Revolutionizing drug discovery: The impact of artificial intelligence on advancements in pharmacology and the pharmaceutical industry

Seema Yadav, Abhishek Singh, Rishika Singhal, Jagat Pal Yadav

PDF(2412 KB)
PDF(2412 KB)
Intelligent Pharmacy ›› 2024, Vol. 2 ›› Issue (3) : 367-380. DOI: 10.1016/j.ipha.2024.02.009
Review article

Revolutionizing drug discovery: The impact of artificial intelligence on advancements in pharmacology and the pharmaceutical industry

Author information +
History +

Abstract

To create novel treatments and treat complex diseases, the pharmaceutical sector is essential. Drug discovery, however, is a time-consuming, pricey, and dangerous endeavor. Artificial intelligence (AI) has become a potent instrument that has transformed several industries, including healthcare, in recent years. This summary gives a general overview of how AI is expediting the creation of novel medicines, revolutionizing the pharmaceutical sector, and enabling drug discovery. The pharmaceutical sector is experiencing a drug discovery revolution because of AI. The drug discovery process is changing at different phases because of AI approaches like machine learning and deep learning. This abstract demonstrates how AI facilitates drug development through target identification, lead compound optimization, drug design, drug repurposing, and clinical trial enhancement. AI integration has the potential to hasten the creation of novel treatments, save costs, and improve patient outcomes. To fully realize the potential of AI in pharmaceutical research and development, issues relating to data accessibility, algorithm interpretability, and laws must be resolved.

Keywords

Artificial intelligence / AI pharmacology / AI in drug discovery / Medical diagnosis / Clinical trials

Cite this article

Download citation ▾
Seema Yadav, Abhishek Singh, Rishika Singhal, Jagat Pal Yadav. Revolutionizing drug discovery: The impact of artificial intelligence on advancements in pharmacology and the pharmaceutical industry. Intelligent Pharmacy, 2024, 2(3): 367‒380 https://doi.org/10.1016/j.ipha.2024.02.009

