Y. Lin Q.X. Tu G.G. Yang B.W. Synthesis and receptor dependent 4D-QSAR studies of 4,5-dihydro-1,3,4-oxadiazole derivatives targeting cannabinoid receptor. SAR QSAR Environ. Res. 2021 32 3 175 190 10.1080/1062936X.2021.1879256 33618568
    [Google Scholar]
  18. Karnik K.S. Sarkate A.P. Tiwari S.V. Azad R. Burra P.V.L.S. Wakte P.S. Computational and Synthetic approach with Biological Evaluation of Substituted Quinoline derivatives as small molecule L858R/T790M/C797S triple mutant EGFR inhibitors targeting resistance in Non-Small Cell Lung Cancer (NSCLC). Bioorg. Chem. 2021 107 104612 10.1016/j.bioorg.2020.104612 33476869
    [Google Scholar]
  19. El Mchichi L. El Aissouq A. Kasmi R. Belhassan A. El-Mernissi R. Ouammou A. Lakhlifi T. Bouachrine M. In silico design of novel pyrazole derivatives containing thiourea skeleton as anti-cancer agents using: 3D QSAR, drug-likeness studies ADMET prediction and molecular docking Mater. Today Proc 2021 In: Mater. Today Proc; 45, pp (Part 8) 7661 7674 10.1016/j.matpr.2021.03.152
    [Google Scholar]
  20. Zeng Q. Hu H. Huang Z. Guo A. Lu S. Tong W. Zhang Z. Shen T. Active and machine learning-enhanced discovery of new FGFR3 inhibitor, Rhapontin, through virtual screening of receptor structures and anti-cancer activity assessment. Front. Mol. Biosci. 2024 11 1413214 10.3389/fmolb.2024.1413214 38919748
    [Google Scholar]
  21. Bhat S.S. Mahapatra S.D. R, S.; Sommano, S.R.; Prasad, S.K. Virtual screening and quantitative structure–activity relationship of moringa oleifera with melanoma antigen A (MAGE-A) genes against the therapeutics of non-small cell lung cancers (NSCLCs). Cancers 2022 14 20 5052 10.3390/cancers14205052 36291836
    [Google Scholar]
  22. Ibrahim M.T. Uzairu A. Shallangwa G.A. Uba S. Lead identification of some anti-cancer agents with prominent activity against non-small cell lung cancer (NSCLC) and structure-based design. Chemistry Africa 2020 3 4 1023 1044 10.1007/s42250‑020‑00191‑y
    [Google Scholar]
  23. Dey S.P. Sepay N. Mallik A.K. Patra A. Novel chalcones as Bcl-2 inhibitor in lung cancer: Docking, design and synthesis of 2,3-Tetrasubstituted-2,3-dihydrobenzofuran-3-carboxamides. J. Chem. Sci. 2020 132 1 105 10.1007/s12039‑020‑01812‑2
    [Google Scholar]
  24. Hadni H. Elhallaouia M. In silico design of EGFRL858R/T790M/C797S inhibitors via 3D-QSAR, molecular docking, ADMET properties and molecular dynamics simulations. Heliyon 2022 8 11 11537 10.1016/j.heliyon.2022.e11537 36411890
    [Google Scholar]
  25. Amin S.A. Ghosh K. Mondal D. Jha T. Gayen S. Exploring indole derivatives as myeloid cell leukaemia-1 (Mcl-1) inhibitors with multi-QSAR approach: A novel hope in anti-cancer drug discovery. New J. Chem. 2020 44 40 17494 17506 10.1039/D0NJ03863F
    [Google Scholar]
  26. Krzywik J. Aminpour M. Janczak J. Maj E. Moshari M. Mozga W. Wietrzyk J. Tuszyński J.A. Huczyński A. An insight into the anticancer potential of carbamates and thiocarbamates of 10-demethoxy-10-methylaminocolchicine. Eur. J. Med. Chem. 2021 215 113282 10.1016/j.ejmech.2021.113282 33611191
    [Google Scholar]
  27. Yeh Y.C. Lawal B. Hsiao M. Huang T.H. Huang C.Y. Identification of nsp3 (Sh2d3c) as a prognostic biomarker of tumor progression and immune evasion for lung cancer and evaluation of organosulfur compounds from Allium sativum l. as therapeutic candidates. Biomedicines 2021 9 11 1582 10.3390/biomedicines9111582 34829812
