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Curated Papers > In recent years, research on "AI drug discovery" published in high-impact journals (IF ≥ 30, past five years)

Artificial intelligence in cancer target identification and drug discovery. ​

Literature Information ​

DOI10.1038/s41392-022-00994-0
PMID35538061
JournalSignal transduction and targeted therapy
Impact Factor52.7
JCR QuartileQ1
Publication Year2022
Times Cited131
KeywordsArtificial Intelligence, Cancer Target Identification, Drug Discovery
Literature TypeJournal Article, Review
ISSN2059-3635
Pages156
Issue7(1)
AuthorsYujie You, Xin Lai, Yi Pan, Huiru Zheng, Julio Vera, Suran Liu, Senyi Deng, Le Zhang

TL;DR ​

This review highlights the use of artificial intelligence in identifying novel anticancer targets and discovering new drugs through biological networks that reflect cellular interactions related to cancer. By employing network-based and machine learning algorithms, the study provides a quantitative framework that enhances our understanding of cancer mechanisms and facilitates the identification of potential therapeutic targets and drug candidates.

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Artificial Intelligence · Cancer Target Identification · Drug Discovery

Abstract ​

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

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Primary Questions Addressed ​

  1. What are the specific machine learning algorithms that have shown the most promise in identifying anticancer targets?
  2. How do biological networks enhance the accuracy of artificial intelligence in drug discovery compared to traditional methods?
  3. What are the limitations of current artificial intelligence approaches in cancer target identification, and how can they be addressed?
  4. In what ways can artificial intelligence contribute to personalized medicine in cancer treatment through target identification?
  5. How does the integration of multi-omics data with artificial intelligence improve the drug discovery process for cancer therapies?

Key Findings ​

Research Background and Purpose ​

The study discusses the application of artificial intelligence (AI) in identifying novel anticancer targets and discovering drugs. Targeted drug therapies have shown efficacy, but challenges such as limited druggable targets and drug resistance necessitate the exploration of new therapeutic targets. AI offers a promising approach to analyze complex biological networks, aiding in the identification of potential drug targets.

Main Methods/Materials/Experimental Design ​

The authors categorize AI methods into two main approaches: network-based biology analysis algorithms and machine learning (ML)-based algorithms. The following flowchart summarizes the methodologies:

Mermaid diagram
  • Network-based Biology Analysis: Utilizes biological networks to identify cancer targets by exploring interactions among genes, proteins, and other molecules.
  • Machine Learning-based Analysis: Employs algorithms to learn from data, focusing on feature extraction and prediction tasks in biological networks.

Key Results and Findings ​

  1. AI Applications: AI models have been successfully applied to integrate multi-omics data (genomics, proteomics, metabolomics) to enhance target identification and drug discovery.
  2. Algorithm Effectiveness: Network-based algorithms provide diverse perspectives on data, while ML algorithms excel in handling high-dimensional data and feature learning.
  3. Target Identification: Studies highlighted the successful identification of novel therapeutic targets using both AI approaches, with examples including the discovery of key genes in various cancers.

Main Conclusions/Significance/Innovation ​

The integration of AI in cancer research significantly enhances the understanding of complex biological systems and facilitates the identification of novel anticancer targets. The systematic review emphasizes the importance of both network-based and ML-based approaches, suggesting that future research should focus on improving the accuracy and interpretability of AI models.

Research Limitations and Future Directions ​

  1. Data Consistency: The lack of consistent and high-quality data for validation poses challenges for AI model reliability.
  2. Heterogeneous Data Integration: Combining diverse data types remains a complex task that requires advanced methods.
  3. Model Interpretability: Ensuring that AI models are interpretable is crucial for clinical applications. Future research should focus on enhancing model transparency and validation across various datasets.

In summary, while AI shows great promise in advancing cancer therapy, ongoing efforts are needed to address current limitations and optimize these technologies for practical applications in drug discovery and target identification.

