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image of Artificial Intelligence in Computer-Aided Drug Design (CADD) Tools for the Finding of Potent Biologically Active Small Molecules: Traditional to Modern Approach

Abstract

Computer-Aided Drug Design (CADD) entails designing molecules that could potentially interact with a specific biomolecular target and promising their potential binding. The stereo-arrangement and stereo-selectivity of small molecules (SMs)--based chemotherapeutic agents significantly influence their therapeutic potential and enhance their therapeutic advantages. CADD has been a well-established field for decades, but recent years have observed a significant shift toward acceptance of computational approaches in both academia and the pharmaceutical industry. Recently, artificial intelligence (AI), bioinformatics, and data science have played a significant role in drug discovery to accelerate the development of effective treatments, reduce expenses, and eliminate the need for animal testing. This shift can be attributed to the availability of extensive data on molecular properties, binding to therapeutic targets, and their 3D structures. Increasing interest from legislators, pharmaceutical companies, and academic and industrial scientists is evidence that AI is reshaping the drug discovery industry. To achieve success in drug discovery, it is necessary to optimize pharmacodynamic, pharmacokinetic, and clinical outcome-related properties. Moreover, the advent of on-demand virtual libraries containing billions of drug-like SMs, coupled with abundant computing capacities, has further facilitated this transition. To fully capitalize on these resources, rapid computational methods are needed for effective ligand screening. This includes structure-based virtual screening (SBVS) of vast chemical spaces, aided by fast iterative screening approaches. At the same time, advances in deep learning (DL) predictions of ligand properties and target activities have become very helpful, as they no longer need information about the structure of the receptor. This study examines recent progress in the drug discovery and development (DDD) approach, their potential to reshape the entire DDD process, and the challenges they face. This review examines the role of artificial intelligence as a fundamental component in drug discovery, particularly focusing on small molecules. It also discusses how AI-driven approaches can expedite the identification of diverse, potent, target-specific, and drug-like ligands for protein targets. This advancement has the potential to make drug discovery more efficient and cost-effective, ultimately facilitating the development of safer and more effective therapeutics.

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2025-01-15
2025-10-06

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References

  1. Austin T. Research and development in the pharmaceutical industry 2021 Available from: Https://Www.Cbo.Gov/Publication/57126 2021
  2. Sun S. Why 90% of clinical drug development fails and how to improve it? Acta Pharm Sin B. 2022 12 7 3049 3062
    [Google Scholar]
  3. Wu K. Karapetyan E. Schloss J. Vadgama J. Wu Y. Advancements in small molecule drug design: A structural perspective. Drug Discov. Today 2023 28 10 103730 10.1016/j.drudis.2023.103730 37536390
    [Google Scholar]
  4. Surabhi S. Singh B.K. Computer aided drug design: An overview. J. Drug Deliv. Ther. 2018 8 5 504 509 10.22270/jddt.v8i5.1894
    [Google Scholar]
  5. Maurer T.S. Smith D. Beaumont K. Di L. Dose predictions for drug design. J. Med. Chem. 2020 63 12 6423 6435 10.1021/acs.jmedchem.9b01365 31913040
    [Google Scholar]
  6. Daoud N.E.H. Borah P. Deb P.K. Venugopala K.N. Hourani W. Alzweiri M. Bardaweel S.K. Tiwari V. ADMET profiling in drug discovery and development: Perspectives of in silico, in vitro and integrated approaches. Curr. Drug Metab. 2021 22 7 503 522 10.2174/1389200222666210705122913 34225615
    [Google Scholar]
  7. Yadav V. Reang J. Vinita R.K. Tonk, Ligand-based drug design (LBDD). Computer Aided Drug Design (CADD): From Ligand-Based Methods to Structure-Based Approaches. Elsevier 2022 57 99 10.1016/B978‑0‑323‑90608‑1.00009‑5
    [Google Scholar]
  8. Weber I.T. Wang Y.F. Harrison R.W. HIV protease: Historical perspective and current research. Viruses 2021 13 5 839 10.3390/v13050839 34066370
    [Google Scholar]
  9. Loftsson T. Stefánsson E. Aqueous eye drops containing drug/cyclodextrin nanoparticles deliver therapeutic drug concentrations to both anterior and posterior segment. Acta Ophthalmol. 2022 100 1 7 25 10.1111/aos.14861 33876553
    [Google Scholar]
  10. Cizeron A. Saunier F. Gagneux-Brunon A. Pillet S. Cantais A. Botelho-Nevers E. Low rate of oseltamivir prescription among adults and children with confirmed influenza illness in France during the 2018–19 influenza season. J. Antimicrob. Chemother. 2021 76 4 1057 1062 10.1093/jac/dkaa539 33406225
    [Google Scholar]
  11. Kneller D.W. Li H. Phillips G. Weiss K.L. Zhang Q. Arnould M.A. Jonsson C.B. Surendranathan S. Parvathareddy J. Blakeley M.P. Coates L. Louis J.M. Bonnesen P.V. Kovalevsky A. Covalent narlaprevir- and boceprevir-derived hybrid inhibitors of SARS-CoV-2 main protease. Nat. Commun. 2022 13 1 2268 10.1038/s41467‑022‑29915‑z 35477935
    [Google Scholar]
  12. Sarukhanyan E. Shanmugam T.A. Dandekar T. In silico studies reveal peramivir and zanamivir as an optimal drug treatment even if H7N9 avian type influenza virus acquires further resistance. Molecules 2022 27 18 5920 10.3390/molecules27185920 36144655
    [Google Scholar]
  13. Jawarkar R.D. Bakal R.L. Zaki M.E.A. Al-Hussain S. Ghosh A. Gandhi A. Mukerjee N. Samad A. Masand V.H. Lewaa I. QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches. Arab. J. Chem. 2022 15 1 103499 10.1016/j.arabjc.2021.103499 34909066
    [Google Scholar]
  14. Lee J.W. Maria-Solano M.A. Vu T.N.L. Yoon S. Choi S. Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD). Biochem. Soc. Trans. 2022 50 1 241 252 10.1042/BST20211240 35076690
    [Google Scholar]
  15. Anwar T. Kumar P. Khan A.U. Modern Tools and Techniques in Computer-Aided Drug Design, Mol Mol. Docking Comput. Drug Des. Fundam. Tech. Resour. Appl. 2021 1 30 10.1016/B978‑0‑12‑822312‑3.00011‑4
    [Google Scholar]
  16. 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 10.3390/ijms22126184 34201152
    [Google Scholar]
  17. Manigrasso J. Marcia M. De Vivo M. Computer-aided design of RNA-targeted small molecules: A growing need in drug discovery Chem. 2021 7 11 2965 2988 10.1016/j.chempr.2021.05.021
    [Google Scholar]
