
Advancements in contemporary pharmacological innovation: Mechanistic insights and emerging trends in drug discovery and development
Sanjoy Majumder, Gagan Kumar Panigrahi
Intelligent Pharmacy ›› 2025, Vol. 3 ›› Issue (2) : 118-126.
Advancements in contemporary pharmacological innovation: Mechanistic insights and emerging trends in drug discovery and development
Developing a new drug and bringing it to the market is a complex and time-consuming process that involves multiple phases of drug discovery and development. However, recent advancements in various technologies, such as multi-omics, genome editing, Artificial Intelligence (AI), and Machine Learning (ML), have significantly improved this process. These technologies have made the process more accurate, less time-consuming, and cost-effective compared to the conventional methods of drug discovery and development. In the current age, discovering and developing drugs is a collaborative effort that involves scientific breakthroughs, technological advancements, and regulatory oversight. The pharmaceutical industry is constantly innovating new techniques, fostering interdisciplinary collaboration, and prioritizing patient-centered approaches. In this review, we explore the latest and most updated information about using advanced technologies in drug discovery. The review begins by briefly explaining the conventional drug discovery and development process, and then delves into the applications of multi-omics, genome editing technology, systems biology, artificial intelligence, and machine learning.
Artificial intelligence / Drugs / Genome-editing / Machine learning / Multi-omics / Systems biology
[1] |
Dias DA , Urban S , Roessner U . A historical overview of natural products in drug discovery. Metabolites. 2012; 2 (2): 303- 336.
CrossRef
Google scholar
|
[2] |
Panigrahi GK , Sahoo A , Satapathy KB . Insights to plant immunity: defense signaling to epigenetics. Physiol Mol Plant Pathol. 2021; 113: 101568.
CrossRef
Google scholar
|
[3] |
Jones AW . Early drug discovery and the rise of pharmaceutical chemistry. Drug Test Anal. 2011; 3 (6): 337- 344. Jun.
CrossRef
Google scholar
|
[4] |
Singh N , Vayer P , Tanwar S , Poyet JL , Tsaioun K , Villoutreix BO . Drug discovery and development: introduction to the general public and patient groups. Front Drug Disco. 2023; 3: 1201419.
CrossRef
Google scholar
|
[5] |
Singh S , Kumar R , Payra S , Singh SK . Artificial intelligence and machine learning in pharmacological research: bridging the gap between data and drug discovery. Cureus. 2023; 15 (8): e44359. Aug 30.
CrossRef
Google scholar
|
[6] |
Hopkins AL , Groom CR . The druggable genome. Nat Rev Drug Discov. 2002; 1 (9): 727- 730.
CrossRef
Google scholar
|
[7] |
Xu H , Xu H , Lin M , et al. Learning the drug target-likeness of a protein. Proteomics. 2007; 7 (23): 4255- 4263.
CrossRef
Google scholar
|
[8] |
Patro I , Sahoo A , Nayak BR , Das R , Majumder S , Panigrahi GK . Nonsense-mediated mRNA decay: mechanistic insights and physiological significance. Mol Biotechnol. 2023: 1- 15.
CrossRef
Google scholar
|
[9] |
Sakharkar MK , Rajamanickam K , Babu CS , Madan J , Chandra R , Yang J . Preclinical: Drug Target Identification and Validation in Human. 2019.
CrossRef
Google scholar
|
[10] |
Emmerich CH , Gamboa LM , Hofmann MC , et al. Improving target assessment in biomedical research: the GOT-IT recommendations. Nat Rev Drug Discov. 2021; 20 (1): 64- 81.
CrossRef
Google scholar
|
[11] |
Ikeda K , Maezawa Y , Yonezawa T , et al. DLiP-PPI library: an integrated chemical database of small-to-medium-sized molecules targeting protein-protein interactions. Front Chem. 2023; 10: 1090643.
CrossRef
Google scholar
|
[12] |
Chaudhari R , Fong LW , Tan Z , Huang B , Zhang S . An up-to-date overview of computational polypharmacology in modern drug discovery. Expet Opin Drug Discov. 2020; 15 (9): 1025- 1044.
CrossRef
Google scholar
|
[13] |
Kabir A , Muth A . Polypharmacology: the science of multi-targeting molecules. Pharmacol Res. 2022; 176: 106055. Feb.