References

[1]
Chen W, Liu X, Zhang S, Chen S. Artificial intelligence for drug discovery: resources, methods, and applications. Mol Ther Nucleic Acids. 2023;31(March):691–702.
CrossRef Google scholar
[2]
Xu Y, Liu X, Cao X, et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation. 2021;2(4).
CrossRef Google scholar
[3]
Aldoseri A, Al-Khalifa KN, Hamouda AM. Re-thinking data strategy and integration for artificial intelligence: concepts, opportunities, and challenges. Appl Sci. 2023;13(12).
CrossRef Google scholar
[4]
Taye MM. Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers. 2023;12(5).
CrossRef Google scholar
[5]
Tarle S, Kakad A, Shaikh MRN. Review article overview : embracing tools of artificial intelligence in. Pharmaceuticals. 2023;4(6):5749–5755.
[6]
Krenn M, Pollice R, Guo SY, et al. On scientific understanding with artificial intelligence. Nat Rev Phys. 2022;4(12):761–769.
CrossRef Google scholar
[7]
Denecke K, Baudoin CR. A review of artificial intelligence and robotics in transformed health ecosystems. Front Med. 2022;9(July):1–13.
CrossRef Google scholar
[8]
Deiana AMC, Tran N, Agar J, et al. Applications and techniques for fast machine learning in science. Front Big Data. 2022;5(April):1–56. https://doi.org/10.3389/fdata.2022.787421.
[9]
Sarkar C, Das B, Rawat VS, et al. Artificial intelligence and machine learning technology driven modern drug discovery and development. Int J Mol Sci. 2023;24(3).
CrossRef Google scholar
[10]
Fei N, Lu Z, Gao Y, et al. Towards artificial general intelligence via a multimodal foundation model. Nat Commun. 2022;13(1):1–13. https://doi.org/10.1038/S41467-022-30761-2.
[11]
Reiners D, Davahli MR, Karwowski W, Cruz-Neira C. The combination of artificial intelligence and extended reality: a systematic review. Front Virtual Real. 2021;2(September):1–13.
CrossRef Google scholar
[12]
Bhattamisra SK, Banerjee P, Gupta P, Mayuren J, Patra S, Candasamy M. Artificial intelligence in pharmaceutical and healthcare research. Big Data Cogn Comput. 2023;7(1):10.
CrossRef Google scholar
[13]
Fleming N. AI in drug discovery. Nature. 2018;(March):5–7.
[14]
Patil P, Nrip N, Hajare A, et al. Artificial intelligence and tools in pharmaceuticals: an overview. 2023;16:2075–2082.
CrossRef Google scholar
[15]
Segler M, Preuss M, Waller MP. Towards “Alphachem”: Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies. 5th Int Conf Learn Represent ICLR 2017 -Work Track Proc;2017:1–4, 2014.
[16]
Gaidhani KA, Harwalkar M, Nirgude PS. World journal of pharmaceutical ReseaRch SEED EXTRACTS. World J Pharmaceut Res. 2014;3(3):5041–5048. https://doi.org/10.20959/wjpr201911-15964.
[17]
Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol Pharm. 2016;13(7):2524–2530.
CrossRef Google scholar
[18]
Wu J, Kong L, Yi M, et al. Prediction and screening model for products based on fusion regression and XGBoost classification. Comput Intell Neurosci. 2022:2022.
CrossRef Google scholar
[19]
Dara S, Dhamercherla S, Jadav SS, Babu CM, Ahsan MJ. Machine Learning in Drug Discovery: A Review. vol. 55. Springer Netherlands;2022. https://doi.org/10.1007/S10462-021-10058-4.
[20]
Oyelade J, Isewon I, Oladipupo F, et al. Clustering algorithms: their application to gene expression data. Bioinf Biol Insights. 2016;10:237–253.
CrossRef Google scholar
[21]
Borisa Pooja, Debjani Singh KSR. Impact of artificial intelligence on pharma industry. Manipal J Pharm Sci |. 2020;6(1):54–59.
[22]
Ahmed SF, Alam MS Bin, Hassan M, et al. Deep Learning Modelling Techniques: Current Progress, Applications, Advantages, and Challenges. vol. 56. Springer Netherlands;2023.
CrossRef Google scholar
[23]
Cè M, Irmici G, Foschini C, et al. Artificial intelligence in brain tumor imaging: a step toward personalized medicine. Curr Oncol. 2023;30(3):2673–2701.