  18. Askr H. Elgeldawi E. Aboul Ella H. Elshaier Y.A.M.M. Gomaa M.M. Hassanien A.E. Deep learning in drug discovery: An integrative review and future challenges. Artif. Intell. Rev. 2023 56 7 5975 6037 10.1007/s10462‑022‑10306‑1 36415536
    [Google Scholar]
  19. Attwood M.M. Fabbro D. Sokolov A.V. Knapp S. Schiöth H.B. Trends in kinase drug discovery: Targets, indications and inhibitor design. Nat. Rev. Drug Discov. 2021 20 11 839 861 10.1038/s41573‑021‑00252‑y 34354255
    [Google Scholar]
  20. Jadhav N.Y. Hipparagi S.M. Raju M.B. Kothule M.B. Sagar D. A review on drug design 2015 3 2015 673 690
    [Google Scholar]
  21. Chen Y. Chen N. Xu J. Wang X. Wei X. Tang C. Duanmu Z. Shi J. Apatinib inhibits the proliferation of gastric cancer cells via the AKT/GSK signaling pathway in vivo. Aging (Albany NY) 2021 13 16 20738 20747 10.18632/aging.203458 34453028
    [Google Scholar]
  22. Naito Y. Mishima S. Akagi K. Hayashi N. Hirasawa A. Hishiki T. Igarashi A. Ikeda M. Kadowaki S. Kajiyama H. Kato M. Kenmotsu H. Kodera Y. Komine K. Koyama T. Maeda O. Miyachi M. Nishihara H. Nishiyama H. Ohga S. Okamoto W. Oki E. Ono S. Sanada M. Sekine I. Takano T. Tao K. Terashima K. Tsuchihara K. Yatabe Y. Yoshino T. Baba E. Japanese Society of Medical Oncology/Japan Society of Clinical Oncology/Japanese Society of Pediatric Hematology/Oncology-led clinical recommendations on the diagnosis and use of tropomyosin receptor kinase inhibitors in adult and pediatric patients with neurotrophic receptor tyrosine kinase fusion-positive advanced solid tumors. Int. J. Clin. Oncol. 2023 28 7 827 840 10.1007/s10147‑023‑02345‑7 37212982
    [Google Scholar]
  23. Sochacka-Ćwikła A. Mączyński M. Regiec A. FDA-Approved small molecule compounds as drugs for solid cancers from early 2011 to the end of 2021. Molecules 2022 27 7 2259 10.3390/molecules27072259 35408658
    [Google Scholar]
  24. Vasina M. Velecký J. Planas-Iglesias J. Marques S.M. Skarupova J. Damborsky J. Bednar D. Mazurenko S. Prokop Z. Tools for computational design and high-throughput screening of therapeutic enzymes. Adv. Drug Deliv. Rev. 2022 183 114143 10.1016/j.addr.2022.114143 35167900
    [Google Scholar]
  25. Zhu S. Wu M. Huang Z. An J. Trends in application of advancing computational approaches in GPCR ligand discovery Exp Biol Med (Maywood). 2021 246 9 1011 1024 10.1177/1535370221993422
    [Google Scholar]
  26. Tropsha A. Isayev O. Varnek A. Schneider G. Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: The emergence of deep QSAR. Nat. Rev. Drug Discov. 2023 2023 1 15 10.1038/s41573‑023‑00832‑0 38066301
    [Google Scholar]
  27. Azad I. Khan T. Ahmad N. Khan A.R. Akhter Y. Updates on drug designing approach through computational strategies: A review. Future Sci. OA 2023 9 5 FSO862 10.2144/fsoa‑2022‑0085 37180609
    [Google Scholar]
  28. Manathunga M. Götz A.W. Merz K.M. Jr Computer-aided drug design, quantum-mechanical methods for biological problems. Curr. Opin. Struct. Biol. 2022 75 102417 10.1016/j.sbi.2022.102417 35779437
    [Google Scholar]
  29. Sharma V. Wakode S. Kumar H. Structure- and ligand-based drug design: Concepts, approaches, and challenges. Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences Academic Press 2021 27 53 10.1016/B978‑0‑12‑821748‑1.00004‑X
    [Google Scholar]
  30. Agarwal P. Huckle J. Newman J. Reid D.L. Trends in small molecule drug properties: A developability molecule assessment perspective. Drug Discov. Today 2022 27 12 103366 10.1016/j.drudis.2022.103366 36122862
    [Google Scholar]
  31. Tang S. Davoudi Z. Wang G. Xu Z. Rehman T. Prominski A. Tian B. Bratlie K.M. Peng H. Wang Q. Soft materials as biological and artificial membranes. Chem. Soc. Rev. 2021 50 22 12679 12701 10.1039/D1CS00029B 34636824
    [Google Scholar]
  32. Ayipo Y.O. Ahmad I. Chong C.F. Zainurin N.A. Najib S.Y. Patel H. Mordi M.N. Carbazole derivatives as promising competitive and allosteric inhibitors of human serotonin transporter: Computational pharmacology. J. Biomol. Struct. Dyn. 2023 10.1080/07391102.2023.2198016 37021485
    [Google Scholar]
  33. Beck H. Härter M. Haß B. Schmeck C. Baerfacker L. Small molecules and their impact in drug discovery: A perspective on the occasion of the 125th anniversary of the bayer chemical research laboratory. Drug Discov. Today 2022 27 6 1560 1574 10.1016/j.drudis.2022.02.015 35202802
    [Google Scholar]
  34. Stanzione F. Giangreco I. Cole J.C. Use of molecular docking computational tools in drug discovery. Prog. Med. Chem. 2021 60 273 343 10.1016/bs.pmch.2021.01.004 34147204
    [Google Scholar]
  35. Sen S. Won M. Levine M.S. Noh Y. Sedgwick A.C. Kim J.S. Sessler J.L. Arambula J.F. Metal-based anticancer agents as immunogenic cell death inducers: the past, present, and future. Chem. Soc. Rev. 2022 51 4 1212 1233 10.1039/D1CS00417D 35099487
    [Google Scholar]
  36. Guedeney N. Cornu M. Schwalen F. Kieffer C. Voisin-Chiret A.S. PROTAC technology: A new drug design for chemical biology with many challenges in drug discovery. Drug Discov. Today 2023 28 1 103395 10.1016/j.drudis.2022.103395 36228895
    [Google Scholar]
  37. Upton D.H. Ung C. George S.M. Tsoli M. Kavallaris M. Ziegler D.S. Challenges and opportunities to penetrate the blood-brain barrier for brain cancer therapy. Theranostics 2022 12 10 4734 4752 10.7150/thno.69682 35832071
    [Google Scholar]
  38. Ragelle H. Rahimian S. Guzzi E.A. Westenskow P.D. Tibbitt M.W. Schwach G. Langer R. Additive manufacturing in drug delivery: Innovative drug product design and opportunities for industrial application. Adv. Drug Deliv. Rev. 2021 178 113990 10.1016/j.addr.2021.113990 34600963
    [Google Scholar]
  39. Asran A.M. Mohamed M.A. Khedr G.E. Eldin G.M.G. Yehia A.M. Mishra R.K. Allam N.K. Investigation of the thermal stability of the antihypertensive drug nebivolol under different conditions: Experimental and computational analysis. J. Therm. Anal. Calorim. 2022 147 10 5779 5786 10.1007/s10973‑021‑10893‑1
    [Google Scholar]
  40. Bond M.J. Crews C.M. Proteolysis targeting chimeras (PROTACs) come of age: Entering the third decade of targeted protein degradation. RSC Chem. Biol. 2021 2 3 725 742 10.1039/D1CB00011J 34212149
    [Google Scholar]
  41. Henley M.J. Koehler A.N. Advances in targeting ‘undruggable’ transcription factors with small molecules. Nat. Rev. Drug Discov. 2021 20 9 669 688 10.1038/s41573‑021‑00199‑0 34006959
    [Google Scholar]
  42. Bertonha A.F. Silva C.C.L. Shirakawa K.T. Trindade D.M. Dessen A. Penicillin-binding protein (PBP) inhibitor development: A 10-year chemical perspective. Exp. Biol. Med. (Maywood) 2023 248 19 1657 1670 10.1177/15353702231208407 38030964
    [Google Scholar]