CrossRef
Google scholar
|
[14] |
Hassan Baig M , Ahmad K , Roy S , et al. Computer aided drug design: success and limitations. Curr Pharmaceut Des. 2016; 22 (5): 572- 581.
CrossRef
Google scholar
|
[15] |
Gurung AB , Ali MA , Lee J , Farah MA , Al-Anazi KM . An updated review of computer-aided drug design and its application to COVID-19. BioMed Res Int. 2021: 2021.
CrossRef
Google scholar
|
[16] |
Giordano D , Biancaniello C , Argenio MA , Facchiano A . Drug design by pharmacophore and virtual screening approach. Pharmaceuticals. 2022; 15 (5): 646.
CrossRef
Google scholar
|
[17] |
Alhaji Isa M , Singh Majumdar R . Computer-aided drug design based on comparative modeling, molecular docking and molecular dynamic simulation of Polyphosphate kinase (PPK) from Mycobacterium tuberculosis. J Protein Proteonomics. 2019; 10: 55- 68.
CrossRef
Google scholar
|
[18] |
Katsila T , Spyroulias GA , Patrinos GP , Matsoukas MT . Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J. 2016; 14: 177- 184.
CrossRef
Google scholar
|
[19] |
Blanco-Gonzalez A , Cabezon A , Seco-Gonzalez A , et al. The role of ai in drug discovery: challenges, opportunities, and strategies. Pharmaceuticals. 2023; 16 (6): 891.
CrossRef
Google scholar
|
[20] |
Kiriiri GK , Njogu PM , Mwangi AN . Exploring different approaches to improve the success of drug discovery and development projects: a review. Futur J Pharm Sci. 2020; 6.
CrossRef
Google scholar
|
[21] |
Karmakar P , Trivedi A , Gaitonde V . Introductory Chapter: The Modern-Day Drug Discovery. Drug Discovery and Development-New Advances; 2020.
CrossRef
Google scholar
|
[22] |
Leelananda SP , Lindert S . Computational methods in drug discovery. Beilstein J Org Chem. 2016; 12 (1): 2694- 2718.
CrossRef
Google scholar
|
[23] |
Maia EHB , Assis LC , De Oliveira TA , Da Silva AM , Taranto AG . Structure-based virtual screening: from classical to artificial intelligence. Front Chem. 2020; 8: 343.
CrossRef
Google scholar
|
[24] |
Damm-Ganamet KL , Arora N , Becart S , et al. Accelerating lead identification by high Throughput virtual screening: prospective case studies from the pharmaceutical industry. J Chem Inf Model. 2019; 59 (5): 2046- 2062.
CrossRef
Google scholar
|
[25] |
Lavecchia A , Di Giovanni C . Virtual screening strategies in drug discovery: a critical review. Curr Med Chem. 2013; 20 (23): 2839- 2860.
CrossRef
Google scholar
|
[26] |
Liu SH , Xiao Z , Mishra SK , et al. Identification of small-molecule inhibitors of fibroblast growth factor 23 signaling via in silico hot spot prediction and molecular docking to α-Klotho. J Chem Inf Model. 2022; 62 (15): 3627- 3637.
CrossRef
Google scholar
|
[27] |
Liu X , Yi W , Xi B , Dai Q . Identification of drug-disease associations using a random walk with restart method and supervised learning. Comput Math Methods Med. 2022: 1- 10, 7035634.
CrossRef
Google scholar
|
[28] |
Ibrahim MA , Abdeljawaad KA , Abdelrahman AH , et al. Exploring natural product activity and species source candidates for hunting ABCB1 transporter inhibitors: an in silico drug discovery study. Molecules. 2022; 27 (10): 3104.
CrossRef
Google scholar
|
[29] |
Zhang Q , Han J , Zhu Y , et al. Discovery of novel and potent InhA direct inhibitors by ensemble docking-based virtual screening and biological assays. J Comput Aided Mol Des. 2023; 37 (12): 695- 706.
CrossRef
Google scholar
|
[30] |
Zhang Y , Li S , Xing M , Yuan Q , He H , Sun S . Universal approach to de novo drug design for target proteins using deep reinforcement learning. ACS Omega. 2023; 8 (6): 5464- 5474.
CrossRef
Google scholar
|
[31] |
Anand R . Identification of potential antituberculosis drugs through docking and virtual screening. Interdiscipl Sci Comput Life Sci. 2018; 10: 419- 429.