CrossRef Google scholar
[24]
Sheikh H, Prins C, Schrijvers E. Mission AI: The New System Technology. 2023.
[25]
Patel J, Patel D, Meshram D. Artificial intelligence in pharma industry-A rising concept. J Adv Pharmacogn. 2021;1(2):54–64.
[26]
Bohr A, Memarzadeh K. The Rise of Artificial Intelligence in Healthcare Applications.2020.
CrossRef Google scholar
[27]
Johnson KB, Wei WQ, Weeraratne D, et al. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci. 2021;14(1):86–93.
CrossRef Google scholar
[28]
Al-Antari MA. Artificial intelligence for medical diagnostics—existing and future AI technology. Diagnostics. 2023;13(4):1–3.
CrossRef Google scholar
[29]
Çelik IN, Arslan FK, Tun R, Yildiz I. Artificial intelligence on drug discovery and development. Ank Univ Eczacilik Fak Derg. 2022;46(2):400–427. https://doi.org/10.33483/jfpau.878041q.
[30]
Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23(1):1–15.
CrossRef Google scholar
[31]
Al Kuwaiti A, Nazer K, Al-Reedy A, et al. A review of the role of artificial intelligence in healthcare. J Personalized Med. 2023;13(6):1–22.
CrossRef Google scholar
[32]
Maia E, Vieira P, Praça I. Empowering preventive care with GECA chatbot. Health. 2023;11(18).
CrossRef Google scholar
[33]
Duijnhoven RG, Straus SMJM, Raine JM, de Boer A, Hoes AW, de Bruin ML. Number of patients studied prior to approval of new medicines: a database analysis. PLoS Med. 2013;10(3):1–8.
CrossRef Google scholar
[34]
Fogel DB. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemp Clin Trials Commun. 2018;11(July):156–164.
CrossRef Google scholar
[35]
Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ. 2019;2019(10).
CrossRef Google scholar
[36]
Verma H, Mlynar J, Schaer R, et al. Re-Thinking the role of AI with physicians in oncology: revealing perspec-tives from clinical and research workflows. 2023;19.
CrossRef Google scholar
[37]
Cheng F, Liu C, Jiang J, et al. Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol. 2012;8(5).
CrossRef Google scholar
[38]
Wang W, Yang S, Zhang X, Li J. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics. 2014;30(20):2923–2930.
CrossRef Google scholar
[39]
Jarada TN, Rokne JG, Alhajj R. A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Cheminf. 2020;12(1):1–23.
CrossRef Google scholar
[40]
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127–157.
CrossRef Google scholar
[41]
Prasad K, Kumar V. Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2. Curr Res Pharmacol Drug Discov. 2021;2(May):100042.
CrossRef Google scholar
[42]
Olayan RS, Ashoor H, Bajic VBDDR. Efficient computational method to predict drug-Target interactions using graph mining and machine learning approaches. Bioinformatics. 2018;34(7):1164–1173.
CrossRef Google scholar
[43]
Wang X, Li Q, Liu Y, Du Z, Jin R. Drug repositioning of COVID-19 based on mixed graph network and ion channel. Math Biosci Eng. 2022;19(4):3269–3284.
CrossRef Google scholar
[44]
Meng Y, Wang Y, Xu J, et al. Drug repositioning based on weighted local information augmented graph neural network. Briefings Bioinf. 2024;25(1):1–12.
CrossRef Google scholar
[45]
Xuan P, Song Y, Zhang T, Jia L. Prediction of potential drug-disease associations through deep integration of diversity and projections of various drug features. Int J Mol Sci. 2019;20(17):1–17.
CrossRef Google scholar
[46]
Delcher C, Moga D, Li Y, Muñoz M, Sohn M, Bae J. Pharmacoepidemiology and pharmacovigilance. Remingt Sci Pract Pharm. 2020:899–913. Published online.
CrossRef Google scholar
[47]
Gelfand JM, Noe MH. Pharmacovigilance. 4th ed. Elsevier;2020. https://doi.org/10.1016/B978-0-323-61211-1.00007-3.
[48]
Hamid AAA, Rahim R, Teo SP. Pharmacovigilance and its importance for primary health care professionals. Korean J Fam Med. 2022;43(5):290–295.