  43. Oláh J. Szénási T. Lehotzky A. Norris V. Ovádi J. Challenges in discovering drugs that target the protein–protein interactions of disordered proteins. Int. J. Mol. Sci. 2022 23 3 1550 10.3390/ijms23031550 35163473
    [Google Scholar]
  44. Loughlin F.E. West D.L. Gunzburg M.J. Waris S. Crawford S.A. Wilce M.C.J. Wilce J.A. Tandem RNA binding sites induce self-association of the stress granule marker protein TIA-1. Nucleic Acids Res. 2021 49 5 2403 2417 10.1093/nar/gkab080 33621982
    [Google Scholar]
  45. De Cecco M. Galbraith D.N. McDermott L.L. What makes a good antibody–drug conjugate? Expert Opin. Biol. Ther. 2021 21 7 841 847 10.1080/14712598.2021.1880562 33605810
    [Google Scholar]
  46. Drago J.Z. Modi S. Chandarlapaty S. Unlocking the potential of antibody–drug conjugates for cancer therapy. Nat. Rev. Clin. Oncol. 2021 18 6 327 344 10.1038/s41571‑021‑00470‑8 33558752
    [Google Scholar]
  47. Verma S. Goand U.K. Husain A. Katekar R.A. Garg R. Gayen J.R. Challenges of peptide and protein drug delivery by oral route: Current strategies to improve the bioavailability. Drug Dev. Res. 2021 82 7 927 944 10.1002/ddr.21832 33988872
    [Google Scholar]
  48. Myšková A. Sýkora D. Kuneš J. Maletínská L. Lipidization as a tool toward peptide therapeutics. Drug Deliv. 2023 30 1 2284685 10.1080/10717544.2023.2284685 38010881
    [Google Scholar]
  49. Helmstädter J. Keppeler K. Küster L. Münzel T. Daiber A. Steven S. Glucagon‐like peptide‐1 (GLP‐1) receptor agonists and their cardiovascular benefits—The role of the GLP‐1 receptor. Br. J. Pharmacol. 2022 179 4 659 676 10.1111/bph.15462 33764504
    [Google Scholar]
  50. Laroussi M. Bekeschus S. Keidar M. Bogaerts A. Fridman A. Lu X. Ostrikov K. Hori M. Stapelmann K. Miller V. Reuter S. Laux C. Mesbah A. Walsh J. Jiang C. Thagard S.M. Tanaka H. Liu D. Yan D. Yusupov M. Low-temperature plasma for biology, hygiene, and medicine: Perspective and roadmap. IEEE Trans. Radiat. Plasma Med. Sci. 2022 6 2 127 157 10.1109/TRPMS.2021.3135118
    [Google Scholar]
  51. Alhakamy N.A. Curiel D.T. Berkland C.J. The era of gene therapy: From preclinical development to clinical application. Drug Discov. Today 2021 26 7 1602 1619 10.1016/j.drudis.2021.03.021 33781953
    [Google Scholar]
  52. Sheikh O. Yokota T. Developing DMD therapeutics: A review of the effectiveness of small molecules, stop-codon readthrough, dystrophin gene replacement, and exon-skipping therapies. Expert Opin. Investig. Drugs 2021 30 2 167 176 10.1080/13543784.2021.1868434 33393390
    [Google Scholar]
  53. Ren J. Feng X. Guo Y. Kong D. Wang Y. Xiao J. Jiang W. Feng X. Liu X. Li A. Sun C. He M. Li B. Wang J. Jiang Y. Zheng C. GSK‐3β/β‐catenin pathway plays crucial roles in the regulation of NK cell cytotoxicity against myeloma cells. FASEB J. 2023 37 3 e22821 10.1096/fj.202201658RR 36794671
    [Google Scholar]
  54. Bucher K. Rodríguez-Bocanegra E. Dauletbekov D. Fischer M.D. Immune responses to retinal gene therapy using adeno-associated viral vectors – Implications for treatment success and safety. Prog. Retin. Eye Res. 2021 83 100915 10.1016/j.preteyeres.2020.100915 33069860
    [Google Scholar]
  55. K Shashank Modern cancer therapies and traditional medicine: An integrative approach to combat cancers Bentham Science Publishers 2021
    [Google Scholar]
  56. Nguyen N.T. Characterizing the metabolic fate of (2S, 4R)-4-[18F]fluoroglutamine and its applications in cancer imaging and treatment response monitoring 2021 Available from: https://repository.icr.ac.uk/items/d438464e-7881-4b68-bf6d-3f0675e42163
  57. Jamil A.S. Saputro P.G. Saputro P. Saputro P. Molecular docking and adme studies of centella asiatica as anti hyperuricemia. Pharmacogn. J. 2023 15 2 384 389 10.5530/pj.2023.15.59
    [Google Scholar]
  58. Rakshit G. Murtuja S. Jayaprakash V. Kumar B.K. Murugesan S. Computer Aided Drug Design (CADD) From Ligand-Based Methods to Struct. Approaches Elsevier 2022 181 229 10.1016/B978‑0‑323‑90608‑1.00003‑4
    [Google Scholar]
  59. Khan M.K.A. Akhtar S. Novel drug design and bioinformatics: An introduction. Phys. Sci. Rev. 2023 8 8 1571 1591 10.1515/psr‑2018‑0158
    [Google Scholar]
  60. Bhunia S.S. Saxena M. Saxena A.K. Ligand-and structure-based virtual screening in drug discovery. Topics in Medicinal Chemistry Springer Berlin, Heidelberg 2021 1 59 10.1007/7355_2021_130/COVER
    [Google Scholar]
  61. Yanagisawa K. Kubota R. Yoshikawa Y. Ohue M. Akiyama Y. Effective protein–ligand docking strategy via fragment reuse and a proof-of-concept implementation. ACS Omega 2022 7 34 30265 30274 10.1021/acsomega.2c03470 36061673
    [Google Scholar]
  62. Han R. Yoon H. Kim G. Lee H. Lee Y. Revolutionizing medicinal chemistry: The application of artificial intelligence (AI) in early drug discovery. Pharmaceuticals (Basel) 2023 16 9 1259 10.3390/ph16091259 37765069
    [Google Scholar]
  63. Sayam A.G. Pradhan M. Choudhury A.K. Artificial intelligence the futuristic technology in the drug discovery process: A review. J. Young Pharm. 2023 15 3 390 396 10.5530/jyp.2023.15.54
    [Google Scholar]
  64. 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 10.3390/ijms22041676 33562347
    [Google Scholar]
  65. Meyers J. Fabian B. Brown N. De novo molecular design and generative models. Drug Discov. Today 2021 26 11 2707 2715 10.1016/j.drudis.2021.05.019 34082136
    [Google Scholar]
  66. Xia X. Hu J. Wang Y. Zhang L. Liu Z. Graph-based generative models for de Novo drug design. Drug Discov. Today. Technol. 2019 32-33 45 53 10.1016/j.ddtec.2020.11.004 33386094
    [Google Scholar]
  67. Moussa N. Hassan A. Gharaghani S. Pharmacophore model, docking, QSAR, and molecular dynamics simulation studies of substituted cyclic imides and herbal medicines as COX-2 inhibitors. Heliyon. 2017 7 4 e06605 10.1016/j.heliyon.2021.e06605
    [Google Scholar]
  68. Cho H. Wu M. Bilgin B. Walton S.P. Chan C. Latest developments in experimental and computational approaches to characterize protein–lipid interactions. Proteomics 2012 12 22 3273 3285 10.1002/pmic.201200255 22997137
    [Google Scholar]
  69. Sonaye H.V. Sheikh R.Y. Doifode C.A. Drug repurposing: Iron in the fire for older drugs. Biomed. Pharmacother. 2021 141 111638 10.1016/j.biopha.2021.111638 34153846
    [Google Scholar]
  70. Usha T. Middha S.K. Kukanur A.A. Shravani R.V. Anupama M.N. Harshitha N. Rahamath A. Kulkarni S.S. Goyal A.K. Drug repurposing approaches: Existing leads for novel threats and drug targets. Curr. Protein Pept. Sci. 2021 22 3 251 271 10.2174/1389203721666200921152853 32957901
    [Google Scholar]
  71. Krishnamurthy N. Grimshaw A.A. Axson S.A. Choe S.H. Miller J.E. Drug repurposing: A systematic review on root causes, barriers and facilitators. BMC Health Serv. Res. 2022 22 1 970 10.1186/s12913‑022‑08272‑z 35906687