CrossRef
Google scholar
|
[32] |
Bagherian M , Sabeti E , Wang K , Sartor MA , Nikolovska-Coleska Z , Najarian K . Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. Briefings Bioinf. 2021; 22 (1): 247- 269.
CrossRef
Google scholar
|
[33] |
Burley SK , Bhikadiya C , Bi C , et al. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res. 2021; 49 (D1): D437- D451.
CrossRef
Google scholar
|
[34] |
Jadaun A , Subbarao N , Dixit A . Allosteric inhibition of topoisomerase I by pinostrobin: molecular docking, spectroscopic and topoisomerase I activity studies. J Photochem Photobiol B Biol. 2017; 167: 299- 308.
CrossRef
Google scholar
|
[35] |
Sethi A , Joshi K , Sasikala K , Alvala M . Molecular docking in modern drug discovery: principles and recent applications. Drug disco develo new adv. 2019; 2: 1- 21.
CrossRef
Google scholar
|
[36] |
Halder D , Das S , Jeyaprakash RS . Identification of natural product as selective PI3Kα inhibitor against NSCLC: multi-ligand pharmacophore modeling, molecular docking, ADME, DFT, and MD simulations. Mol Divers. 2023. Sep 15.
CrossRef
Google scholar
|
[37] |
Shah Manan , Patel Maanit , Shah Monit , Patel Monali , Prajapati Mitul . Computational transformation in drug discovery: a comprehensive study on molecular docking and quantitative structure activity relationship (QSAR). Intelligent Pharmacy. 2024.
CrossRef
Google scholar
|
[38] |
Matter H , Sotriffer C . Applications and success stories in virtual screening. In: Sotriffer C, ed. Virtual Screening: Principles, Challenges, and Practical Guidelines. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA; 2011: 319- 358.
CrossRef
Google scholar
|
[39] |
Pina AS , Hussain A , Roque ACA . An historical overview of drug discovery. LigandMacromolecular Interactions Drug Disco: Metho Proto. 2010: 3- 12.
CrossRef
Google scholar
|
[40] |
Doytchinova I . Drug design—past, present, future. Molecules. 2022; 27 (5): 1496.
CrossRef
Google scholar
|
[41] |
Mohs RC , Greig NH . Drug discovery and development: role of basic biological research. Alzheimers Dement. 2017; 3 (4): 651- 657. Nov 11.
CrossRef
Google scholar
|
[42] |
Behera A , Panigrahi GK , Sahoo SK . Nonsense-Mediated mRNA decay in human health and diseases: current understanding, regulatory mechanisms and future perspectives. Mol Biotechnol. 2024.
CrossRef
Google scholar
|
[43] |
Qin D . Next-generation sequencing and its clinical application. Cancer Biol Med. 2019; 16 (1): 4- 10. Feb.
CrossRef
Google scholar
|
[44] |
Zhao BW , Su XR , Hu PW , Huang YA , You ZH , Hu L . iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network. Bioinformatics. 2023; 39 (8): btad451.
CrossRef
Google scholar
|
[45] |
Nisha CM , Kumar A , Nair P , et al. Molecular docking and in silico ADMET study reveals acylguanidine 7a as a potential inhibitor of β-secretase. Adv bioinform. 2016: 2016.
CrossRef
Google scholar
|
[46] |
Dara S , Dhamercherla S , Jadav SS , Babu CM , Ahsan MJ . Machine learning in drug discovery: a review. Artif Intell Rev. 2022; 55 (3): 1947- 1999.
CrossRef
Google scholar
|
[47] |
Carpenter KA , Huang X . Machine learning-based virtual screening and its applications to Alzheimer's drug discovery: a review. Curr Pharmaceut Des. 2018; 24 (28): 3347- 3358.
CrossRef
Google scholar
|
[48] |
Oliveira TAD , Silva MPD , Maia EHB , Silva AMD , Taranto AG . Virtual screening algorithms in drug discovery: a review focused on machine and deep learning methods. Drugs Drug Candida. 2023; 2 (2): 311- 334.
CrossRef
Google scholar
|
[49] |
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
|
[50] |
Niazi SK , Mariam Z . Recent advances in machine-learning-based chemoinformatics: a comprehensive review. Int J Mol Sci. 2023; 24 (14): 11488. Jul 15.
CrossRef
Google scholar
|
[51] |
Batool M , Ahmad B , Choi S . A structure-based drug discovery paradigm. Int J Mol Sci. 2019; 20 (11): 2783.
