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  <doi_batch_id>aspg-41-3485-1792222686</doi_batch_id>
  <timestamp>20261017073806</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Metaheuristic Optimization Review</full_title>
    <abbrev_title>MOR</abbrev_title>
    <issn media_type="electronic">3066-280X</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>3</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Artificial Intelligence in Drug Discovery: A Review of AI Approaches for Target Identification</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Faustino D.</given_name>
      <surname>Reyes</surname>
      <affiliations>
       <institution>
        <institution_name>ICT Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Artificial Intelligence (AI) has become a revolutionary solution in drug discovery and development in aspects including high costs, long times, and high failure rates. This review describes the development and focuses on areas where AI has been used for target identification, lead optimization, design of new drugs from scratch and drug repurposing. Deep learning frameworks such as generative adversarial networks (GANs), variational autoencoders (VAEs), and explainable AI (XAI) approaches have been instrumental and comparative progress in enhancing the efficacy and specificity of drug discovery processes. AI has made advances in clinical trials, trial conduct, and participant selection, as well as enhanced patient-tailored therapies for personalized medicine. Issues such as data credibility, model explainability, and algorithmic biases are still present, and logical and social sciences' cooperation and code of conduct are needed. As such, this review aligns current developments with these challenges to demonstrate the possibilities of AI in revolutionizing pharma research and enhancing health solutions worldwide.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>05</first_page>
     <last_page>09</last_page>
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     <item_number item_number_type="article-number">3485</item_number>
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     <doi>10.54216/MOR.030102</doi>
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