Literature DB >> 31071378

The significance of artificial intelligence in drug delivery system design.

Parichehr Hassanzadeh1, Fatemeh Atyabi2, Rassoul Dinarvand3.   

Abstract

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Artificial neural networks; Drug delivery systems; Target fishing

Mesh:

Year:  2019        PMID: 31071378     DOI: 10.1016/j.addr.2019.05.001

Source DB:  PubMed          Journal:  Adv Drug Deliv Rev        ISSN: 0169-409X            Impact factor:   15.470


  13 in total

Review 1.  Intelligent automated drug administration and therapy: future of healthcare.

Authors:  Richa Sharma; Dhirendra Singh; Prerna Gaur; Deepak Joshi
Journal:  Drug Deliv Transl Res       Date:  2021-01-14       Impact factor: 4.617

Review 2.  Lipid-Based Nanocarriers for Ophthalmic Administration: Towards Experimental Design Implementation.

Authors:  Felipe M González-Fernández; Annalisa Bianchera; Paolo Gasco; Sara Nicoli; Silvia Pescina
Journal:  Pharmaceutics       Date:  2021-03-26       Impact factor: 6.321

Review 3.  Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Authors:  Rohan Gupta; Devesh Srivastava; Mehar Sahu; Swati Tiwari; Rashmi K Ambasta; Pravir Kumar
Journal:  Mol Divers       Date:  2021-04-12       Impact factor: 3.364

Review 4.  Biomaterial-based immunoengineering to fight COVID-19 and infectious diseases.

Authors:  Jana Zarubova; Xuexiang Zhang; Tyler Hoffman; Mohammad Mahdi Hasani-Sadrabadi; Song Li
Journal:  Matter       Date:  2021-03-09

Review 5.  The significance of bioengineered nanoplatforms against SARS-CoV-2: From detection to genome editing.

Authors:  Parichehr Hassanzadeh
Journal:  Life Sci       Date:  2021-03-04       Impact factor: 6.780

6.  Artificial intelligence and guidance of medicine in the bubble.

Authors:  Asma Akbar; Nagavalli Pillalamarri; Sriya Jonnakuti; Mujib Ullah
Journal:  Cell Biosci       Date:  2021-06-09       Impact factor: 7.133

7.  Digital electronics in fibres enable fabric-based machine-learning inference.

Authors:  Gabriel Loke; Tural Khudiyev; Brian Wang; Stephanie Fu; Syamantak Payra; Yorai Shaoul; Johnny Fung; Ioannis Chatziveroglou; Pin-Wen Chou; Itamar Chinn; Wei Yan; Anna Gitelson-Kahn; John Joannopoulos; Yoel Fink
Journal:  Nat Commun       Date:  2021-06-03       Impact factor: 14.919

Review 8.  Enhancing Clinical Translation of Cancer Using Nanoinformatics.

Authors:  Madjid Soltani; Farshad Moradi Kashkooli; Mohammad Souri; Samaneh Zare Harofte; Tina Harati; Atefeh Khadem; Mohammad Haeri Pour; Kaamran Raahemifar
Journal:  Cancers (Basel)       Date:  2021-05-19       Impact factor: 6.639

9.  Inhibition Ability of Natural Compounds on Receptor-Binding Domain of SARS-CoV2: An In Silico Approach.

Authors:  Miroslava Nedyalkova; Mahdi Vasighi; Subrahmanyam Sappati; Anmol Kumar; Sergio Madurga; Vasil Simeonov
Journal:  Pharmaceuticals (Basel)       Date:  2021-12-18

Review 10.  State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation.

Authors:  Shan Wang; Jinwei Di; Dan Wang; Xudong Dai; Yabing Hua; Xiang Gao; Aiping Zheng; Jing Gao
Journal:  Pharmaceutics       Date:  2022-01-13       Impact factor: 6.321

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