CrossRef Google scholar
[49]
Jeetu G, Anusha G. Pharmacovigilance: a worldwide master key for drug safety monitoring. J Young Pharm. 2010;2(3):315–320.
CrossRef Google scholar
[50]
Harrison R, Walton M, Manias E, et al. The missing evidence: a systematic review of patients’ experiences of adverse events in health care. Int J Qual Health Care. 2015;27(6):424–442.
CrossRef Google scholar
[51]
Ouoba K, Lehmann H, Zongo A, Pabst JY, Semdé R. Current status and challenges of pharmacovigilance of traditional medicines in French-speaking West African (UEMOA) countries. Pharmaceut Med. 2023;37(4):305–318.
CrossRef Google scholar
[52]
Li C, Chen Y, Shang Y. A review of industrial big data for decision making in intelligent manufacturing. Eng Sci Technol an Int J. 2022;29:101021.
CrossRef Google scholar
[53]
Kumar Sethi MPU. Pharmacovigilance: challenges in India. J Pharmacovigil. 2016;4(1):1000194.
CrossRef Google scholar
[54]
Cowie MR, Blomster JI, Curtis LH, et al. Electronic health records to facilitate clinical research. Clin Res Cardiol. 2017;106(1):1–9.
CrossRef Google scholar
[55]
Trifirò G, Crisafulli S. A new era of pharmacovigilance: future challenges and opportunities. Front Drug Saf Regul. 2022;2(February):2020–2023.
CrossRef Google scholar
[56]
Sloane R, Osanlou O, Lewis D, Bollegala D, Maskell S, Pirmohamed M. Social media and pharmacovigilance: a review of the opportunities and challenges. Br J Clin Pharmacol. 2015;80(4):910–920.
CrossRef Google scholar
[57]
Ross MK, Wei W, Ohno-Machado L. “Big data” and the electronic health record. Yearb Med Inform. 2014;9:97–104.
CrossRef Google scholar
[58]
Ball R, Dal Pan G. “Artificial intelligence” for pharmacovigilance: ready for prime time? Drug Saf. 2022;45(5):429–438.
CrossRef Google scholar
[59]
Mohammed KI, Zaidan AA, Zaidan BB, et al. Real-time remote-health monitoring systems: a review on patients prioritisation for multiple-chronic diseases, taxonomy analysis, concerns and solution procedure. J Med Syst. 2019;43(7).
CrossRef Google scholar
[60]
Wani P, Shelke A, Marwadi M, Somase V, Borade P, Pansare K. Role of artificial intelligence in pharmacovigilance: a concise review. 2022;13(7):6149–6156. https://doi.org/10.47750/pnr.2022.13.S07.747.
[61]
Khalid S, Khan MA, Mazliham MS, et al. Predicting risk through artificial intelligence based on machine learning algorithms: a case of Pakistani nonfinancial firms. Complexity. 2022:2022. November 2019.
CrossRef Google scholar
[62]
Gonzalez-Hernandez G, Krallinger M, Muñoz M, Rodriguez-Esteban R, Uzuner Ö, Hirschman L. Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers. Database. 2022;2022(00):1–10.
CrossRef Google scholar
[63]
Bate A, Stegmann JU. Artificial intelligence and pharmacovigilance: what is happening, what could happen and what should happen? Heal Policy Technol. 2023;12(2):100743.
CrossRef Google scholar
[64]
Belhekar MN, Taur SR, Munshi RP. A study of agreement between the Naranjo algorithm and WHO-UMC criteria for causality assessment of adverse drug reactions. Indian J Pharmacol. 2014;46(1):117–120.
CrossRef Google scholar
[65]
Bouaziz M. The Future of Pharmacovigilance with the Use of Artificial Intelligence Sounds Good..
[66]
Sahu A, Mishra J, Kushwaha N. Artificial intelligence (AI) in drugs and pharmaceuticals. Comb Chem High Throughput Screen. 2022;25(11):1818–1837.
CrossRef Google scholar
[67]
Ece A. Computer-aided drug design. BMC Chem. 2023;17(1):1–3.
CrossRef Google scholar
[68]
Kim HY, Cho GJ, Kwon HS. Applications of artificial intelligence in obstetrics. Ultrasonography. 2023;42(1):2–9.
CrossRef Google scholar
[69]
Sarkis M, Bernardi A, Shah N, Papathanasiou MM. Emerging challenges and opportunities in pharmaceutical manufacturing and distribution. Processes. 2021;9(3):1–16.
CrossRef Google scholar
