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				<full_title>Journal of Applied Pharmaceutical Technologies and Systems</full_title>
				<abbrev_title>J Appl Pharm Technol Syst</abbrev_title>
				<issn>3149-9589</issn>
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					<year>2026</year>
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					<volume>5</volume>
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					<title>Explainable AI-Generated Formulation Designs for Regulatory and Scientific Decision-Making</title>
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            <given_name>George</given_name>
            <surname>Papadopoulos</surname>
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            <given_name>Eleni</given_name>
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					  <unstructured_citation>Ros H, Abdalla Y, Cook MT, Shorthouse D. Efficient discovery of new medicine formulations using a semi-self-driven robotic formulator. Digital Discovery. 2025;4(8):2263-72.</unstructured_citation>
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					  <unstructured_citation>Bao Z, Bufton J, Hickman RJ, Aspuru-Guzik A, Bannigan P, Allen C. Revolutionizing drug formulation development: the increasing impact of machine learning. Adv Drug Deliv Rev. 2023;202:115108.</unstructured_citation>
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