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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Current Computer-Aided Drug Design</journal-id><journal-title-group><journal-title xml:lang="en">Current Computer-Aided Drug Design</journal-title><trans-title-group xml:lang="ru"><trans-title>Current Computer-Aided Drug Design</trans-title></trans-title-group></journal-title-group><issn publication-format="print">1573-4099</issn><issn publication-format="electronic">1875-6697</issn><publisher><publisher-name xml:lang="en">Bentham Science</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">644312</article-id><article-id pub-id-type="doi">10.2174/0115734099260187230921073932</article-id><article-categories><subj-group subj-group-type="toc-heading"><subject>Chemistry</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Singh</surname><given-names>Smita</given-names></name><email>info@benthamscience.net</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name><surname>Singh</surname><given-names>Pranjal</given-names></name><email>info@benthamscience.net</email><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><name><surname>Sachan</surname><given-names>Kapil</given-names></name><email>info@benthamscience.net</email><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><name><surname>Kumar</surname><given-names>Mukesh</given-names></name><email>info@benthamscience.net</email><xref ref-type="aff" rid="aff4"/></contrib><contrib contrib-type="author"><name><surname>Bhardwaj</surname><given-names>Poonam</given-names></name><email>info@benthamscience.net</email><xref ref-type="aff" rid="aff5"/></contrib></contrib-group><aff id="aff1"><institution>Department of Pharmaceutics, SRM Modinagar College of Pharmacy</institution></aff><aff id="aff2"><institution>Department of Pharmaceutics, SRM Modinagar College of Pharmacy, SRM Institute of Science and Technology</institution></aff><aff id="aff3"><institution>KIET Group of Institutions,, KIET School of Pharmacy,</institution></aff><aff id="aff4"><institution>IIMT College of Medical Sciences, IIMT University,</institution></aff><aff id="aff5"><institution>, NKBR College of Pharmacy and Research Center</institution></aff><pub-date date-type="pub" iso-8601-date="2024-06-01" publication-format="electronic"><day>01</day><month>06</month><year>2024</year></pub-date><volume>20</volume><issue>6</issue><issue-title xml:lang="ru"/><fpage>723</fpage><lpage>735</lpage><history><date date-type="received" iso-8601-date="2025-01-07"><day>07</day><month>01</month><year>2025</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2024, Bentham Science Publishers</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Bentham Science Publishers</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/></permissions><self-uri xlink:href="https://rjeid.com/1573-4099/article/view/644312">https://rjeid.com/1573-4099/article/view/644312</self-uri><abstract xml:lang="en"><p id="idm46041443626720">:The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of \"force fields\" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.</p></abstract><kwd-group xml:lang="en"><kwd>Toxicity</kwd><kwd>bioinformatics</kwd><kwd>experimental modeling</kwd><kwd>drug discovery</kwd><kwd>molecule discovery</kwd><kwd>ADME-Tox.</kwd></kwd-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Shaker, B.; Ahmad, S.; Lee, J.; Jung, C.; Na, D. In silico methods and tools for drug discovery. Comput. Biol. 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