[70]
Khalid GM, Usman AG. Application of data-intelligence algorithms for modeling the compaction performance of new pharmaceutical excipients. Futur J Pharm Sci. 2021;7(1).
CrossRef Google scholar
[71]
Lohit N, Singh AK, Kumar A, et al. Description and in silico ADME studies of USFDA approved drugs or drugs under clinical trial which violate the Lipinski’s rule of 5. Lett Drug Des Discov. 2023;20:3–5.
CrossRef Google scholar
[72]
Minbaleev AV. The concept of “artificial intelligence” in law. Bull Udmurt Univ Ser Econ Law. 2022;32(6):1094–1099.
CrossRef Google scholar
[73]
Rantanen J, Khinast J. The future of pharmaceutical manufacturing sciences. J Pharmaceut Sci. 2015;104(11):3612–3638.
CrossRef Google scholar
[74]
Overgaard SM, Graham MG, Brereton T, et al. Implementing quality management systems to close the AI translation gap and facilitate safe, ethical, and effective health AI solutions. npj Digit Med. 2023;6(1).
CrossRef Google scholar
[75]
Hole G, Hole AS, McFalone-Shaw I. Digitalization in pharmaceutical industry: what to focus on under the digital implementation process? Int J Pharm X. 2021;3:100095.
CrossRef Google scholar
[76]
Tirkolaee EB, Sadeghi S, Mooseloo FM, Vandchali HR, Aeini S. Application of machine learning in supply chain management: a comprehensive overview of the main areas. Math Probl Eng. 2021;2021(Ml).
CrossRef Google scholar
[77]
Parvathaneni M, Awol AK, Kumari M, Lan K, Lingam M. Application of artificial intelligence and machine learning in drug discovery and development. J Drug Deliv Therapeut. 2023;13(1):151–158.
CrossRef Google scholar
[78]
Iqbal S, Qureshi AN, Li J, Mahmood T. On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks. vol. 30. Springer Netherlands;2023.
CrossRef Google scholar
[79]
Tang X. The role of artificial intelligence in medical imaging research. BJR|Open. 2020;2(1):20190031.
CrossRef Google scholar
[80]
Fogarasi M, Coburn JC, Ripley B. Algorithms used in medical image segmentation for 3D printing and how to understand and quantify their performance. 3D Print Med. 2022:1–15. Published online.
CrossRef Google scholar
[81]
Karalis VD. The integration of artificial intelligence into clinical practice. Published online. 2024:14–44.
CrossRef Google scholar
[82]
Gomes RFT, Schmith J, Figueiredo RM de, et al. Use of artificial intelligence in the classification of elementary oral lesions from clinical images. Int J Environ Res Publ Health. 2023;20(5).
CrossRef Google scholar
[83]
Puttagunta M, Ravi S. Medical image analysis based on deep learning approach. Multimed Tool Appl. 2021;80(16):24365–24398. https://doi.org/10.1007/S11042-021-10707-4.
[84]
Ghaffar N, Erkan N, Ahad K. Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discov Artif Intell.2023. https://doi.org/10.1007/S44163-023-00049-5. Published online.
[85]
Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Hum Comput. 2023;14(7):8459–8486.
CrossRef Google scholar
[86]
Pierre K, Haneberg AG, Kwak S, et al. Applications of artificial intelligence in the radiology roundtrip: process streamlining, workflow optimization, and beyond. Semin Roentgenol. 2023;58(2):158–169.
CrossRef Google scholar
[87]
Jellinger KA. The neuropathological diagnosis of Alzheimer disease. J Neural Transm Suppl. 1998;5(53):97–118.
CrossRef Google scholar
[88]
Antwi WK, Akudjedu TN, Botwe BO. Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers’ perspectives. Insights Imaging. 2021;12(1).
CrossRef Google scholar
[89]
Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73–81.
CrossRef Google scholar
[90]
Yuan C, Ryan PB, Ta C, et al. Criteria2Query: a natural language interface to clinical databases for cohort definition. J Am Med Inf Assoc. 2019;26(4):294–305.
CrossRef Google scholar
[91]