References ​

  1. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. - Tongxin Wang;Wei Shao;Zhi Huang;Haixu Tang;Jie Zhang;Zhengming Ding;Kun Huang - Nature communications (2021)
  2. Maximum entropy methods for extracting the learned features of deep neural networks. - Alex Finnegan;Jun S Song - PLoS computational biology (2017)
  3. Multi-omics approaches to disease. - Yehudit Hasin;Marcus Seldin;Aldons Lusis - Genome biology (2017)
  4. Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics. - Nikolaos Perakakis;Alireza Yazdani;George E Karniadakis;Christos Mantzoros - Metabolism: clinical and experimental (2018)
  5. PREDICT: a method for inferring novel drug indications with application to personalized medicine. - Assaf Gottlieb;Gideon Y Stein;Eytan Ruppin;Roded Sharan - Molecular systems biology (2011)
  6. A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes. - Feixiong Cheng;Junfei Zhao;Michaela Fooksa;Zhongming Zhao - Journal of the American Medical Informatics Association : JAMIA (2016)
  7. Identifying novel genes and chemicals related to nasopharyngeal cancer in a heterogeneous network. - Zhandong Li;Lifeng An;Hao Li;ShaoPeng Wang;You Zhou;Fei Yuan;Lin Li - Scientific reports (2016)
  8. Identification of key regulators in prostate cancer from gene expression datasets of patients. - Irengbam Rocky Mangangcha;Md Zubbair Malik;Ömer Küçük;Shakir Ali;R K Brojen Singh - Scientific reports (2019)
  9. Bioinformatic analysis of chromatin organization and biased expression of duplicated genes between two poplars with a common whole-genome duplication. - Le Zhang;Jingtian Zhao;Hao Bi;Xiangyu Yang;Zhiyang Zhang;Yutao Su;Zhenghao Li;Lei Zhang;Brian J Sanderson;Jianquan Liu;Tao Ma - Horticulture research (2021)
  10. A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. - Susan Dina Ghiassian;Jörg Menche;Albert-László Barabási - PLoS computational biology (2015)

Literatures Citing This Work ​

  1. Applications of artificial intelligence multiomics in precision oncology. - Ruby Srivastava - Journal of cancer research and clinical oncology (2023)
  2. An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection. - Fubo Ma;Ming Xiao;Lin Zhu;Wen Jiang;Jizhe Jiang;Peng-Fei Zhang;Kang Li;Min Yue;Le Zhang - Frontiers in genetics (2022)
  3. Integration of artificial intelligence and precision oncology in Latin America. - Liliana Sussman;Juan Esteban Garcia-Robledo;Camila Ordóñez-Reyes;Yency Forero;Andrés F Mosquera;Alejandro Ruíz-Patiño;Diego F Chamorro;Andrés F Cardona - Frontiers in medical technology (2022)
  4. A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer. - Le Zhang;Shiwei Fan;Julio Vera;Xin Lai - Computational and structural biotechnology journal (2023)
  5. Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda. - Erik Karger;Marko Kureljusic - Pharmaceuticals (Basel, Switzerland) (2022)
  6. A Computer Simulation of SARS-CoV-2 Mutation Spectra for Empirical Data Characterization and Analysis. - Ming Xiao;Fubo Ma;Jun Yu;Jianghang Xie;Qiaozhen Zhang;Peng Liu;Fei Yu;Yuming Jiang;Le Zhang - Biomolecules (2022)
  7. Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis. - Le Zhang;Jiayidaer Badai;Guan Wang;Xufang Ru;Wenkai Song;Yujie You;Jiaojiao He;Suna Huang;Hua Feng;Runsheng Chen;Yi Zhao;Yujie Chen - Frontiers in immunology (2023)
  8. Artificial intelligence for drug discovery: Resources, methods, and applications. - Wei Chen;Xuesong Liu;Sanyin Zhang;Shilin Chen - Molecular therapy. Nucleic acids (2023)
  9. Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers. - Bernardo Pereira Cabral;Luiza Amara Maciel Braga;Shabbir Syed-Abdul;Fabio Batista Mota - Current oncology (Toronto, Ont.) (2023)
  10. Editorial: Medical knowledge-assisted machine learning technologies in individualized medicine. - Feng Gao;William C Cho;Xin Gao;Wei Wang - Frontiers in molecular biosciences (2023)

... (121 more literatures)

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