CrossRef
Google scholar
|
[52] |
Gibbs RA . The human genome project changed everything. Nat Rev Genet. 2020; 21 (10): 575- 576.
CrossRef
Google scholar
|
[53] |
Somda D , Kpordze SW , Jerpkorir M , et al. The Role of Bioinformatics in Drug Discovery: A Comprehensive Overview. 2023.
CrossRef
Google scholar
|
[54] |
Wingert BM , Camacho CJ . Improving small molecule virtual screening strategies for the next generation of therapeutics. Curr Opin Chem Biol. 2018; 44: 87- 92. Jun.
CrossRef
Google scholar
|
[55] |
Singh SP , Panigrahi GK , Sahoo A . Optimizing nanoparticles use for growth promotion in zebrafish: insights on concentration-dependent effects. Nanotechnol Environ Eng. 2024.
CrossRef
Google scholar
|
[56] |
Paananen Jussi , Fortino Vittorio . An omics perspective on drug target discovery platforms. Briefings Bioinf. 2020; 21 (6). November.
CrossRef
Google scholar
|
[57] |
Xia X . Bioinformatics and drug discovery. Curr Top Med Chem. 2017; 17 (15): 1709- 1726.
CrossRef
Google scholar
|
[58] |
Wooller SK , Benstead-Hume G , Chen X , Ali Y , Pearl FM . Bioinformatics in translational drug discovery. Biosci Rep. 2017; 37 (4): BSR20160180.
CrossRef
Google scholar
|
[59] |
Aggarwal S , Karmakar A , Krishnakumar S , et al. Advances in drug discovery based on genomics, proteomics and bioinformatics in malaria. Curr Top Med Chem. 2023; 23 (7): 551- 578.
CrossRef
Google scholar
|
[60] |
Russell C , Rahman A , Mohammed AR . Application of genomics, proteomics and metabolomics in drug discovery, development and clinic. Ther Deliv. 2013; 4 (3): 395- 413.
CrossRef
Google scholar
|
[61] |
de Oliveira Viana J , Scotti MT , Scotti L . Molecular docking studies in multitarget antitubercular drug discovery. Multi-Target Drug Design Using Chem-Bioinformatic Approaches. 2019: 107- 154.
CrossRef
Google scholar
|
[62] |
Zhang Y , Luo M , Wu P , Wu S , Lee TY , Bai C . Application of computational biology and artificial intelligence in drug design. Int J Mol Sci. 2022; 23 (21): 13568.
CrossRef
Google scholar
|
[63] |
Li H , Yang Y , Hong W , Huang M , Wu M , Zhao X . Applications of genome editing technology in the targeted therapy of human diseases: mechanisms, advances and prospects. Signal Transduct Targeted Ther. 2020; 5 (1): 1.
CrossRef
Google scholar
|
[64] |
Li K , Du Y , Li L , Wei DQ . Bioinformatics approaches for anti-cancer drug discovery. Curr Drug Targets. 2020; 21 (1): 3- 17.
CrossRef
Google scholar
|
[65] |
Koromina M , Pandi MT , Patrinos GP . Rethinking drug repositioning and development with artificial intelligence, machine learning, and omics. OMICS, Nov. 2019; 23 (11): 539- 548.
CrossRef
Google scholar
|
[66] |
Chakraborty C , Teoh SL , Das S . The smart programmable CRISPR technology: a next generation genome editing tool for investigators. Curr Drug Targets. 2017; 18 (14): 1653- 1663.
CrossRef
Google scholar
|
[67] |
Zhang C , Quan R , Wang J . Development and application of CRISPR/Cas9 technologies in genomic editing. Hum Mol Genet. 2018; 27 (R2): R79- R88. Aug 1.
CrossRef
Google scholar
|
[68] |
Chanchal DK , Chaudhary JS , Kumar P , Agnihotri N , Porwal P . CRISPR-based therapies: revolutionizing drug development and precision medicine. Curr Gene Ther. 2024; 24 (3): 193- 207.
CrossRef
Google scholar
|
[69] |
Liu W , Li L , Jiang J , Wu M , Lin P . Applications and challenges of CRISPR-Cas gene-editing to disease treatment in clinics. Precision clini med. 2021; 4 (3): 179- 191.
CrossRef
Google scholar
|
[70] |
Fellmann C , Gowen BG , Lin PC , Doudna JA , Corn JE . Cornerstones of CRISPR-Cas in drug discovery and therapy. Nat Rev Drug Discov. 2017; 16 (2): 89- 100.
