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	<title>SynGFN Methodology &#8211; BIOENGINEER.ORG</title>
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		<title>Exploring Chemical Space with Generative Flow Models</title>
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		<pubDate>Thu, 13 Nov 2025 14:49:25 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Artificial Intelligence in Molecular Discovery]]></category>
		<category><![CDATA[Chemical Space Exploration]]></category>
		<category><![CDATA[drug discovery optimization]]></category>
		<category><![CDATA[Generative Flow Models]]></category>
		<category><![CDATA[SynGFN Methodology]]></category>
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					<description><![CDATA[In a groundbreaking development for the field of molecular discovery, researchers have unveiled a novel approach known as SynGFN, a system designed to enhance the conventional design-make-test-analyze cycle through the power of artificial intelligence. This new methodology highlights the necessity of removing the compartmentalized framework that historically characterized computer-aided molecular design and synthesis. By addressing [&#8230;]]]></description>
		
		
		
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