Wong A, Plasek JM, Montecalvo SP, Zhou L. Natural language processing and its implications for the future of medication safety: a narrative review of recent advances and challenges. Pharmacother J Hum Pharmacol Drug Ther. 2018;38(8):822–841.
CrossRef Google scholar
[92]
Del Rio-Bermudez C, Medrano IH, Yebes L, Poveda JL. Towards a symbiotic relationship between big data, artificial intelligence, and hospital pharmacy. J Pharm Policy Pract. 2020;13(1):4–9.
CrossRef Google scholar
[93]
Huang WT, Hung HH, Kao YW, et al. Application of neural network and cluster analyses to differentiate TCM patterns in patients with breast cancer. Front Pharmacol. 2020;11:670.
CrossRef Google scholar
[94]
Kumar M, Nguyen TPN, Kaur J, et al. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep. 2023;75(1):3–18.
CrossRef Google scholar
[95]
Javaid M, Haleem A, Singh RP, Suman R. Towards insighting cybersecurity for healthcare domains: a comprehensive review of recent practices and trends. Cyber Secur Appl. 2023;1(December).
CrossRef Google scholar
[96]
Yeboah-Ofori A, Islam S. Cyber security threat modeling for supply chain organizational environments. Future Internet. 2019;11(3).
CrossRef Google scholar
[97]
Sarker IH. Machine learning for intelligent data analysis and automation in cybersecurity: current and future prospects. Ann Data Sci. 2023;10(6):1473–1498.
CrossRef Google scholar
[98]
Sarker IH. Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. 2021;2(3):1–21.
CrossRef Google scholar
[99]
Yaacoub JPA, Noura HN, Salman O, Chehab A. Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations. Int J Inf Secur. 2022;21(1):115–158.
CrossRef Google scholar
[100]
Xiang D, Cai W. Privacy protection and secondary use of health data: strategies and methods. BioMed Res Int. 2021:2021.
CrossRef Google scholar
[101]
Forcier MB, Gallois H, Mullan S, Joly Y. Integrating artificial intelligence into health care through data access: can the GDPR act as a beacon for policymakers? J Law Biosci. 2019;6(1):317–335.
CrossRef Google scholar
[102]
Jarab AS, Abu Heshmeh SR, Al Meslamani AZ. Artificial intelligence (AI) in pharmacy: an overview of innovations. J Med Econ. 2023;26(1):1261–1265.
CrossRef Google scholar
[103]
Tagde P, Tagde S, Bhattacharya T, et al. Blockchain and artificial intelligence technology in e-Health. Environ Sci Pollut Res. 2021;28(38):52810–52831.
CrossRef Google scholar
[104]
Kaur R, Gabrijelčič D, Klobučar T. Artificial intelligence for cybersecurity: literature review and future research directions. Inf Fusion. 2023;97(January).
CrossRef Google scholar
[105]
Atlam HF, Walters RJ, Wills GB, Daniel J. Fuzzy logic with expert judgment to implement an adaptive risk-based access control model for IoT. Mobile Network Appl. 2021;26(6):2545–2557.
CrossRef Google scholar
[106]
Rukhiran M, Wong-In S, Netinant P. User acceptance factors related to biometric recognition technologies of examination attendance in higher education: TAM model. Sustain Times. 2023;15(4).
CrossRef Google scholar
[107]
Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019;40(8):577–591.
CrossRef Google scholar
[108]
van der Lee M, Swen JJ. Artificial intelligence in pharmacology research and practice. Clin Transl Sci. 2023;16(1):31–36.
CrossRef Google scholar
[109]
Yadav JP, Kumar A, Maria S, Prateek G, Amita P, Vikas V. Insights into the Mechanisms of Diabetic Wounds: Pathophysiology, Molecular Targets, and Treatment Strategies through Conventional and Alternative Therapies. Springer International Publishing;2024.
CrossRef Google scholar
[110]
Jabeen A, Ranganathan S. Applications of machine learning in GPCR bioactive ligand discovery. Curr Opin Struct Biol. 2019;55:66–76.
CrossRef Google scholar
[111]
Durairaj M, Ranjani V. Data mining applications in healthcare sector: a study. Int J Sci Technol Res. 2013;2(10):29–35.