    [Google Scholar]
  72. Sahoo B.M. Ravi Kumar B.V.V. Sruti J. Mahapatra M.K. Banik B.K. Borah P. Drug repurposing strategy (DRS): Emerging approach to identify potential therapeutics for treatment of novel coronavirus infection. Front. Mol. Biosci. 2021 8 628144 10.3389/fmolb.2021.628144 33718434
    [Google Scholar]
  73. Gaitonde V. Drug discovery and development : New advances IntechOpen London, UK 2020 10.5772/intechopen.77685
    [Google Scholar]
  74. Asiamah I. Obiri S.A. Tamekloe W. Armah F.A. Borquaye L.S. Applications of molecular docking in natural products-based drug discovery. Sci. Afr. 2023 20 e01593 10.1016/j.sciaf.2023.e01593
    [Google Scholar]
  75. C S. S D.K. Ragunathan V. Tiwari P. A S. P B.D. Molecular docking, validation, dynamics simulations, and pharmacokinetic prediction of natural compounds against the SARS-CoV-2 main-protease. J. Biomol. Struct. Dyn. 2022 40 2 585 611 10.1080/07391102.2020.1815584
    [Google Scholar]
  76. Panossian A.G. Efferth T. Shikov A.N. Pozharitskaya O.N. Kuchta K. Mukherjee P.K. Banerjee S. Heinrich M. Wu W. Guo D. Wagner H. Evolution of the adaptogenic concept from traditional use to medical systems: Pharmacology of stress‐ and aging‐related diseases. Med. Res. Rev. 2021 41 1 630 703 10.1002/med.21743 33103257
    [Google Scholar]
  77. Burley S.K. Berman H.M. Duarte J.M. Feng Z. Flatt J.W. Hudson B.P. Lowe R. Peisach E. Piehl D.W. Rose Y. Sali A. Sekharan M. Shao C. Vallat B. Voigt M. Westbrook J.D. Young J.Y. Zardecki C. Protein data bank: A comprehensive review of 3D structure holdings and worldwide utilization by researchers, educators, and students. Biomolecules 2022 12 10 1425 10.3390/biom12101425 36291635
    [Google Scholar]
  78. Choudhary P. Anyango S. Berrisford J. Varadi M. Tolchard J. Velankar S. Unified access to up-to-date residue-level annotations from UniProt and other biological databases for PDB data via PDBx/mmCIF files BioRxiv 2022 2022.08 10.1101/2022.08.10.503473
    [Google Scholar]
  79. Ferreira L.G. Dos Santos R.N. Oliva G. Andricopulo A.D. Molecular docking and structure-based drug design strategies. Molecules. 2015 20 7 13384 13421 10.3390/molecules200713384
    [Google Scholar]
  80. Andreani J. Quignot C. Guerois R. Structural prediction of protein interactions and docking using conservation and coevolution. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2020 10 6 e1470 10.1002/wcms.1470
    [Google Scholar]
  81. El-Hela A.A. Bakr M.S.A. Hegazy M.M. Dahab M.A. Elmaaty A.A. Ibrahim A.E. El Deeb S. Abbass H.S. Phytochemical characterization of pterocephalus frutescens with in-silico evaluation as chemotherapeutic medicine and oral pharmacokinetics prediction study. Sci. Pharm. 2023 91 1 7 10.3390/scipharm91010007
    [Google Scholar]
  82. Guterres H. Im W. Improving protein-ligand docking results with high-throughput molecular dynamics simulations. J. Chem. Inf. Model. 2020 60 4 2189 2198 10.1021/acs.jcim.0c00057 32227880
    [Google Scholar]
  83. Azad I. Azad I. Molecular docking in the study of ligand-protein recognition: An overview. Biomedical Engineering. IntechOpen London, UK. 2023 10.5772/intechopen.106583
    [Google Scholar]
  84. Olson A. AutoDock software development and maintenance 20114 Available from: https://grantome.com/grant/NIH/R01-GM069832-11accessed March 6, 2024
  85. Trott O. Olson A.J. AutoDock vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010 31 2 455 461 10.1002/jcc.21334 19499576
    [Google Scholar]
  86. Sarkar A. Concilio S. Sessa L. Marrafino F. Piotto S. Advancements and novel approaches in modified AutoDock Vina algorithms for enhanced molecular docking. Results Chem. 2024 7 101319 10.1016/j.rechem.2024.101319
    [Google Scholar]
  87. Ravindranath P.A. Forli S. Goodsell D.S. Olson A.J. Sanner M.F. AutoDockFR: Advances in protein-ligand docking with explicitly specified binding site flexibility. PLOS Comput. Biol. 2015 11 12 e1004586 10.1371/journal.pcbi.1004586 26629955
    [Google Scholar]
  88. Hsu K.C. Chen Y.F. Lin S.R. Yang J.M. iGEMDOCK: A graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis. BMC Bioinformatics 2011 12 S1 Suppl. 1 S33 10.1186/1471‑2105‑12‑S1‑S33 21342564
    [Google Scholar]
  89. Bhowmik D. Nandi R. Prakash A. Kumar D. Evaluation of flavonoids as 2019-nCoV cell entry inhibitor through molecular docking and pharmacological analysis. Heliyon 2021 7 3 e06515 10.1016/j.heliyon.2021.e06515 33748510
    [Google Scholar]
  90. Vincent S. Arokiyaraj S. Saravanan M. Dhanraj M. Molecular docking studies on the anti-viral effects of compounds from kabasura kudineer on SARS-CoV-2 3CLpro. Front. Mol. Biosci. 2020 7 613401 10.3389/fmolb.2020.613401 33425994
    [Google Scholar]
  91. Salo-Ahen O.M.H. Alanko I. Bhadane R. Alexandre A.M. Honorato R.V. Hossain S. Juffer A.H. Kabedev A. Lahtela-Kakkonen M. Larsen A.S. Lescrinier E. Marimuthu P. Mirza M.U. Mustafa G. Nunes-Alves A. Pantsar T. Saadabadi A. Singaravelu K. Vanmeert M. Molecular dynamics simulations in drug discovery and pharmaceutical development Processes 2021 9 1 71 10.3390/pr9010071
    [Google Scholar]
  92. Li L. Liu S. Wang B. Liu F. Xu S. Li P. Chen Y. An updated review on developing small molecule kinase inhibitors using computer-aided drug design approaches. Int. J. Mol. Sci. 2023 24 18 13953 10.3390/ijms241813953 37762253
    [Google Scholar]
  93. Rajasekhar S. Karuppasamy R. Chanda K. Exploration of potential inhibitors for tuberculosis via structure‐based drug design, molecular docking, and molecular dynamics simulation studies. J. Comput. Chem. 2021 42 24 1736 1749 10.1002/jcc.26712 34216033
    [Google Scholar]
  94. Shukla R. Tripathi T. Molecular dynamics simulation in drug discovery: Opportunities and challenges Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design Springer Singapore 2021 1 295 316
    [Google Scholar]
  95. Jakhar R. Dangi M. Khichi A. Chhillar A.K. Relevance of molecular docking studies in drug designing. Curr. Bioinform. 2020 15 4 270 278 10.2174/1574893615666191219094216
    [Google Scholar]
  96. Gaspar R. Coelho F. Silva B.F.B. Lipid-nucleic acid complexes: Physicochemical aspects and prospects for cancer treatment. Molecules 2020 25 21 5006 10.3390/molecules25215006 33126767
    [Google Scholar]
  97. Phatak S.S. Stephan C.C. Cavasotto C.N. High-throughput and in silico screenings in drug discovery. Expert Opin. Drug Discov. 2009 4 9 947 959 10.1517/17460440903190961 23480542
    [Google Scholar]
