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Qi X, Zhao Y, Qi Z, Hou S, Chen J. Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges. Molecules 2024; 29:903. [PMID: 38398653 PMCID: PMC10892089 DOI: 10.3390/molecules29040903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.
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Affiliation(s)
- Xin Qi
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Yuanchun Zhao
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Zhuang Qi
- School of Software, Shandong University, Jinan 250101, China;
| | - Siyu Hou
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
| | - Jiajia Chen
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou 215011, China; (Y.Z.); (S.H.); (J.C.)
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Abou Hajal A, Al Meslamani AZ. Insights into artificial intelligence utilisation in drug discovery. J Med Econ 2024; 27:304-308. [PMID: 38385328 DOI: 10.1080/13696998.2024.2315864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 02/05/2024] [Indexed: 02/23/2024]
Affiliation(s)
- Abdallah Abou Hajal
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
| | - Ahmad Z Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
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Angelo JS, Guedes IA, Barbosa HJC, Dardenne LE. Multi-and many-objective optimization: present and future in de novo drug design. Front Chem 2023; 11:1288626. [PMID: 38192501 PMCID: PMC10773868 DOI: 10.3389/fchem.2023.1288626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024] Open
Abstract
de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.
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Affiliation(s)
| | | | | | - Laurent E. Dardenne
- Coordenação de Modelagem Computacional, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
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Boatner LM, Palafox MF, Schweppe DK, Backus KM. CysDB: a human cysteine database based on experimental quantitative chemoproteomics. Cell Chem Biol 2023; 30:683-698.e3. [PMID: 37119813 PMCID: PMC10510411 DOI: 10.1016/j.chembiol.2023.04.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 02/02/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023]
Abstract
Cysteine chemoproteomics provides proteome-wide portraits of the ligandability or potential "druggability" for thousands of cysteine residues. Consequently, these studies are facilitating resources for closing the druggability gap, namely, achieving pharmacological manipulation of ∼96% of the human proteome that remains untargeted by U.S. Food and Drug Administration (FDA) approved small molecules. Recent interactive datasets have enabled users to interface more readily with cysteine chemoproteomics datasets. However, these resources remain limited to single studies and therefore do not provide a mechanism to perform cross-study analyses. Here we report CysDB as a curated community-wide repository of human cysteine chemoproteomics data derived from nine high-coverage studies. CysDB is publicly available at https://backuslab.shinyapps.io/cysdb/ and features measures of identification for 62,888 cysteines (24% of the cysteinome), as well as annotations of functionality, druggability, disease relevance, genetic variation, and structural features. Most importantly, we have designed CysDB to incorporate new datasets to further support the continued growth of the druggable cysteinome.
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Affiliation(s)
- Lisa M Boatner
- Biological Chemistry Department, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Maria F Palafox
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Devin K Schweppe
- Department of Genome Sciences, University of Washington, Seattle, WA 98185, USA
| | - Keriann M Backus
- Biological Chemistry Department, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; DOE Institute for Genomics and Proteomics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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