CrossRef
Google scholar
|
[71] |
Chan YT , Lu Y , Wu J , et al. CRISPR-Cas9 library screening approach for anti-cancer drug discovery: overview and perspectives. Theranostics. 2022; 12 (7): 3329.
CrossRef
Google scholar
|
[72] |
Zou J , Zheng MW , Li G , Su ZG . Advanced systems biology methods in drug discovery and translational biomedicine. BioMed Res Int. 2013: 2013.
CrossRef
Google scholar
|
[73] |
Yue R , Dutta A . Computational systems biology in disease modeling and control, review and perspectives. npj Syst Biol Appl. 2022.
CrossRef
Google scholar
|
[74] |
Ebrahimi A , Roshani F . Systems biology approaches to identify driver genes and drug combinations for treating COVID-19. Sci Rep. 2024; 14 (1): 2257.
CrossRef
Google scholar
|
[75] |
Leung EL , Cao ZW , Jiang ZH , Zhou H , Liu L . Network-based drug discovery by integrating systems biology and computational technologies. Briefings Bioinf. 2013; 14 (4): 491- 505.
CrossRef
Google scholar
|
[76] |
Chua HN , Roth FP . Discovering the targets of drugs via computational systems biology. J Biol Chem. 2011; 286 (27): 23653- 23658. Jul 8.
CrossRef
Google scholar
|
[77] |
Malandraki-Miller S , Riley PR . Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today. 2021; 26 (4): 887- 901. Apr.
CrossRef
Google scholar
|
[78] |
Chan HS , Shan H , Dahoun T , Vogel H , Yuan S . Advancing drug discovery via artificial intelligence. Trends Pharmacol Sci. 2019; 40 (8): 592- 604.
CrossRef
Google scholar
|
[79] |
Deng L , Zhong W , Zhao L , et al. Artificial intelligence-based application to explore inhibitors of neurodegenerative diseases. Front Neurorob. 2020; 14: 617327.
CrossRef
Google scholar
|
[80] |
Tripathi MK , Nath A , Singh TP , Ethayathulla AS , Kaur P . Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery. Mol Divers. 2021; 25: 1439- 1460.
CrossRef
Google scholar
|
[81] |
Mak KK , Pichika MR . Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019; 24 (3): 773- 780.
CrossRef
Google scholar
|
[82] |
Cruz JA , Wishart DS . Applications of machine learning in cancer prediction and prognosis. Cancer Inf. 2006; 2: 117693510600200030.
CrossRef
Google scholar
|
[83] |
Paul D , Sanap G , Shenoy S , Kalyane D , Kalia K , Tekade RK . Artificial intelligence in drug discovery and development. Drug Discov Today. 2021; 26 (1): 80- 93.
CrossRef
Google scholar
|
[84] |
Selvaraj C , Chandra I , Singh SK . Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Mol Divers. 2021: 1- 21.
CrossRef
Google scholar
|
[85] |
Kumar M , Nguyen TN , Kaur J , et al. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep. 2023; 75 (1): 3- 18.
CrossRef
Google scholar
|
[86] |
Han R , Yoon H , Kim G , Lee H , Lee Y . Revolutionizing medicinal chemistry: the application of artificial intelligence (AI) in early drug discovery. Pharmaceuticals. 2023; 16 (9): 1259.
CrossRef
Google scholar
|
[87] |
Álvarez-Machancoses Ó , Fernández-Martínez JL . Using artificial intelligence methods to speed up drug discovery. Expet Opin Drug Discov. 2019; 14 (8): 769- 777.
CrossRef
Google scholar
|
[88] |
Bess A , Berglind F , Mukhopadhyay S , et al. Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases. Drug Discov Today. 2022; 27 (4): 1099- 1107.
CrossRef
Google scholar
|
[89] |
Visan AI , Negut I . Integrating artificial intelligence for drug discovery in the context of revolutionizing drug delivery. Life. 2024; 14 (2): 233. Feb 7.
CrossRef
Google scholar
|
[90] |
Jeon J , Nim S , Teyra J , et al. A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening. Genome Med. 2014; 6: 1- 18.
CrossRef
Google scholar
|
[91] |
Vamathevan J , Clark D , Czodrowski P , et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019; 18 (6): 463- 477.