  98. Lima L.R. Bastos R.S. Ferreira E.F.B. Leão R.P. Araújo P.H.F. Pita S.S.R. De Freitas H.F. Espejo-Román J.M. Dos Santos E.L.V.S. Ramos R.S. Macêdo W.J.C. Santos C.B.R. Identification of potential new Aedes aegypti juvenile hormone inhibitors from N-Acyl piperidine derivatives: A bioinformatics approach. Int. J. Mol. Sci. 2022 23 17 9927 10.3390/ijms23179927 36077329
    [Google Scholar]
  99. Ekins S. Balakin K.V. Savchuk N. Ivanenkov Y. Insights for human ether-a-go-go-related gene potassium channel inhibition using recursive partitioning and kohonen and sammon mapping techniques. J. Med. Chem. 2006 49 17 5059 5071 10.1021/jm060076r 16913696
    [Google Scholar]
  100. Wu Y.J. Meanwell N.A. Geminal diheteroatomic motifs: Some applications of acetals, ketals, and their sulfur and nitrogen homologues in medicinal chemistry and drug design. J. Med. Chem. 2021 64 14 9786 9874 10.1021/acs.jmedchem.1c00790 34213340
    [Google Scholar]
  101. Maia E.H.B. Assis L.C. de Oliveira T.A. da Silva A.M. Taranto A.G. Marques da Silva A. Gutterres Taranto A. Structure-based virtual screening: From classical to artificial intelligence. Front Chem. 2020 8 343 10.3389/fchem.2020.00343 32411671
    [Google Scholar]
  102. Yadav M.R. Murumkar P.R. Yadav R. Joshi K. Structure-based virtual screening in drug discovery Cheminformatics, QSAR and Machine Learning Applications for Novel Drug development Academic Press 2023 69 88 10.1016/B978‑0‑443‑18638‑7.00006‑2
    [Google Scholar]
  103. Sohraby F. Aryapour H. Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs. Semin. Cancer Biol. 2021 68 249 257 10.1016/j.semcancer.2020.04.007 32360530
    [Google Scholar]
  104. Vázquez J. López M. Gibert E. Herrero E. Luque F.J. Merging ligand-based and structure-based methods in drug discovery: An overview of combined virtual screening approaches. Molecules 2020 25 20 4723 10.3390/molecules25204723 33076254
    [Google Scholar]
  105. Berrhail F. Belhadef H. Genetic algorithm-based feature selection approach for enhancing the effectiveness of similarity searching in ligand-based virtual screening. Curr. Bioinform. 2020 15 5 431 444 10.2174/1574893614666191119123935
    [Google Scholar]
  106. Singh N. Chaput L. Villoutreix B.O. Fast rescoring protocols to improve the performance of structure-based virtual screening performed on protein–protein interfaces. J. Chem. Inf. Model. 2020 60 8 3910 3934 10.1021/acs.jcim.0c00545 32786511
    [Google Scholar]
  107. Herrera-Acevedo C. Dos Santos Maia M. Cavalcanti É.B.V.S. Coy-Barrera E. Scotti L. Scotti M.T. Selection of antileishmanial sesquiterpene lactones from SistematX database using a combined ligand-/structure-based virtual screening approach. Mol. Divers. 2021 25 4 2411 2427 10.1007/s11030‑020‑10139‑6 32909084
    [Google Scholar]
  108. Luo L. Zhong A. Wang Q. Zheng T. Structure-based pharmacophore modeling, virtual screening, molecular docking, ADMET, and molecular dynamics (MD) simulation of potential inhibitors of PD-L1 from the library of marine natural products. Mar Drugs. 2021 20 1 29 10.3390/md20010029
    [Google Scholar]
  109. Mahdavi S.Z.B. Oroojalian F. Eyvazi S. Hejazi M. Baradaran B. Pouladi N. Tohidkia M.R. Mokhtarzadeh A. Muyldermans S. An overview on display systems (phage, bacterial, and yeast display) for production of anticancer antibodies; advantages and disadvantages. Int. J. Biol. Macromol. 2022 208 421 442 10.1016/j.ijbiomac.2022.03.113 35339499
    [Google Scholar]
  110. Carracedo-Reboredo P. Liñares-Blanco J. Rodríguez-Fernández N. Cedrón F. Novoa F.J. Carballal A. Maojo V. Pazos A. Fernandez-Lozano C. A review on machine learning approaches and trends in drug discovery. Comput. Struct. Biotechnol. J. 2021 19 4538 4558 10.1016/j.csbj.2021.08.011 34471498
    [Google Scholar]
  111. Giangreco I. Mukhopadhyay A. Cole J.C. Validation of a field-based ligand screener using a novel benchmarking data set for assessing 3D-Based Virtual Screening Methods. J. Chem. Inf. Model. 2021 61 12 5841 5852 10.1021/acs.jcim.1c00866 34792345
    [Google Scholar]
  112. Velmurugan D. Gayathri D. Ramakrishnan C. Bhattacharjee A. Protein–ligand interactions in molecular modeling and structure-based drug design. Anal. Appl. 2020 ••• 351 369 10.1142/9789811211874_0014
    [Google Scholar]
  113. Moros M. Lewinska A. Merola F. Ferraro P. Wnuk M. Tino A. Tortiglione C. Gold nanorods and nanoprisms mediate different photothermal cell death mechanisms in vitro and in vivo. ACS Appl. Mater. Interfaces 2020 12 12 13718 13730 10.1021/acsami.0c02022 32134240
    [Google Scholar]
  114. Sivakumar K.C. Haixiao J. Naman C.B. Sajeevan T.P. Prospects of multitarget drug designing strategies by linking molecular docking and molecular dynamics to explore the protein–ligand recognition process. Drug Dev. Res. 2020 81 6 685 699 10.1002/ddr.21673 32329098
    [Google Scholar]
  115. Wang Z. Pan H. Sun H. Kang Y. Liu H. Cao D. Hou T. fastDRH: A webserver to predict and analyze protein–ligand complexes based on molecular docking and MM/PB(GB)SA computation. Brief. Bioinform. 2022 23 5 bbac201 10.1093/bib/bbac201 35580866
    [Google Scholar]
  116. Miryala S.K. Basu S. Naha A. Debroy R. Ramaiah S. Anbarasu A. Natarajan S. Identification of bioactive natural compounds as efficient inhibitors against Mycobacterium tuberculosis protein-targets: A molecular docking and molecular dynamics simulation study. J. Mol. Liq. 2021 341 117340 10.1016/j.molliq.2021.117340
    [Google Scholar]
  117. Filipe H.A.L. Loura L.M.S. Molecular dynamics simulations: Advances and applications. Molecules 2022 27 7 2105 10.3390/molecules27072105 35408504
    [Google Scholar]
  118. Shen K.H. Fan M. Hall L.M. Molecular dynamics simulations of ion-containing polymers using generic coarse-grained models. Macromolecules 2021 54 5 2031 2052 10.1021/acs.macromol.0c02557
    [Google Scholar]
  119. Zhang X. Sundram S. Oppelstrup T. Kokkila-Schumacher S.I.L. Carpenter T.S. Ingólfsson H.I. Streitz F.H. Lightstone F.C. Glosli J.N. Ddc M.D. ddcMD: A fully GPU-accelerated molecular dynamics program for the Martini force field. J. Chem. Phys. 2020 153 4 045103 10.1063/5.0014500 32752727
    [Google Scholar]
  120. Minezawa N. Nakajima T. Quantum mechanical/molecular mechanical trajectory surface hopping molecular dynamics simulation by spin-flip time-dependent density functional theory. J. Chem. Phys. 2020 152 2 024119 10.1063/1.5132879 31941312
    [Google Scholar]
  121. Snyder R. Kim B. Pan X. Shao Y. Pu J. Bridging semiempirical and ab initio QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation. J. Chem. Phys. 2023 159 5 054107 10.1063/5.0156327 37530109
    [Google Scholar]
  122. Walters W.P. Barzilay R. Applications of deep learning in molecule generation and molecular property prediction. Acc. Chem. Res. 2021 54 2 263 270 10.1021/acs.accounts.0c00699 33370107
