Literature DB >> 31886259

Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery.

Nagasundaram Nagarajan1, Edward K Y Yapp2, Nguyen Quoc Khanh Le1, Balu Kamaraj3, Abeer Mohammed Al-Subaie4, Hui-Yuan Yeh1.   

Abstract

Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into "usable" knowledge. Being well aware of this, the world's leading pharmaceutical companies have already begun to use artificial intelligence to improve their research regarding new drugs. The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources to aid drug development. Pharmaceutical and medical researchers have extensive data sets that can be analyzed by strong AI systems. This review focused on how computational biology and artificial intelligence technologies can be implemented by integrating the knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in the cancer precision drug discovery.
Copyright © 2019 Nagasundaram Nagarajan et al.

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Mesh:

Year:  2019        PMID: 31886259      PMCID: PMC6925679          DOI: 10.1155/2019/8427042

Source DB:  PubMed          Journal:  Biomed Res Int            Impact factor:   3.411


  141 in total

1.  Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening.

Authors:  Xianfeng Ma; Zheng Li; Luke E K Achenie; Hongliang Xin
Journal:  J Phys Chem Lett       Date:  2015-08-27       Impact factor: 6.475

2.  Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms.

Authors:  M C Maiden; J A Bygraves; E Feil; G Morelli; J E Russell; R Urwin; Q Zhang; J Zhou; K Zurth; D A Caugant; I M Feavers; M Achtman; B G Spratt
Journal:  Proc Natl Acad Sci U S A       Date:  1998-03-17       Impact factor: 11.205

3.  Erratum: "Perspective: Machine learning potentials for atomistic simulations" [J. Chem. Phys. 145, 170901 (2016)].

Authors:  Jörg Behler
Journal:  J Chem Phys       Date:  2016-12-07       Impact factor: 3.488

Review 4.  First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems.

Authors:  Jörg Behler
Journal:  Angew Chem Int Ed Engl       Date:  2017-08-18       Impact factor: 15.336

Review 5.  Overcoming implementation challenges of personalized cancer therapy.

Authors:  Funda Meric-Bernstam; Gordon B Mills
Journal:  Nat Rev Clin Oncol       Date:  2012-07-31       Impact factor: 66.675

6.  Machine learning bandgaps of double perovskites.

Authors:  G Pilania; A Mannodi-Kanakkithodi; B P Uberuaga; R Ramprasad; J E Gubernatis; T Lookman
Journal:  Sci Rep       Date:  2016-01-19       Impact factor: 4.379

7.  A spectral approach integrating functional genomic annotations for coding and noncoding variants.

Authors:  Iuliana Ionita-Laza; Kenneth McCallum; Bin Xu; Joseph D Buxbaum
Journal:  Nat Genet       Date:  2016-01-04       Impact factor: 38.330

8.  Evaluating Variant Calling Tools for Non-Matched Next-Generation Sequencing Data.

Authors:  Sarah Sandmann; Aniek O de Graaf; Mohsen Karimi; Bert A van der Reijden; Eva Hellström-Lindberg; Joop H Jansen; Martin Dugas
Journal:  Sci Rep       Date:  2017-02-24       Impact factor: 4.379

9.  Fast and accurate short read alignment with Burrows-Wheeler transform.

Authors:  Heng Li; Richard Durbin
Journal:  Bioinformatics       Date:  2009-05-18       Impact factor: 6.937

10.  Identifying Mendelian disease genes with the variant effect scoring tool.

Authors:  Hannah Carter; Christopher Douville; Peter D Stenson; David N Cooper; Rachel Karchin
Journal:  BMC Genomics       Date:  2013-05-28       Impact factor: 3.969

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  7 in total

1.  Molecular dynamics simulations, docking and MMGBSA studies of newly designed peptide-conjugated glucosyloxy stilbene derivatives with tumor cell receptors.

Authors:  Mia I Rico; Charlotta G Lebedenko; Saige M Mitchell; Ipsita A Banerjee
Journal:  Mol Divers       Date:  2022-01-17       Impact factor: 3.364

Review 2.  Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.

Authors:  Chandrabose Selvaraj; Ishwar Chandra; Sanjeev Kumar Singh
Journal:  Mol Divers       Date:  2021-10-23       Impact factor: 2.943

Review 3.  Implications of prognosis-associated genes in pancreatic tumor metastasis: lessons from global studies in bioinformatics.

Authors:  Sophia G Kisling; Gopalakrishnan Natarajan; Ramesh Pothuraju; Ashu Shah; Surinder K Batra; Sukhwinder Kaur
Journal:  Cancer Metastasis Rev       Date:  2021-09-30       Impact factor: 9.264

4.  Comprehensive Analysis of Prognostic and Genetic Signatures for General Transcription Factor III (GTF3) in Clinical Colorectal Cancer Patients Using Bioinformatics Approaches.

Authors:  Gangga Anuraga; Wan-Chun Tang; Nam Nhut Phan; Hoang Dang Khoa Ta; Yen-Hsi Liu; Yung-Fu Wu; Kuen-Haur Lee; Chih-Yang Wang
Journal:  Curr Issues Mol Biol       Date:  2021-04-27       Impact factor: 2.976

5.  An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.

Authors:  Fangyi Xu; Kaihua Lou; Chao Chen; Qingqing Chen; Dawei Wang; Jiangfen Wu; Wenchao Zhu; Weixiong Tan; Yong Zhou; Yongjiu Liu; Bing Wang; Xiaoguo Zhang; Zhongfa Zhang; Jianjun Zhang; Mingxia Sun; Guohua Zhang; Guojiao Dai; Hongjie Hu
Journal:  Med Phys       Date:  2022-04-18       Impact factor: 4.506

6.  Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.

Authors:  Manish Kumar Tripathi; Abhigyan Nath; Tej P Singh; A S Ethayathulla; Punit Kaur
Journal:  Mol Divers       Date:  2021-06-23       Impact factor: 3.364

7.  Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models.

Authors:  Alexandros Laios; Alexandros Gryparis; Diederick DeJong; Richard Hutson; Georgios Theophilou; Chris Leach
Journal:  J Ovarian Res       Date:  2020-09-29       Impact factor: 4.234

  7 in total

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