CrossRef
Google scholar
|
[92] |
Lee I , Keum J , Nam H . DeepConv-DTI: prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput Biol. 2019; 15 (6): e1007129. Jun 14.
CrossRef
Google scholar
|
[93] |
Bender A , Cortés-Ciriano I . Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: ways to make an impact, and why we are not there yet. Drug Discov Today. 2021; 26 (2): 511- 524.
CrossRef
Google scholar
|
[94] |
Sliwoski G , Kothiwale S , Meiler J , Lowe EW . Computational methods in drug discovery. Pharmacol Rev. 2014; 66 (1): 334- 395.
CrossRef
Google scholar
|
[95] |
Khan O , Badhiwala JH , Grasso G , Fehlings MG . Use of machine learning and artificial intelligence to drive personalized medicine approaches for spine care. World neurosurgery. 2020; 140: 512- 518.
CrossRef
Google scholar
|
[96] |
Bravo Serrano À , Piñero González J , Queralt Rosinach N , Rautschka M , Furlong LI . Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research. BMC Bioinf. 2015; 16 (1): 55, 2015 Feb 21.
CrossRef
Google scholar
|
[97] |
Song M , Kim M , Kang K , Kim YH , Jeon S . Application of public knowledge discovery tool (pkde4j) to represent biomedical scientific knowledge. Front Res Metr Anal. 2018; 3: 7.
CrossRef
Google scholar
|
[98] |
Alam T , Schmeier S . Deep learning in biomedical text mining: contributions and challenges. In: Multiple Perspectives on Artificial Intelligence in Healthcare: Opportunities and Challenges. Cham: Springer International Publishing; 2021: 169- 184.
CrossRef
Google scholar
|
[99] |
Sebastiano MR , Ermondi G , Hadano S , Caron G . AI-based protein structure databases have the potential to accelerate rare diseases research: AlphaFoldDB and the case of IAHSP/Alsin. Drug Discov Today. 2022; 27 (6): 1652- 1660.
CrossRef
Google scholar
|
[100] |
Jumper J , Evans R , Pritzel A , et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021; 596 (7873): 583- 589.
CrossRef
Google scholar
|
[101] |
Bhhatarai B , Walters WP , Hop CE , Lanza G , Ekins S . Opportunities and challenges using artificial intelligence in ADME/Tox. Nat Mater. 2019; 18 (5): 418- 422.
CrossRef
Google scholar
|
[102] |
Panigrahi GK , Sahoo A , Satapathy KB . Differential expression of selected Arabidopsis resistant genes under abiotic stress conditions. Plant Sci Today. 2021; 8 (4): 859- 864.
CrossRef
Google scholar
|
[103] |
Wang DD , Zhu M , Yan H . Computationally predicting binding affinity in proteinligand complexes: free energy-based simulations and machine learning-based scoring functions. Briefings Bioinf. 2021; 22 (3): bbaa107.
CrossRef
Google scholar
|
[104] |
Born J , Manica M . Trends in deep learning for property-driven drug design. Curr Med Chem. 2021; 28 (38): 7862- 7886.
CrossRef
Google scholar
|
[105] |
Panigrahi GK , Satapathy KB . Arabidopsis DCP5, a Decapping Complex Protein Interacts with Ubiquitin-5 in the Processing Bodies. 2020.
|
[106] |
Sahoo A , Satapathy KB . Differential expression of Arabidopsis EJC core proteins under short-day and long-day growth conditions. Plant Sci Today. 2021; 8 (4): 815- 819.
CrossRef
Google scholar
|
[107] |
Panigrahi GK , Satapathy KB . Formation of Arabidopsis Poly (A)-Specific Ribonuclease associated processing bodies in response to pathogenic infection. Plant Archives. 2020; 20 (2): 4907- 4912.
|
[108] |
Panigrahi GK , Satapathy KB . Sacrificed surveillance process favours plant defense: a review. Plant Archives. 2020; 20 (2).
|
[109] |
McGibbon M , Money-Kyrle S , Blay V , Houston DR . SCORCH: improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation. J Adv Res. 2023; 46: 135- 147.
CrossRef
Google scholar
|
[110] |
Singh S , Gupta H , Sharma P , Sahi S . Advances in Artificial Intelligence (AI)-assisted approaches in drug screening. Artific Intellig Chem. 2024; 2 (1): 100039.
