The world needs pharmacological medication therapy for the diagnosis and treatment of numerous ailments. Time constraints make it necessary to find novel chemical compounds for the same, nevertheless, current methods of screening medicinal molecules and their target proteins cannot be completed as quickly as with AI. Assigning the right target during drug molecule development is crucial for effective treatment. A disease involves several proteins. Designing any medication molecule for its specific target over disease is considerably aided by predicting the structure or makeup of the targeted protein. AI can help in the creation of structure-based drugs by foreseeing the effect of a molecule on the target as well as safety concerns by anticipating a target protein's 3D structure in line with its chemical environment. Using the AI tool Alpha-Fold, which is based on DNNs, the 3D protein structure was predicted by examining the distances between neighbouring amino acids and the corresponding angles of the peptide bonds. In a study, RNN was used to predict the protein structure. A recurrent geometric network (RGN) is said to be composed of three stages: computation, geometry, and assessment. Here, the torsional angles for a particular residue and a partially formed backbone obtained from the geometric unit upstream of this served as the input and output for encoding the fundamental protein sequence. The final unit produced the 3D structure. As a result, it is cutting edge to use AI approaches to screen medications based on the study of target proteins.


Artificial Intelligence


  1. Ramesh A. Artificial intelligence in medicine. Ann. R. Coll. Surg. Engl. 2004, 86:334–338.
  2. Miles J., Walker A. The potential application of artificial intelligence in transport. IEE Proc.-Intell. Transport Syst. 2006, 153:183–198. 
  3. Yang Y., Siau K. MWAIS, 2018. A Qualitative Research on Marketing and Sales in the Artificial Intelligence Age. 
  4. Wirtz B.W. Artificial intelligence and the public sector—applications and challenges. Int. J. Public Adm. 2019, 42:596–615. 
  5. Smith R.G., Farquhar A. The road ahead for knowledge management: an AI perspective. AI Mag. 2000, 21:17–17. 
  6. Lamberti M.J. A study on the application and use of artificial intelligence to support drug development. Clin. Ther. 2019, 41:1414–1426. 
  7. Beneke F., Mackenrodt M.-O. Artificial intelligence and collusion. IIC Int. Rev. Intellectual Property Competition Law. 2019, 50:109–134. 
  8. Steels L., Brooks R. Routledge, 2018. The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents. 
  9. Bielecki A., Bielecki A. Foundations of artificial neural networks. In: Kacprzyk Janusz., editor. Models of Neurons and Perceptrons: Selected Problems and Challenges. Springer International Publishing, 2019. 15–28. Polish academy of sciences, Warsaw, Poland.
  10. Kalyane D. Artificial intelligence in the pharmaceutical sector: current scene and future prospect. In: Tekade Rakesh K., editor. The Future of Pharmaceutical Product Development and Research. Elsevier, 2020. 73–107.
  11. Vyas M. Artificial intelligence: the beginning of a new era in pharmacy profession. Asian J. Pharm. 2018, 12:72–76.
  12. Mak K.-K., Pichika M.R. Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today. 2019, 24:773–780. 
  13. Sellwood M.A. Artificial intelligence in drug discovery. Fut. Sci. 2018, 10:2025–2028.
  14. Chan H.S. Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci. 2019, 40(8):592–604.
  15. Álvarez-Machancoses Ó, Fernández-Martínez J.L. Using artificial intelligence methods to speed up drug discovery. Expert Opin. Drug Discovery. 2019, 14:769–777. 
  16. Fleming N. How artificial intelligence is changing drug discovery. Nature. 2018, 557:S55–S55.
  17. Dana D. Deep learning in drug discovery and medicine, scratching the surface. Molecules. 2018, 23:2384.
  18. Zang Q. In silico prediction of physicochemical properties of environmental chemicals using molecular fingerprints and machine learning. J. Chem. Inf. Model. 2017, 57:36–49.
  19. Yang X. Concepts of artificial intelligence for computer-assisted drug discovery. Chem. Rev. 2019, 119:10520–10594. 
  20. Hessler G., Baringhaus K.-H. Artificial intelligence in drug design. Molecules. 2018, 23:2520. 
  21. Lusci A. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J. Chem. Inf. Model. 2013, 53:1563–1575.
  22. Kumar R. Prediction of human intestinal absorption of compounds using artificial intelligence techniques. Curr. Drug Discovery Technol. 2017, 14:244–254.
  23. Rupp M. Estimation of acid dissociation constants using graph kernels. Mol. Inf. 2010, 29:731–740.