    [Google Scholar]
  28. Aguilera-Durán G. Romo-Mancillas A. Behavior of chemokine receptor 6 (Cxcr6) in complex with cxcl16 soluble form chemokine by molecular dynamic simulations: General protein-ligand interaction model and 3d-qsar studies of synthetic antagonists. Life 2021 11 4 346 10.3390/life11040346 33920834
    [Google Scholar]
  29. Patel N.B. Prajapati N. Patel N. Integrative in silico analysis of Pinus Roxburghii phytochemicals for drug discovery. 2023 Available from [https://www.researchgate.net/publication/373512116
    [Google Scholar]
  30. Nath R. Singha S. Nath D. Das G. Patra J. Talukdar A. Phytochemicals from Allium tuberosum rottler ex spreng show potent inhibitory activity against b-raf, egfr, k-ras, and pi3k of non-small cell lung cancer targets. Appl. Sci. 2022 12 22 11749 10.3390/app122211749
    [Google Scholar]
  31. Gamal H. Ismail K.A. Omar A.M.M.E. Teleb M. Abu-Serie M.M. Huang S. Abdelsattar A.S. Zamponi G.W. Fahmy H. Non-small cell lung cancer sensitisation to platinum chemotherapy via new thiazole-triazole hybrids acting as dual T-type CCB/MMP-9 inhibitors. J. Enzyme Inhib. Med. Chem. 2024 39 1 2388209 10.1080/14756366.2024.2388209 39140776
    [Google Scholar]
  32. Erazua E.A. Oyebamiji A.K. Akintelu S.A. Adewole P.D. Adelakun A. Adeleke B.B. Quantitative structure-activity relationship, molecular docking and ADMET screening of tetrahydroquinoline derivatives as anti-small cell lung cancer agents. Eclét. Quím. 2023 48 1 55 71 10.26850/1678‑4618eqj.v48.1.2023.p55‑71
    [Google Scholar]
  33. Ramesh P. Karuppasamy R. Veerappapillai S. Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer. Med. Oncol. 2022 40 1 56 10.1007/s12032‑022‑01924‑4 36542155
    [Google Scholar]
  34. Elrayess R. Abdel Aziz Y.M. Elgawish M.S. Elewa M. Yassen A.S.A. Elhady S.S. Elshihawy H.A. Said M.M. Discovery of potent dual egfr/her2 inhibitors based on thiophene scaffold targeting h1299 lung cancer cell line. Pharmaceuticals 2020 14 1 9 10.3390/ph14010009 35056059
    [Google Scholar]
  35. Luo W. Chang G. Lin D. Xie H. Sun H. Li Z. Mo S. Wang R. Wang Y. Zheng Z. 3,3′-((3,4,5-trifluoropHenyl)methylene)bis(4-hydroxy-2H-chromen-2-one) inhibit lung cancer cell proliferation and migration. PLoS One 2024 19 5 0303186 10.1371/journal.pone.0303186 38776295
    [Google Scholar]
  36. Sulaimon L. Descriptor-free QSAR: Effectiveness in screening for putative inhibitors of FGFR1. J. Biomol. Struct. Dyn. 2023 41 5 2016 2032 10.1080/07391102.2022.2026248
    [Google Scholar]
  37. Karim E.M. Discovery of a potential inhibitor against lung cancer: Computational approaches and molecular dynamics study. Phys. Chem. Res. 2024 12 3 579 589 10.22036/pcr.2023.415502.2423
    [Google Scholar]
  38. Zhi H.Y. Zhao L. Lee C.C. Chen C.Y.C. A novel graph neural network methodology to investigate dihydroorotate dehydrogenase inhibitors in small cell lung cancer. Biomolecules 2021 11 3 477 10.3390/biom11030477 33806898
    [Google Scholar]
  39. Sun T. Liu J. Yuan H. Li X. Yan H. Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm. Front. Oncol. 2024 14 1403392 10.3389/fonc.2024.1403392 39184040
    [Google Scholar]
  40. Pang X. Zhao Y. Li G. Liu J. Yan A.A. SAR and QSAR study on cyclin dependent kinase 4 inhibitors using machine learning methods. Digit. Discov 2023 2 4 1026 1041 10.1039/D2DD00143H
    [Google Scholar]
