Can computers conceive the complexity of cancer to cure it? Using artificial intelligence technology in cancer modelling and drug discovery

被引:6
作者
Adams, Rachael C. [1 ]
Rashidieh, Behnam [1 ]
机构
[1] QIMR Berghofer Med Res Inst, 300 Herston Rd, Herston, Qld 4006, Australia
关键词
artificial intelligence; machine learning; deep learning; cancer modeling; drug discovery; MOLECULAR-DYNAMICS SIMULATION; MATHEMATICAL-MODEL; BREAST-CANCER; PREDICTION; BIOMARKERS; RESISTANCE; INHIBITORS; HALLMARKS;
D O I
10.3934/mbe.2020340
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Drug discovery and the development of safe and effective therapeutics is an intricate procedure, further complicated in the context of cancer research by the inherent heterogeneity and complexity of the disease. To address the difficulties of identifying, validating, and pursuing a promising drug target, artificial intelligence (AI) technologies including machine learning (ML) have been adopted at all stages throughout the drug development pipeline. Various methods are widely employed to efficiently process and learn from experimental data sets, with agent-based models garnering thorough interest due to their ability to model individual cell populations with aberrant phenotypes. The predictive power of artificial intelligence modelling techniques founded in comprehensive datasets and automated decision-making generates an obvious avenue of interest for application in the drug discovery pipeline.
引用
收藏
页码:6515 / 6530
页数:16
相关论文
共 81 条
[1]  
Alber MS, 2003, IMA VOL MATH APPL, V134, P1
[2]   Vertex models: from cell mechanics to tissue morphogenesis [J].
Alt, Silvanus ;
Ganguly, Poulami ;
Salbreux, Guillaume .
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 2017, 372 (1720)
[3]   Interaction and molecular dynamics simulation study of Osimertinib (AstraZeneca 9291) anticancer drug with the EGFR kinase domain in native protein and mutated L844V and C797S [J].
Assadollahi, Vahideh ;
Rashidieh, Behnam ;
Alasvand, Masoud ;
Abdolahi, Alina ;
Lopez, J. Alejandro .
JOURNAL OF CELLULAR BIOCHEMISTRY, 2019, 120 (08) :13046-13055
[4]   Molecular Dynamics Simulation of EFGR L844V Mutant Sensitive to AZD9291 in Non-Small Cell Lung Cancer [J].
Assadollahi, Vahideh ;
Rashidieh, Behnam .
JOURNAL OF THORACIC ONCOLOGY, 2017, 12 (01) :S1210-S1210
[5]   Introduction: Teaching Black Lives Matter [J].
Austin, Paula ;
Cardwell, Erica ;
Kennedy, Christopher ;
Spencer, Robyn .
RADICAL TEACHER, 2016, (106) :13-17
[6]   Use of a Novel Artificial Intelligence Platform on Mobile Devices to Assess Dosing Compliance in a Phase 2 Clinical Trial in Subjects With Schizophrenia [J].
Bain, Earle E. ;
Shafner, Laura ;
Walling, David P. ;
Othman, Ahmed A. ;
Chuang-Stein, Christy ;
Hinkle, John ;
Hanina, Adam .
JMIR MHEALTH AND UHEALTH, 2017, 5 (02)
[7]  
Becker A, 2012, MONITORING THE NERVOUS SYSTEM FOR ANESTHESIOLOGISTS AND OTHER HEALTH CARE PROFESSIONALS, P3, DOI 10.1007/978-1-4614-0308-1_1
[8]   A MATHEMATICAL-MODEL OF THE DEVELOPMENT OF DRUG-RESISTANCE TO CANCER-CHEMOTHERAPY [J].
BIRKHEAD, BG ;
RANKIN, EM ;
GALLIVAN, S ;
DONES, L ;
RUBENS, RD .
EUROPEAN JOURNAL OF CANCER & CLINICAL ONCOLOGY, 1987, 23 (09) :1421-1427
[9]   Agent-based modeling: Methods and techniques for simulating human systems [J].
Bonabeau, E .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2002, 99 :7280-7287
[10]   Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research [J].
Bravo, Alex ;
Pinero, Janet ;
Queralt-Rosinach, Nuria ;
Rautschka, Michael ;
Furlong, Laura I. .
BMC BIOINFORMATICS, 2015, 16