CrossRef
Google scholar
|
[111] |
Hughes JP , Rees S , Kalindjian SB , Philpott KL . Principles of early drug discovery. Br J Pharmacol. 2011; 162 (6): 1239- 1249. Mar.
CrossRef
Google scholar
|
[112] |
Berrhail F , Belhadef H , Haddad M . Deep Convolutional Neural Network to improve the performances of screening process in LBVS. Expert Syst Appl. 2022; 203: 117287.
CrossRef
Google scholar
|
[113] |
Bustamam A , Hamzah H , Husna NA , et al. Artificial intelligence paradigm for ligand-based virtual screening on the drug discovery of type 2 diabetes mellitus. J Big Data. 2021; 8 (1): 74.
CrossRef
Google scholar
|
[114] |
Kim S , Cho KH . PyQSAR: a fast QSAR modeling platform using machine learning and jupyter notebook. Bull Kor Chem Soc. 2019; 40 (1): 39- 44.
CrossRef
Google scholar
|
[115] |
Mu G , Liu H , Wen Y , Luan F . Quantitative structure-property relationship study for the prediction of characteristic infrared absorption of carbonyl group of commonly used carbonyl compounds. Vib Spectrosc. 2011; 55 (1): 49- 57.
CrossRef
Google scholar
|
[116] |
Soares TA , Nunes-Alves A , Mazzolari A , Ruggiu F , Wei GW , Merz K . The (Re)-Evolution of Quantitative Structure-Activity Relationship (QSAR) studies propelled by the surge of machine learning methods. J Chem Inf Model. 2022; 62 (22): 5317- 5320.
CrossRef
Google scholar
|
[117] |
Prabha T , Selvinthanuja C , Hemalatha S , Sengottuvelu S , Senthil J . Machine learning algorithm used to build a QSAR model for pyrazoline scaffold as antitubercular agent. J Med Pharm Allied Sci. 2021; 10: 4024- 4030.
CrossRef
Google scholar
|
[118] |
Guan L , Yang H , Cai Y , et al. ADMET-score -a comprehensive scoring function for evaluation of chemical drug-likeness. MedChemComm. 2018; 10 (1): 148- 157.
CrossRef
Google scholar
|
[119] |
Yang H , Sun L , Li W , Liu G , Tang Y . In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts. Front Chem. 2018; 6: 30.
CrossRef
Google scholar
|
[120] |
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
|
[121] |
Gu Y , Wang Y , Zhu K , Li W , Liu G , Tang Y . DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles. J Cheminf. 2024; 16 (1): 4.
CrossRef
Google scholar
|
[122] |
Kashyap K , Siddiqi MI . Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents. Mol Divers. 2021; 25 (3): 1517- 1539. Aug.
CrossRef
Google scholar
|
[123] |
Chopra H , Shin DK , Munjal K , Dhama K , Emran TB . Revolutionizing clinical trials: the role of AI in accelerating medical breakthroughs. Int J Surg. 2023; 109 (12): 4211- 4220.
CrossRef
Google scholar
|
[124] |
Panigrahi GK , Satapathy KB . Pseudomonas syringae pv. syringae infection orchestrates the fate of the arabidopsis J domain containing cochaperone and decapping protein factor 5. Physiol Mol Plant Pathol. 2021; 113: 1- 9, 101598.
CrossRef
Google scholar
|
[125] |
Sahoo A , Satapathy KB , Panigrahi GK . Ectopic expression of disease resistance protein promotes resistance against pathogen infection and drought stress in Arabidopsis. Physiol Mol Plant Pathol. 2023; 124: 1- 7, 101949.
CrossRef
Google scholar
|
[126] |
Moret M , Pachon Angona I , Cotos L , et al. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat Commun. 2023; 14 (1): 114.
CrossRef
Google scholar
|
[127] |
Jung HW , Panigrahi GK , Jung G-Y , et al. PAMP-triggered immunity involves proteolytic degradation of core nonsense-mediated mRNA decay factors during early defense response. Plant Cell. 2020; 32 (4): 1081- 1101.
CrossRef
Google scholar
|
[128] |
Sahoo A , Satapathy KB , Panigrahi GK . Security check: plant immunity under temperature surveillance. J Plant Biochem Biotechnol. 2023: 1- 4.
CrossRef
Google scholar
|
[129] |
Panigrahi GK , Sahoo A , Satapathy KB . The processing body component varicose plays a multiplayer role towards stress management in Arabidopsis. Plant Physiol Rep. 2024: 1- 10.