  41. Sarhan M.O. Abd El-Karim S.S. Anwar M.M. Gouda R.H. Zaghary W.A. Khedr M.A. Discovery of new coumarin-based lead with potential anticancer, cdk4 inhibition and selective radiotheranostic effect: Synthesis, 2d & 3d qsar, molecular dynamics, in vitro cytotoxicity, radioiodination, and biodistribution studies. Molecules 2021 26 8 2273 10.3390/molecules26082273 33919867
    [Google Scholar]
  42. Masudur Rahman Munna M. Touki Tahamid Tusar M. Sajnin Shanta S. Hossain Ahmed M. Sarafat Ali M. Unveiling promising phytocompounds from Moringa oleifera as dual inhibitors of EGFR(T790M/C797S) and VEGFR-2 in non-small cell lung cancer through in silico screening, ADMET, dynamics simulation, and DFT analysis. J. Genet. Eng. Biotechnol. 2024 22 3 100406 10.1016/j.jgeb.2024.100406 39179328
    [Google Scholar]
  43. Wei T. Zheng Z. Wei X. Liu Y. Li W. Fang B. Yun D. Dong Z. Yi B. Li W. Wu X. Chen D. Chen L. Wu J. Rational design, synthesis, and pharmacological characterisation of dicarbonyl curcuminoid analogues with improved stability against lung cancer via ROS and ER stress mediated cell apoptosis and pyroptosis. J. Enzyme Inhib. Med. Chem. 2022 37 1 2357 2369 10.1080/14756366.2022.2116015 36039017
    [Google Scholar]
  44. Xu Y. Fan B. Gao Y. Chen Y. Han D. Lu J. Liu T. Gao Q. Zhang J.Z. Wang M. Design two novel tetrahydroquinoline derivatives against anticancer target LSD1 with 3D-QSAR model and molecular simulation. Molecules 2022 27 23 8358 10.3390/molecules27238358 36500451
    [Google Scholar]
  45. Tong J.B. Feng Y. Luo D. Wang T.H. 6-amide-2-aryl benzoxazole/benzimidazole derivatives as VEFGR-2 inhibitors in two-and three-dimensional QSAR studies: Topomer CoMFA and HQSAR. Chem. Pap. 2021 75 7 3551 3562 10.1007/s11696‑021‑01588‑w
    [Google Scholar]
  46. Yaseen Y. Kubba A. Shihab W. Tahtamouni L. Synthesis, docking study, and structure-activity relationship of novel niflumic acid derivatives acting as anticancer agents by inhibiting VEGFR or EGFR tyrosine kinase activities. Pharmacia 2022 69 3 595 614 10.3897/pharmacia.69.e86504
    [Google Scholar]
  47. Daoui O. 3D-QSAR modeling, molecular docking and drug-like properties investigations of novel heterocyclic compounds derived from magnolia officinalis as hit compounds against NSCLC. Moroccan J. Chem. 2022 10 4 881 890 10.48317/IMIST.PRSM/morjchem‑v10i4.34498
    [Google Scholar]
  48. Razib S.M. Synthesis and characterization, 2d nmr of xanthone derivatives: Molecular docking and qsar studies to understand their potential as 3EQM, 7TAI, 4XR8, 6MNX and 4G3D inhibitors. Available from 2024 [https://orcid.org/my-orcid?orcid=0000-0001-7343-8087] [http://dx.doi.org/10.2139/ssrn.4750231
    [Google Scholar]
  49. Daoui O. Elkhattabi S. Bakhouch M. Belaidi S. Bhandare R.R. Shaik A.B. Mali S.N. Chtita S. Cyclohexane-1,3-dione derivatives as future therapeutic agents for nsclc: QSAR modeling, in silico ADME-TOX properties, and structure-based drug designing approach. ACS Omega 2023 8 4 4294 4319 10.1021/acsomega.2c07585 36743017
    [Google Scholar]
  50. Odugbemi A.I. Nyirenda C. Christoffels A. Egieyeh S.A. Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors. Comput. Struct. Biotechnol. J. 2024 23 1 10.1016/j.csbj.2024.07.003
    [Google Scholar]
  51. Nakarin F. Boonpalit K. Kinchagawat J. Wachiraphan P. Rungrotmongkol T. Nutanong S. Assisting multitargeted ligand affinity prediction of receptor tyrosine kinases associated nonsmall cell lung cancer treatment with multitasking principal neighborhood aggregation. Molecules 2022 27 4 1226 10.3390/molecules27041226 35209011