    [Google Scholar]
  123. Zhu S. Gilbert M. Chetty I. Siddiqui F. The 2021 landscape of FDA-approved artificial intelligence/machine learning-enabled medical devices: An analysis of the characteristics and intended use. Int. J. Med. Inform. 2022 165 104828 10.1016/j.ijmedinf.2022.104828 35780651
    [Google Scholar]
  124. Mohsen A. Tripathi L.P. Mizuguchi K. Deep learning prediction of adverse drug reactions in drug discovery using open TG–GATEs and FAERS databases. Front. Drug Discov. (Lausanne) 2021 1 768792 10.3389/fddsv.2021.768792
    [Google Scholar]
  125. Garofalo M. Grazioso G. Cavalli A. Sgrignani J. How computational chemistry and drug delivery techniques can support the development of new anticancer drugs. Molecules. 2020 25 7 1756 10.3390/molecules25071756
    [Google Scholar]
  126. Alimardan Z. Abbasi M. Khodarahmi G. Kashfi K. Hasanzadeh F. Mahmud A. Identification of new small molecules as dual FoxM1 and Hsp70 inhibitors using computational methods. Res. Pharm. Sci. 2022 17 6 635 656 10.4103/1735‑5362.359431 36704430
    [Google Scholar]
  127. Matthews H.K. Bertoli C. de Bruin R.A.M. Cell cycle control in cancer. Nat. Rev. Mol. Cell Biol. 2022 23 1 74 88 10.1038/s41580‑021‑00404‑3 34508254
    [Google Scholar]
  128. Iwaloye O. Ottu P.O. Olawale F. Babalola O.O. Elekofehinti O.O. Kikiowo B. Adegboyega A.E. Ogbonna H.N. Adeboboye C.F. Folorunso I.M. Fakayode A.E. Akinjiyan M.O. Onikanni S.A. Shityakov S. Computer-aided drug design in anti-cancer drug discovery: What have we learnt and what is the way forward? Inform. Med. Unlocked 2023 41 101332 10.1016/j.imu.2023.101332
    [Google Scholar]
  129. Munisamy M. Mukherjee N. Thomas L. Pham A.T. Shakeri A. Zhao Y. Kolesar J. Rao P.P.N. Rangnekar V.M. Rao M. Therapeutic opportunities in cancer therapy: Targeting the p53-MDM2/MDMX interactions. Am. J. Cancer Res. 2021 11 12 5762 5781 35018225
    [Google Scholar]
  130. Anifowose A. Agbowuro A.A. Yang X. Wang B. Anticancer strategies by upregulating p53 through inhibition of its ubiquitination by MDM2. Med. Chem. Res. 2020 29 7 1105 1121 10.1007/s00044‑020‑02574‑9
    [Google Scholar]
  131. Zhu H. Gao H. Ji Y. Zhou Q. Du Z. Tian L. Jiang Y. Yao K. Zhou Z. Targeting p53–MDM2 interaction by small-molecule inhibitors: Learning from MDM2 inhibitors in clinical trials. J. Hematol. Oncol. 2022 15 1 91 10.1186/s13045‑022‑01314‑3 35831864
    [Google Scholar]
  132. Mahdy H.A. Ibrahim M.K. Metwaly A.M. Belal A. Mehany A.B.M. El-Gamal K.M.A. El-Sharkawy A. Elhendawy M.A. Radwan M.M. Elsohly M.A. Eissa I.H. Design, synthesis, molecular modeling, in vivo studies and anticancer evaluation of quinazolin-4(3H)-one derivatives as potential VEGFR-2 inhibitors and apoptosis inducers. Bioorg. Chem. 2020 94 103422 10.1016/j.bioorg.2019.103422 31812261
    [Google Scholar]
  133. Liu X.J. Zhao H.C. Hou S.J. Zhang H.J. Cheng L. Yuan S. Zhang L.R. Song J. Zhang S.Y. Chen S.W. Recent development of multi-target VEGFR-2 inhibitors for the cancer therapy. Bioorg. Chem. 2023 133 106425 10.1016/j.bioorg.2023.106425 36801788
    [Google Scholar]
  134. Nirmaladevi R. Paital B. Jayachandran P. Padma P.R. Nirmaladevi R. Epigenetic alterations in cancer. Front. Biosci. 2020 25 6 1058 1109 10.2741/4847 32114424
    [Google Scholar]
  135. Sarkies P. Molecular mechanisms of epigenetic inheritance: Possible evolutionary implications. Semin. Cell Dev. Biol. 2020 97 106 115 10.1016/j.semcdb.2019.06.005 31228598
    [Google Scholar]
  136. Milazzo G. Mercatelli D. Di Muzio G. Triboli L. De Rosa P. Perini G. Giorgi F.M. Histone deacetylases (HDACs): Evolution, specificity, role in transcriptional complexes, and pharmacological actionability. Genes (Basel) 2020 11 5 556 10.3390/genes11050556 32429325
    [Google Scholar]
  137. Núñez-Álvarez Y. Suelves M. HDAC11: A multifaceted histone deacetylase with proficient fatty deacylase activity and its roles in physiological processes. FEBS J. 2022 289 10 2771 2792 10.1111/febs.15895 33891374
    [Google Scholar]
  138. de Araújo R.S.A. da Silva-Junior E.F. de Aquino T.M. Scotti M.T. Ishiki H.M. Scotti L. Mendonça-Junior F.J.B. Computer-aided drug design applied to secondary metabolites as anticancer agents. Curr. Top. Med. Chem. 2020 20 19 1677 1703 10.2174/1568026620666200607191838 32515312
    [Google Scholar]
  139. Cui W. Aouidate A. Wang S. Yu Q. Li Y. Yuan S. Discovering anti-cancer drugs via computational methods. Front. Pharmacol. 2020 11 733 10.3389/fphar.2020.00733 32508653
    [Google Scholar]
  140. Wang L. Song Y. Wang H. Zhang X. Wang M. He J. Li S. Zhang L. Li K. Cao L. Advances of artificial intelligence in anti-cancer drug design: A review of the past decade. Pharmaceuticals (Basel) 2023 16 2 253 10.3390/ph16020253 37259400
    [Google Scholar]
  141. Karwasra R. Khanna K. Singh S. Ahmad S. Verma S. The incipient role of computational intelligence in oncology: Drug designing, discovery, and development. Computational Intelligence in Oncology. Studies in Computational Intelligence Springer Singapore 2022 369 384 10.1007/978‑981‑16‑9221‑5_21
    [Google Scholar]
  142. Campos M.R.S. del Carmen Quintal Bojórquez N. Traditional and novel computer-aided drug design (CADD) approaches in the anticancer drug discovery process. Curr. Cancer Drug Targets 2023 23 5 333 345 10.2174/1568009622666220705104249 35792126
    [Google Scholar]
  143. Biswas A. Jayaprakash V. CADD approaches in anticancer drug discovery CADD and Informatics in Drug Discovery ResearchGate 2023 283 311 10.1007/978‑981‑99‑1316‑9_12
    [Google Scholar]
  144. Majumder A. Gupta A.K. Ghosal P.S. Varma M. A review on hospital wastewater treatment: A special emphasis on occurrence and removal of pharmaceutically active compounds, resistant microorganisms, and SARS-CoV-2. J. Environ. Chem. Eng. 2021 9 2 104812 10.1016/j.jece.2020.104812 33251108
    [Google Scholar]
  145. Manna S. Loeffler T.D. Batra R. Banik S. Chan H. Varughese B. Sasikumar K. Sternberg M. Peterka T. Cherukara M.J. Gray S.K. Sumpter B.G. Sankaranarayanan S.K.R.S. Learning in continuous action space for developing high dimensional potential energy models. Nat. Commun. 2022 13 1 368 10.1038/s41467‑021‑27849‑6 35042872
    [Google Scholar]
  146. Gupta Y. Maciorowski D. Mathur R. Pearce C.M. Ilc D.J. Husein H. Bharti A. Becker D. Brijesh R. Bradfute S.B. Durvasula R. Kempaiah P. Revealing SARS-CoV-2 functional druggability through multi-target cadd screening of repurposable drugs Pharmacol Toxicol. 2020 10.20944/preprints202005.0199.v1
    [Google Scholar]