[112]
Palanisamy V, Thirunavukarasu R. Implications of big data analytics in developing healthcare frameworks–A review. J King Saud Univ Inf Sci. 2019;31(4):415–425.
CrossRef Google scholar
[113]
Seyhan AA, Carini C. Are innovation and new technologies in precision medicine paving a new era in patients centric care? J Transl Med. 2019;17:1–28.
CrossRef Google scholar
[114]
Bertucci F, Lcs AG, Monneur A, et al. E-health and “Cancer outside the hospital walls”, Big Data and artificial intelligence. Bull Cancer. 2019;107(1):102–112.
CrossRef Google scholar
[115]
Tobore I, Li J, Yuhang L, et al. Deep learning intervention for health care challenges: some biomedical domain considerations. JMIR mHealth uHealth. 2019;7(8):e11966.
CrossRef Google scholar
[116]
Pal J, Kumar D, Verma A, Pathak P. Revolutionizing diabetic wound healing: targeted therapeutic strategies based on growth factors. Obes Med. 2024;47(January):100535.
CrossRef Google scholar
[117]
Yadav JP. Based on clinical research matrix metalloprotease (MMP) inhibitors to promote diabetic wound healing. Horm Metab Res. 2023;55(11):752–757.
CrossRef Google scholar
[118]
Kolla L, Gruber FK, Khalid O, Hill C, Parikh RB. The case for AI-driven cancer clinical trials-The efficacy arm in silico. Biochim Biophys Acta, Rev Cancer. 2021;1876(1):188572.
CrossRef Google scholar
[119]
Yousefirizi F, Decazes P, Amyar A, Ruan S, Saboury B, Rahmim A. AI-based detection, classification and prediction/prognosis in medical imaging: towards radiophenomics. Pet Clin. 2022;17(1):183–212.
CrossRef Google scholar
[120]
van Laar SA, Gombert-Handoko KB, Guchelaar H, Zwaveling J. An electronic health record text mining tool to collect real-world drug treatment outcomes: a validation study in patients with metastatic renal cell carcinoma. Clin Pharmacol Ther. 2020;108(3):644–652.
CrossRef Google scholar
[121]
Venkatapurapu SP, Iwakiri R, Udagawa E, et al. A computational platform integrating a mechanistic model of Crohn’s disease for predicting temporal progression of mucosal damage and healing. Adv Ther. 2022;39(7):3225–3247.
CrossRef Google scholar
[122]
Sweilam NH, Tharwat AA, Moniem NKA. Support vector machine for diagnosis cancer disease: a comparative study. Egypt Informatics J. 2010;11(2):81–92.
CrossRef Google scholar
[123]
Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev. 2012;36(4):1140–1152.
CrossRef Google scholar
[124]
Dheeba J, Singh NA, Selvi ST. Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J Biomed Inf. 2014;49:45–52.
CrossRef Google scholar
[125]
Khedher L, Ramírez J, Górriz JM, Brahim A, Segovia F. Early diagnosis of Alzheimer’s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing. 2015;151(P1):139–150.
CrossRef Google scholar
[126]
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410.
CrossRef Google scholar
[127]
Griffis JC, Allendorfer JB, Szaflarski JP. Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans. J Neurosci Methods. 2016;257:97–108.
CrossRef Google scholar
[128]
Kamnitsas K, Ledig C, Newcombe VFJ, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61–78.
CrossRef Google scholar
[129]
Long E, Lin H, Liu Z, et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat Biomed Eng. 2017;1(2):24.
CrossRef Google scholar
[130]
Smith GF. Artificial intelligence in drug safety and metabolism. Artif Intell Drug Des. 2022:483–501. Published online.
CrossRef Google scholar
[131]
van Gelder T, Vinks AA. Machine learning as a novel method to support therapeutic drug management and precision dosing. Clin Pharmacol Ther. 2021;110(2):273–276.
CrossRef Google scholar
[132]
Labriffe M, Woillard J, Debord J, Marquet P. Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles. CPT Pharmacometrics Syst Pharmacol. 2022;11(8):1018–1028.