    [Google Scholar]
  52. Issa N.T. Stathias V. Schürer S. Dakshanamurthy S. Machine and deep learning approaches for cancer drug repurposing. Semin. Cancer Biol. 2021 68 132 142 10.1016/j.semcancer.2019.12.011
    [Google Scholar]
  53. Islam T. Ai-driven drug repurposing: Uncovering hidden potentials of established medications for rare disease treatment 2024 Available from:www.bpasjournals.com
  54. Li B. Dai C. Wang L. Deng H. Li Y. Guan Z. Ni H. A novel drug repurposing approach for non-small cell lung cancer using deep learning. PLoS One 2020 15 6 0233112 10.1371/journal.pone.0233112 32525938
    [Google Scholar]
  55. Baby K. Maity S. Mehta C.H. Nayak U.Y. Shenoy G.G. Pai K.S.R. Harikumar K.B. Nayak Y. Computational drug repurposing of Akt-1 allosteric inhibitors for non-small cell lung cancer. Sci. Rep. 2023 13 1 7947 10.1038/s41598‑023‑35122‑7 37193898
    [Google Scholar]
  56. Chaube U.J. Rawal R. Jha A.B. Variya B. Bhatt H.G. Design and development of Tetrahydro-Quinoline derivatives as dual mTOR-C1/C2 inhibitors for the treatment of lung cancer. Bioorg. Chem. 2021 106 104501 10.1016/j.bioorg.2020.104501 33280832
    [Google Scholar]
  57. He G. Gong B. Li J. Song Y. Li S. Lu X. An improved receptor-based pharmacophore generation algorithm guided by atomic chemical characteristics and hybridization types. Front. Pharmacol. 2018 9 1463 10.3389/fphar.2018.01463 30618755
    [Google Scholar]
  58. Hussein M.F. Novel Indene-[1,3,4] Oxadiazine hybrids: Design, construction, molecular docking, QSAR, ADME study and anticancer potential. Egypt. J. Chem. 2024 67 2 497 512 10.21608/ejchem.2023.216875.8133
    [Google Scholar]
  59. Singh S. Mangla B. Javed S. Kumar P. Ahsan W. Multivariate Statistical 2D QSAR Analysis of Indenoisoquinoline-based Topoisomerase- I Inhibitors as Anti-lung Cancer Agents. Anticancer. Agents Med. Chem. 2023 23 20 2237 2247 10.2174/0118715206262897230924011648 37807646
    [Google Scholar]
  60. Maiti P. Sharma P. Nand M. Bhatt I.D. Ramakrishnan M.A. Mathpal S. Joshi T. Pant R. Mahmud S. Simal-Gandara J. Alshehri S. Ghoneim M.M. Alruwaily M. Awadh A.A.A. Alshahrani M.M. Chandra S. Integrated machine learning and chemoinformatics-based screening of mycotic compounds against kinesin spindle proteineg5 for lung cancer therapy. Molecules 2022 27 5 1639 10.3390/molecules27051639 35268740
    [Google Scholar]
  61. Ghazi Y.A.F. Mahdi M.F. Dawood A.H. Theoretical drug design, molecular docking and ADME study of new 1,3,4-oxadiazole derivatives: Promising anticancer agents against both breast and lung cancers. Egypt. J. Chem. 2021 64 11 6269 6283 10.21608/ejchem.2021.75663.3735
    [Google Scholar]
  62. Lee J.Y. Yang H. Kim D. Kyaw K.Z. Hu R. Fan Y. Lee S.K. Antiproliferative activity of a new quinazolin-4(3H)-One Derivative via targeting aurora kinase a in non-small cell lung cancer. Pharmaceuticals 2022 15 6 698 10.3390/ph15060698 35745617
    [Google Scholar]
  63. Xu W.T. Li T.Z. Li S.M. Wang C. Wang H. Luo Y.H. Piao X.J. Wang J.R. Zhang Y. Zhang T. Xue H. Cao L.K. Jin C.H. Cytisine exerts anti-tumour effects on lung cancer cells by modulating reactive oxygen species-mediated signalling pathways. Artif. Cells Nanomed. Biotechnol. 2020 48 1 84 95 10.1080/21691401.2019.1699813 31852250