  147. Zhang K. Wu M. Liu Y. Feng Y. Zheng J. KR4SL: Knowledge graph reasoning for explainable prediction of synthetic lethality. Bioinformatics 2023 39 39 Suppl. 1 i158 i167 10.1093/bioinformatics/btad261 37387166
    [Google Scholar]
  148. Jin B. Xu X. Wholesale price forecasts of green grams using the neural network Asian J. Econ. Bank. 2024 10.1108/AJEB‑01‑2024‑0007
    [Google Scholar]
  149. Jin B. Xu X. Price forecasting through neural networks for crude oil, heating oil, and natural gas. Meas Energy. 2024 1 100001 10.1016/j.meaene.2024.100001
    [Google Scholar]
  150. Krasoulis A. Antonopoulos N. Pitsikalis V. Theodorakis S. DENVIS: Scalable and high-throughput virtual screening using graph neural networks with atomic and surface protein pocket features. J. Chem. Inf. Model. 2022 62 19 4642 4659 10.1021/acs.jcim.2c01057 36154119
    [Google Scholar]
  151. Buranda T. Gineste C. Wu Y. Bondu V. Perez D. Lake K.R. Edwards B.S. Sklar L.A. A high-throughput flow cytometry screen identifies molecules that inhibit hantavirus cell entry. SLAS Discov. 2018 23 7 634 645 10.1177/2472555218766623 29608398
    [Google Scholar]
  152. Congreve M. Chessari G. Tisi D. Woodhead A.J. Recent developments in fragment-based drug discovery. J. Med. Chem. 2008 51 13 3661 3680 10.1021/jm8000373 18457385
    [Google Scholar]
  153. Keserű G.M. Erlanson D.A. Ferenczy G.G. Hann M.M. Murray C.W. Pickett S.D. Design principles for fragment libraries: Maximizing the value of learnings from pharma fragment-based drug discovery (FBDD) programs for use in academia. J. Med. Chem. 2016 59 18 8189 8206 10.1021/acs.jmedchem.6b00197 27124799
    [Google Scholar]
  154. Verma S.K. Ratre P. Jain A.K. Liang C. Gupta G.D. Thareja S. De novo designing, assessment of target affinity and binding interactions against aromatase: Discovery of novel leads as anti-breast cancer agents. Struct. Chem. 2021 32 2 847 858 10.1007/s11224‑020‑01673‑y
    [Google Scholar]
  155. Basith S. Cui M. Macalino S.J.Y. Choi S. Expediting the design, discovery and development of anticancer drugs using computational approaches. Curr. Med. Chem. 2018 24 42 4753 4778 10.2174/0929867323666160902160535 27593958
    [Google Scholar]
  156. Xu X. Zhang Y. Corn cash price forecasting with neural networks. Comput. Electron. Agric. 2021 184 106120 10.1016/j.compag.2021.106120
    [Google Scholar]
  157. Alom M.Z. Taha T.M. Yakopcic C. Westberg S. Sidike P. Nasrin M.S. Hasan M. Van Essen B.C. Awwal A.A.S. Asari V.K. A state-of-the-art survey on deep learning theory and architectures. Electronics (Basel) 2019 8 3 292 10.3390/electronics8030292
    [Google Scholar]
  158. Rawat S. Subramaniam K. Subramanian S.K. Subbarayan S. Dhanabalan S. Chidambaram S.K.M. Stalin B. Roy A. Nagaprasad N. Aruna M. Tesfaye J.L. Badassa B. Krishnaraj R. Drug repositioning using computer-aided drug design (CADD). Curr. Pharm. Biotechnol. 2023 24 10.2174/1389201024666230821103601 37605405
    [Google Scholar]
  159. Priscila M. Frias C. Optimisation and use of cell-based assays and in vivo assay for screening drugs for alzheimer’s disease A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Science & Engineering. 2017
    [Google Scholar]
  160. Jin B. Xu X. Machine learning predictions of regional steel price indices for east China Ironmaking & Steelmaking 2024 10.1177/03019233241254891
    [Google Scholar]
  161. Drew D.S. Muhammed K. Baig F. Kelly M. Saleh Y. Sarangmat N. Okai D. Hu M. Manohar S. Husain M. Dopamine and reward hypersensitivity in parkinson’s disease with impulse control disorder. Brain 2020 143 8 2502 2518 10.1093/brain/awaa198 32761061
    [Google Scholar]
  162. Viana J.O. Félix M.B. Maia M.S. Serafim V.L. Scotti L. Scotti M.T. Drug discovery and computational strategies in the multitarget drugs era. Braz. J. Pharm. Sci. 2018 54 spe e01010 10.1590/s2175‑97902018000001010
    [Google Scholar]
  163. Ejiofor I.I. Ekeomodi C.C. CADD for cancer therapy: Current and future perspective. Targeted Cancer Therapy in Biomedical Engineering. Springer Singapore 2023 325 363 10.1007/978‑981‑19‑9786‑0_9
    [Google Scholar]
  164. Zhang J. Karanicolas J. Multi-task and multi-view learning for predicting adverse drug reactions 2012 Available from: https://www.semanticscholar.org/paper/Multi-task-and-Multi-view-Learning-for-Predicting-Zhang/2c5eb61b522a3965b01658c34400ac8c78a2124c
  165. Collier K.A. Valencia H. Newton H. Hade E.M. Sborov D.W. Cavaliere R. Poi M. Phelps M.A. Liva S.G. Coss C.C. Wang J. Khountham S. Monk P. Shapiro C.L. Piekarz R. Hofmeister C.C. Welling D.B. Mortazavi A. A phase 1 trial of the histone deacetylase inhibitor AR-42 in patients with neurofibromatosis type 2-associated tumors and advanced solid malignancies. Cancer Chemother. Pharmacol. 2021 87 5 599 611 10.1007/s00280‑020‑04229‑3 33492438
    [Google Scholar]
  166. Aarthy M. Panwar U. Singh S.K. Magnitude and advancements of cadd in identifying therapeutic intervention against flaviviruses. Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design Springer Singapore 2021 179 203
    [Google Scholar]
  167. Kulkov I. The role of artificial intelligence in business transformation: A case of pharmaceutical companies. Technol. Soc. 2021 66 101629 10.1016/j.techsoc.2021.101629
    [Google Scholar]
  168. Nishant R. Kennedy M. Corbett J. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. Int. J. Inf. Manage. 2020 53 102104 10.1016/j.ijinfomgt.2020.102104
    [Google Scholar]
  169. Santorsola M. Lescai F. The promise of explainable deep learning for omics data analysis: Adding new discovery tools to AI. N. Biotechnol. 2023 77 1 11 10.1016/j.nbt.2023.06.002 37329982
    [Google Scholar]
  170. Gogleva A. Polychronopoulos D. Pfeifer M. Poroshin V. Ughetto M. Martin M.J. Thorpe H. Bornot A. Smith P.D. Sidders B. Dry J.R. Ahdesmäki M. McDermott U. Papa E. Bulusu K.C. Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer. Nat. Commun. 2022 13 1 1667 10.1038/s41467‑022‑29292‑7 35351890
    [Google Scholar]
  171. Vatansever S. Schlessinger A. Wacker D. Kaniskan H.Ü. Jin J. Zhou M.M. Zhang B. Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions. Med. Res. Rev. 2021 41 3 1427 1473 10.1002/med.21764 33295676
    [Google Scholar]
  172. Safari-Alighiarloo N. Taghizadeh M. Rezaei-Tavirani M. Goliaei B. Peyvandi A.A. Protein-protein interaction networks (PPI) and complex diseases. Gastroenterol. Hepatol. Bed Bench 2014 7 1 17 31 25436094
    [Google Scholar]
  173. Gautam V. Gaurav A. Masand N. Lee V.S. Patil V.M. Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol. Divers. 2023 27 2 959 985 10.1007/s11030‑022‑10489‑3 35819579
    [Google Scholar]