CrossRef
Google scholar
|
[130] |
Das R , Panigrahi GK . Messenger RNA surveillance: current understanding, regulatory mechanisms and future implications. Mol Biotechnol. 2024: 1- 18.
CrossRef
Google scholar
|
[131] |
Tong X , Liu X , Tan X , et al. Generative models for de novo drug design. J Med Chem. 2021; 64 (19): 14011- 14027.
CrossRef
Google scholar
|
[132] |
Mouchlis VD , Afantitis A , Serra A , et al. Advances in de novo drug design: from conventional to machine learning methods. Int J Mol Sci. 2021; 22 (4): 1676.
CrossRef
Google scholar
|
[133] |
Martinelli DD . Generative machine learning for de novo drug discovery: A systematic review. Comput Biol Med. 2022; 145: 105403.
CrossRef
Google scholar
|
[134] |
Li B , Tan K , Lao AR , Wang H , Zheng H , Zhang L . A comprehensive review of artificial intelligence for pharmacology research. Front Genet. 2024; 15: 1450529.
CrossRef
Google scholar
|
[135] |
Meli R , Morris GM , Biggin PC . Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: a review. Front bioinform. 2022; 2: 885983.
CrossRef
Google scholar
|
[136] |
Guan L , Yang H , Cai Y , et al. ADMET-score-a comprehensive scoring function for evaluation of chemical drug-likeness. Medchemcomm. 2019; 10 (1): 148- 157.
CrossRef
Google scholar
|
[137] |
Guo S , Zhang D , Wang H , et al. Computational and systematic analysis of multiomics data for drug discovery and development. Front Med. 2023; 10: 1146896.
CrossRef
Google scholar
|
[138] |
Zhong HA . ADMET properties: overview and current topics. In: Grover A, ed. Drug Design: Principles and Applications. Singapore: Springer; 2017.
CrossRef
Google scholar
|
[139] |
Lv Q , Zhou F , Liu X , Zhi L . Artificial intelligence in small molecule drug discovery from 2018 to 2023: does it really work? Bioorg Chem. 2023: 106894.
CrossRef
Google scholar
|
[140] |
Panigrahi GK , Sahoo SK , Sahoo A , et al. Bioactive molecules from plants: a prospective approach to combat SARS-CoV-2. Adv Tradit Med. 2023; 23 (3): 617- 630.
CrossRef
Google scholar
|
[141] |
Pareek V , Tuteja L , Sharma L , Kumar S , Verma N . Revolutionizing drug design with artificial intelligence: a comprehensive review of techniques, applications, and case studies. J Pharmaceut Res. 2023; 22 (3): 104.
CrossRef
Google scholar
|
[142] |
Pereira T , Abbasi M , Ribeiro B , Arrais JP . Diversity oriented deep reinforcement learning for targeted molecule generation. J Cheminf. 2021; 13 (1): 21.
CrossRef
Google scholar
|
[143] |
Wang C , Hu G , Wang K , Brylinski M , Xie L , Kurgan L . PDID: database of molecular-level putative protein-drug interactions in the structural human proteome. Bioinformatics. 2016; 32 (4): 579- 586.
CrossRef
Google scholar
|
[144] |
Yang F , Zhang Q , Ji X , et al. Machine learning applications in drug repurposing. Interdiscipl Sci Comput Life Sci. 2022; 14 (1): 15- 21.
CrossRef
Google scholar
|
[145] |
Anokian E , Bernett J , Freeman A , et al. Machine Learning and Artificial Intelligence in Drug Repurposing-Challenges and Perspectives. DrugRxiv; 2024.
CrossRef
Google scholar
|
[146] |
Fang Y , Pan X , Shen HB . De novo drug design by iterative multiobjective deep reinforcement learning with graph-based molecular quality assessment. Bioinformatics. 2023; 39 (4): btad157.
CrossRef
Google scholar
|
[147] |
Juhn Y , Liu H . Artificial intelligence approaches using natural language processing to advance EHR-based clinical research. J Allergy Clin Immunol. 2020; 145 (2): 463- 469.
CrossRef
Google scholar
|
[148] |
Askin S , Burkhalter D , Calado G , El Dakrouni S . Artificial intelligence applied to clinical trials: opportunities and challenges. Health Technol. 2023; 13 (2): 203- 213.
CrossRef
Google scholar
|
/
〈 |
|
〉 |