    [Google Scholar]
  64. Bhujbal S.P. Kim H. Bae H. Hah J.M. Design and synthesis of aminopyrimidinyl pyrazole analogs as plk1 inhibitors using hybrid 3D-QSAR and molecular docking. Pharmaceuticals 2022 15 10 1170 10.3390/ph15101170 36297281
    [Google Scholar]
  65. Jiang G.L. Song L.H. Qiu Y.F. Liu Y. 3D-QSAR and docking studies on pyrimidine derivatives of second-generation ALK inhibitors. Pharmaceutical Fronts 2022 4 3 e136 e150 10.1055/s‑0042‑1750044
    [Google Scholar]
  66. Akash S. Kumer A. Rahman M.M. Emran T.B. Sharma R. Singla R.K. Alhumaydhi F.A. Khandaker M.U. Park M.N. Idris A.M. Wilairatana P. Kim B. Development of new bioactive molecules to treat breast and lung cancer with natural myricetin and its derivatives: A computational and SAR approach. Front. Cell. Infect. Microbiol. 2022 12 952297 10.3389/fcimb.2022.952297 36237438
    [Google Scholar]
  67. Li X. Tang Y. Liang P. Sun M. Li T. Shen Z. Sha S. Luteolin inhibits A549 cells proliferation and migration by down-regulating androgen receptors. Eur. J. Med. Res. 2023 28 1 353 10.1186/s40001‑023‑01302‑4 37716981
    [Google Scholar]
  68. Toropova A.P. Meneses J. Alfaro-Moreno E. Toropov A.A. The system of self-consistent models based on quasi-SMILES as a tool to predict the potential of nano-inhibitors of human lung carcinoma cell line A549 for different experimental conditions. Drug Chem. Toxicol. 2024 47 3 306 313 10.1080/01480545.2023.2174986 36744523
    [Google Scholar]
  69. Maciejewska N. Olszewski M. Jurasz J. Serocki M. Dzierzynska M. Cekala K. Wieczerzak E. Baginski M. Novel chalcone-derived pyrazoles as potential therapeutic agents for the treatment of non-small cell lung cancer. Sci. Rep. 2022 12 1 3703 10.1038/s41598‑022‑07691‑6 35260633
    [Google Scholar]
  70. Weng C-W. Pharmacophore-based virtual screening for the identification of the novel Src inhibitor SJG-136 against lung cancer cell growth and motility. 2020 Available from:www.ajcr.us/
  71. Tang W. Shen T. Chen Z. In silico discovery of potential PPI inhibitors for anti-lung cancer activity by targeting the CCND1-CDK4 complex via the P21 inhibition mechanism. Front Chem. 2024 12 1404573 10.3389/fchem.2024.1404573 38957406
    [Google Scholar]
  72. Ul-Haq Z. Khan A. Ashraf S. Morales-Bayuelo A. Quantum mechanics and 3D-QSAR studies on thienopyridine analogues: Inhibitors of IKKβ. Heliyon 2020 6 6 04125 10.1016/j.heliyon.2020.e04125 32566780
    [Google Scholar]
  73. Shao L.H. Fan S-L. Meng Y-F. Gan Y-Y. Shao W-B. Wang Z-C. Chen D-P. Ouyang G-P. Design, synthesis, biological activities and 3D-QSAR studies of quinazolinone derivatives containing hydrazone structural units. New J. Chem. 2021 45 10 4626 4631 10.1039/D0NJ05450J
    [Google Scholar]
/content/journals/ctmc/10.2174/0115680266391548250930172321
Loading
Current Updates on Recent Developments in Artificial Intelligence in QSAR Modelling for Drug Discovery against Lung Cancer
Bentham Science Publishers ; https://doi.org/10.2174/0115680266391548250930172321
/content/journals/ctmc/10.2174/0115680266391548250930172321
/content/journals/ctmc/10.2174/0115680266391548250930172321
Loading

Data & Media loading...


  • Article Type:     Review Article
Keywords: Multiple linear regression ; Artificial intelligence ; QSAR ; Support vector machines ; Machine learning ; Molecular descriptors

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test