CrossRef Google scholar
[133]
McInnes G, Dalton R, Sangkuhl K, et al. Transfer learning enables prediction of CYP2D6 haplotype function. PLoS Comput Biol. 2020;16(11):e1008399.
CrossRef Google scholar
[134]
Kim H, Kim E, Lee I, Bae B, Park M, Nam H. Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches. Biotechnol Bioproc Eng. 2020;25:895–930.
CrossRef Google scholar
[135]
Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Doğan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Briefings Bioinf. 2019;20(5):1878–1912.
CrossRef Google scholar
[136]
Morris GM, Lim-Wilby M. Molecular docking. Mol Model proteins. 2008:365–382. Published online.
CrossRef Google scholar
[137]
Trott O, Olson AJ. 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.
CrossRef Google scholar
[138]
Friesner RA, Banks JL, Murphy RB, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem. 2004;47(7):1739–1749.
CrossRef Google scholar
[139]
Ewing TJA, Makino S, Skillman AG, Kuntz ID. Dock 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des. 2001;15:411–428.
CrossRef Google scholar
[140]
Pinzi L, Rastelli G. Molecular docking: shifting paradigms in drug discovery. Int J Mol Sci. 2019;20(18):4331.
CrossRef Google scholar
[141]
Ballester PJ, Mitchell JBO. A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics. 2010;26(9):1169–1175.
CrossRef Google scholar
[142]
Fan N, Bauer CA, Stork C, de Bruyn Kops C, Kirchmair J. ALADDIN: docking approach augmented by machine learning for protein structure selection yields superior virtual screening performance. Mol Inform. 2020;39(4):1900103.
CrossRef Google scholar
[143]
Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021;25:1315–1360.
CrossRef Google scholar
[144]
Jiménez-Luna J, Cuzzolin A, Bolcato G, Sturlese M, Moro S. A deep-learning approach toward rational molecular docking protocol selection. Molecules. 2020;25(11):2487.
CrossRef Google scholar
[145]
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.
CrossRef Google scholar
[146]
Dai W, Guo D. A ligand-based virtual screening method using direct quantification of generalization ability. Molecules. 2019;24(13):2414.
CrossRef Google scholar
[147]
Lima AN, Philot EA, Trossini GHG, Scott LPB, Maltarollo VG, Honorio KM. Use of machine learning approaches for novel drug discovery. Expet Opin Drug Discov. 2016;11(3):225–239.
CrossRef Google scholar
[148]
Abdolmaleki A, Ghasemi JB. Inhibition activity prediction for a dataset of candidates’ drug by combining fuzzy logic with MLR/ANN QSAR models. Chem Biol Drug Des. 2019;93(6):1139–1157.
CrossRef Google scholar
[149]
Žuvela P, David J, Wong MW. Interpretation of ANN-based QSAR models for prediction of antioxidant activity of flavonoids. J Comput Chem. 2018;39(16):953–963.
CrossRef Google scholar
[150]
Paolini GV, Shapland RHB, van Hoorn WP, Mason JS, Hopkins AL. Global mapping of pharmacological space. Nat Biotechnol. 2006;24(7):805–815.
CrossRef Google scholar
[151]
Gottlieb A, Stein GY, Ruppin E, Sharan R. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol. 2011;7(1):496.
CrossRef Google scholar
[152]
Xia Z, Wu LY, Zhou X, Wong STC. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst Biol. 2010;4:1–16. BioMed Central.
CrossRef Google scholar
[153]
Luo Y, Zhao X, Zhou J, et al. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat Commun. 2017;8(1):573.
CrossRef Google scholar
[154]
Giuliani S, Silva AC, Borba JVVB, et al. Computationally-guided drug repurposing enables the discovery of kinase targets and inhibitors as new schistosomicidal agents. PLoS Comput Biol. 2018;14(10):e1006515.
CrossRef Google scholar

RIGHTS & PERMISSIONS

2024 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
AI Summary AI Mindmap
PDF(2412 KB)

Accesses

Citations

Detail

Sections
Recommended

/