  174. Ginghina O. Hudita A. Zamfir M. Spanu A. Mardare M. Bondoc I. Buburuzan L. Georgescu S.E. Costache M. Negrei C. Nitipir C. Galateanu B. Liquid biopsy and artificial intelligence as tools to detect signatures of colorectal malignancies: A modern approach in patient’s stratification. Front. Oncol. 2022 12 856575 10.3389/fonc.2022.856575 35356214
    [Google Scholar]
  175. Ferry B. Gervasoni D. Vogt C. Regulatory and Ethical Considerations. Stereotaxic Neurosurgery in Laboratory Rodent. Springer Paris 2014 1 18 10.1007/978‑2‑8178‑0472‑9_1
    [Google Scholar]
  176. Björklund P. The curious case of artificial intelligence : An analysis of the relationship between the EU medical device regulations and algorithmic decision systems used within the medical domain 2021 Available from: https://www.diva-portal.org/smash/get/diva2:1553551/FULLTEXT01.pdf
  177. Belenguer L. AI bias: Exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical industry. AI Ethics. 2022 2 4 771 787 10.1007/s43681‑022‑00138‑8
    [Google Scholar]
  178. Purisima E.O. Corbeil C.R. Gaudreault F. Wei W. Deprez C. Sulea T. Solvated interaction energy: From small-molecule to antibody drug design. Front. Mol. Biosci. 2023 10 1210576 10.3389/fmolb.2023.1210576 37351549
    [Google Scholar]
  179. Tsopra R. Fernandez X. Luchinat C. Alberghina L. Lehrach H. Vanoni M. Dreher F. Sezerman O.U. Cuggia M. de Tayrac M. Miklasevics E. Itu L.M. Geanta M. Ogilvie L. Godey F. Boldisor C.N. Campillo-Gimenez B. Cioroboiu C. Ciusdel C.F. Coman S. Hijano Cubelos O. Itu A. Lange B. Le Gallo M. Lespagnol A. Mauri G. Soykam H.O. Rance B. Turano P. Tenori L. Vignoli A. Wierling C. Benhabiles N. Burgun A. A framework for validating AI in precision medicine: Considerations from the European ITFoC consortium. BMC Med. Inform. Decis. Mak. 2021 21 1 274 10.1186/s12911‑021‑01634‑3 34600518
    [Google Scholar]
  180. Dwivedi Y.K. Hughes L. Ismagilova E. Aarts G. Coombs C. Crick T. Duan Y. Dwivedi R. Edwards J. Eirug A. Galanos V. Ilavarasan P.V. Janssen M. Jones P. Kar A.K. Kizgin H. Kronemann B. Lal B. Lucini B. Medaglia R. Le Meunier-FitzHugh K. Le Meunier-FitzHugh L.C. Misra S. Mogaji E. Sharma S.K. Singh J.B. Raghavan V. Raman R. Rana N.P. Samothrakis S. Spencer J. Tamilmani K. Tubadji A. Walton P. Williams M.D. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manage. 2021 57 101994 10.1016/j.ijinfomgt.2019.08.002
    [Google Scholar]
  181. Gerke S. Minssen T. Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare Artificial Intelligence in Healthcare Academic Press 2020 295 336 10.1016/B978‑0‑12‑818438‑7.00012‑5
    [Google Scholar]
  182. Shah A. Jain M. Limitations and future challenges of computer-aided drug design methods, Comput. Aided Drug Des. From Ligand-Based Methods to Struct. Approaches 2022 283 297 10.1016/B978‑0‑323‑90608‑1.00006‑X
    [Google Scholar]
  183. Swanson J.L. Chin P.S. Romero J.M. Srivastava S. Ortiz-Guzman J. Hunt P.J. Arenkiel B.R. Advancements in the quest to map, monitor, and manipulate neural circuitry. Front. Neural Circuits 2022 16 886302 10.3389/fncir.2022.886302 35719420
    [Google Scholar]
  184. Shehab M. Abualigah L. Shambour Q. Abu-Hashem M.A. Shambour M.K.Y. Alsalibi A.I. Gandomi A.H. Machine learning in medical applications: A review of state-of-the-art methods. Comput. Biol. Med. 2022 145 105458 10.1016/j.compbiomed.2022.105458 35364311
    [Google Scholar]
  185. Ramnath S. Haghighi P. Venkiteswaran A. Shah J.J. Interoperability of CAD geometry and product manufacturing information for computer integrated manufacturing. Int. J. Comput. Integrated Manuf. 2020 33 2 116 132 10.1080/0951192X.2020.1718760
    [Google Scholar]
  186. Schuler J. Falls Z. Mangione W. Hudson M.L. Bruggemann L. Samudrala R. Evaluating the performance of drug-repurposing technologies. Drug Discov. Today 2022 27 1 49 64 10.1016/j.drudis.2021.08.002 34400352
    [Google Scholar]
  187. Ten C.A.D. Challenges (Basel) 2005
    [Google Scholar]
  188. Howell C.J. Fisher T. Muniz C.N. Maimon D. Rotzinger Y. A depiction and classification of the stolen data market ecosystem and comprising darknet markets: A multidisciplinary approach J Contemp Crim Justice. 2023 39 2 298 317 10.1177/10439862231158005
    [Google Scholar]
  189. 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 10.3389/fchem.2020.00726 33062633
    [Google Scholar]
  190. Poluri K.M. Gulati K. Tripathi D.K. Nagar N. Drug design methods to regulate protein–protein interactions. Protein-Protein Interact Springer Singapore 2023 265 341 10.1007/978‑981‑99‑2423‑3_6
    [Google Scholar]
  191. Charitopoulos A. Rangoussi M. Koulouriotis D. On the use of soft computing methods in educational data mining and learning analytics research: a review of years 2010–2018. Int. J. Artif. Intell. Educ. 2020 30 3 371 430 10.1007/s40593‑020‑00200‑8
    [Google Scholar]
  192. Bagert J.D. Muir T.W. Molecular epigenetics: Chemical biology tools come of age. Annu Rev Biochem. 2021 90 287 320 10.1146/annurev‑biochem‑080120‑021109
    [Google Scholar]
  193. Benabdallah G. Peek N. Bourgault S. Ja-cobs J. Remote learners, home makers: How digital fabricationwas taught online during a pandemic. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems New York, NY, USA ,07 May 2021, P.1 - 14 10.1145/3411764.3445450
    [Google Scholar]
  194. Bassani D. Moro S. Past, present, and future perspectives on computer-aided drug design methodologies. Molecules 2023 28 9 3906 10.3390/molecules28093906 37175316
    [Google Scholar]
  195. Dara S. Dhamercherla S. Jadav S.S. Babu C.H.M. Ahsan M.J. Machine learning in drug discovery: A review. Artif. Intell. Rev. 2022 55 3 1947 1999 10.1007/s10462‑021‑10058‑4 34393317
    [Google Scholar]
  196. Nayarisseri A. Khandelwal R. Tanwar P. Madhavi M. Sharma D. Thakur G. Speck-Planche A. Singh S.K. Artificial intelligence, big data and machine learning approaches in precision medicine & drug discovery. Curr. Drug Targets 2021 22 6 631 655 10.2174/18735592MTEzsMDMnz 33397265
    [Google Scholar]
/content/journals/cchts/10.2174/0113862073334062241015043343
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Artificial Intelligence in Computer-Aided Drug Design (CADD) Tools for the Finding of Potent Biologically Active Small Molecules: Traditional to Modern Approach
Bentham Science Publishers ; https://doi.org/10.2174/0113862073334062241015043343
/content/journals/cchts/10.2174/0113862073334062241015043343
/content/journals/cchts/10.2174/0113862073334062241015043343
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  • Article Type:     Review Article
Keywords: artificial intelligence ; Drug ; deep learning ; machine learning ; computer-aided drug design ; anticancer ; small molecules

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