Prediction of Metabolism of Drugs Using Artificial Intelligence: How far have we Reached?

被引:24
|
作者
Kumar, Rajnish [1 ]
Sharma, Anju [1 ]
Siddiqui, Mohammed Haris [2 ]
Tiwari, Rajesh Kumar [1 ]
机构
[1] Amity Univ Uttar Pradesh, Amity Inst Biotechnol, Lucknow 226028, Uttar Pradesh, India
[2] Integral Univ, Dept Bioengn, PO Basha,Kursi Rd, Lucknow 226026, Uttar Pradesh, India
关键词
Artificial intelligence; drug designing; drug metabolism; machine learning; pharmacokinetics; prediction; MACHINE-LEARNING TECHNIQUES; CYP-MEDIATED SITES; CYTOCHROME-P450; 3A4; CHEMICAL METABOLISM; 1A2; INHIBITION; RS-PREDICTOR; MODELS; REGIOSELECTIVITY; CLASSIFICATION; STABILITY;
D O I
10.2174/1389200216666151103121352
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Information about drug metabolism is an essential component of drug development. Modeling the drug metabolism requires identification of the involved enzymes, rate and extent of metabolism, the sites of metabolism etc. There has been continuous attempts in the prediction of metabolism of drugs using artificial intelligence in effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are number of predictive models available for metabolism using Support vector machines, Artificial neural networks, Bayesian classifiers etc. There is an urgent need to review their progress so far and address the existing challenges in prediction of metabolism. In this attempt, we are presenting the currently available literature models and some of the critical issues regarding prediction of drug metabolism.
引用
收藏
页码:129 / 141
页数:13
相关论文
共 50 条
  • [41] THE PREDICTION OF COTTON YARN PROPERTIES USING ARTIFICIAL-INTELLIGENCE
    STJEPANOVIC, Z
    JEZERNIK, A
    COMPUTERS IN INDUSTRY, 1991, 17 (2-3) : 217 - 223
  • [42] Prediction of Drug-plasma Protein Binding using Artificial Intelligence Based Algorithms
    Kumar, Rajnish
    Sharma, Anju
    Siddiqui, Mohammed Haris
    Tiwari, Rajesh Kumar
    COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING, 2018, 21 (01) : 57 - 64
  • [43] Commentary: Spinopelvic parameters-how far have we come?
    Cheng, Ivan
    SPINE JOURNAL, 2012, 12 (05): : 447 - 448
  • [44] Position Prediction in Space System for Vehicles Using Artificial Intelligence
    Lee, Won-Chan
    Jeon, You-Boo
    Han, Seong-Soo
    Jeong, Chang-Sung
    SYMMETRY-BASEL, 2022, 14 (06):
  • [45] Prediction of preterm birth using artificial intelligence: a systematic review
    Akazawa, Munetoshi
    Hashimoto, Kazunori
    JOURNAL OF OBSTETRICS AND GYNAECOLOGY, 2022, 42 (06) : 1662 - 1668
  • [46] The European Draft Regulation on Artificial Intelligence: Houston, We Have a Problem
    Raposo, Vera Lucia
    PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022, 2022, 13566 : 66 - 73
  • [47] Drug-target interaction prediction using artificial intelligence
    Yaseen, Baraa Taha
    Kurnaz, Sefer
    APPLIED NANOSCIENCE, 2021, 13 (5) : 3335 - 3345
  • [48] A review of bus arrival time prediction using artificial intelligence
    Singh, Nisha
    Kumar, Kranti
    WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY, 2022, 12 (04)
  • [49] Prediction of Concrete Compressive Strength Using Artificial Intelligence Methods
    Muliauwan, H. N.
    Prayogo, D.
    Gaby, G.
    Harsono, K.
    2ND INTERNATIONAL CONFERENCE ON SUSTAINABLE INFRASTRUCTURE, 2020, 1625
  • [50] Cancer Chemoprevention by Natural Products: How Far Have We Come?
    Mehta, Rajendra G.
    Murillo, Genoveva
    Naithani, Rajesh
    Peng, Xinjian
    PHARMACEUTICAL RESEARCH, 2010, 27 (06) : 950 - 961