  24. Chai S. A grand product design model for crystallization solvent design. Comput. Chem. Eng. 2020, 135:106764. 
  25. Thafar M. Comparison study of computational prediction tools for drug–target binding affinities. Frontiers Chem. 2019, 7:1–19. 
  26. Öztürk H. DeepDTA: deep drug–target binding affinity prediction. Bioinformatics. 2018, 34:i821–i829.
  27. Lounkine E. Large-scale prediction and testing of drug activity on side-effect targets. Nature. 2012, 486:361–367. 
  28. Mahmud S.H. iDTi-CSsmoteB: identification of drug–target interaction based on drug chemical structure and protein sequence using XGBoost with over-sampling technique SMOTE. IEEE Access. 2019, 7:48699–48714. 
  29. Gao K.Y. Interpretable drug target prediction using deep neural representation. In: Lang Jérôme., editor. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI, 2018. pp. 3371–3377. 
  30. Feng Q. Padme: a deep learning-based framework for drug–target interaction prediction. arXiv. 2018 arXiv: 1807.09741. 
  31. Karimi M. DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks. Bioinformatics. 2019, 35(18):3329–3338.
  32. Pu L. eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol. Toxicol. 2019, 20:2.
  33. Basile A.O., Yahi A., Tatonetti N.P. Artificial intelligence for drug toxicity and safety. Trends Pharmacol. Sci. 2019,40(September (9)):624–635. doi: 10.1016/ Epub 2019 Aug 2. PMID: 31383376, PMCID: PMC6710127.
  34. Lysenko A. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci. Alliance. 2018, 1:YYY–ZZZ. 
  35. Gayvert K.M. A data-driven approach to predicting successes and failures of clinical trials. Cell Chem. Biolo. 2016, 23:1294–1301. 
  36. Jimenez-Carretero D. Tox_(R) CNN: deep learning-based nuclei profiling tool for drug toxicity screening. PLoS Comput. Biol. 2018, 14.
  37. Wan F., Zeng J. Deep learning with feature embedding for compound–protein interaction prediction. bioRxiv. 2016, 2016.
  38. AlQuraishi M. End-to-end differentiable learning of protein structure. Cell Syst. 2019, 8:292–301. 
  39. Hutson M. AI protein-folding algorithms solve structures faster than ever. Nature. 2019, XX:YYY–ZZZ. 
  40. Avdagic Z. Artificial intelligence in prediction of secondary protein structure using CB513 database. Summit Transl. Bioinf. 2009, 2009:1. 
  41. Tian K. Boosting compound-protein interaction prediction by deep learning. Methods. 2016, 110:64–72. 
  42. Wang F. Computational screening for active compounds targeting protein sequences: methodology and experimental validation. J. Chem. Inf. Model. 2011, 51:2821–2828. 
  43. Xiao X. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. J. Biomol. Struct. Dyn. 2015, 33:2221–2233.
  44. Park K. A review of computational drug repurposing. Transl. Clin. Pharmacol. 2019, 27:59–63. 
  45. Zeng X. Target identification among known drugs by deep learning from heterogeneous networks. Chem. Sci. 2020, 11:1775–1797. 
  46. Achenbach J. Computational tools for polypharmacology and repurposing. Fut. Med. Chem. 2011, 3:961–968.
  47. Li X. Prediction of synergistic anticancer drug combinations based on drug target network and drug induced gene expression profiles. Artif. Intell. Med. 2017, 83:35–43.
  48. Reddy A.S., Zhang S. Polypharmacology: drug discovery for the future. Expert Rev. Clin. Pharmacol. 2013, 6:41–47.
  49. Li Z. KinomeX: a web application for predicting kinome-wide polypharmacology effect of small molecules. Bioinformatics. 2019, 35:5354–5356. 
  50. Cyclica . Cyclica, 2017. Cyclica Launches Ligand Express™, a Disruptive Cloud–Based Platform to Revolutionize Drug Discovery.
  51. Corey E., Wipke W.T. Computer-assisted design of complex organic syntheses. Science. 1969, 166:178–192. 
  52. Grzybowski B.A. Chematica: a story of computer code that started to think like a chemist. Chem. 2018, 4:390–398. 
  53. Klucznik T. Efficient syntheses of diverse, medicinally relevant targets planned by computer and executed in the laboratory. Chem. 2018, 4:522–532. 
  54. Segler M.H. Planning chemical syntheses with deep neural networks and symbolic AI. Nature. 2018, 555:604–610.

Ishu Garg
Corresponding author

ITS college of Pharmacy, Muradnagar, Ghaziabad-201206, Uttar Pradesh, India


ITS college of Pharmacy, Muradnagar, Ghaziabad-201206, Uttar Pradesh, India

Mohit Kumar

ITS college of Pharmacy, Muradnagar, Ghaziabad-201206, Uttar Pradesh, India

Vishvanshi Tyagi

ITS college of Pharmacy, Muradnagar, Ghaziabad-201206, Uttar Pradesh, India

Ishu Garg*, Harish, Mohit Kumar, Vishvanshi Tyagi, Nishant, Artificial Intelligence in Predicting Drug Target Proteins- A Review, Int. J. in Pharm. Sci., 2023, Vol 1, Issue 8, 72-78.

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