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	<title>topological and chemical bond encoding &#8211; Science</title>
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	<title>topological and chemical bond encoding &#8211; Science</title>
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		<title>SADRP: A structurally aware multi-modal framework for drug response prediction</title>
		<link>https://scienmag.com/sadrp-a-structurally-aware-multi-modal-framework-for-drug-response-prediction/</link>
		
		<dc:creator><![CDATA[Juliet Wilcox]]></dc:creator>
		<pubDate>Thu, 22 Oct 2026 00:09:42 +0000</pubDate>
				<category><![CDATA[Biology]]></category>
		<category><![CDATA[Technology and Engineering]]></category>
		<category><![CDATA[AWARE]]></category>
		<category><![CDATA[Biomarkers]]></category>
		<category><![CDATA[cancer cell line molecular profiling]]></category>
		<category><![CDATA[cancer cell lines]]></category>
		<category><![CDATA[computational drug discovery]]></category>
		<category><![CDATA[cross-omics data fusion]]></category>
		<category><![CDATA[DNA Methylation]]></category>
		<category><![CDATA[drug discovery]]></category>
		<category><![CDATA[drug response prediction]]></category>
		<category><![CDATA[drug sensitivity prediction models]]></category>
		<category><![CDATA[gene expression]]></category>
		<category><![CDATA[gene expression and DNA methylation integration]]></category>
		<category><![CDATA[mechanistic insights in drug response]]></category>
		<category><![CDATA[molecular structure]]></category>
		<category><![CDATA[multi-modal cancer cell analysis]]></category>
		<category><![CDATA[multi-omics]]></category>
		<category><![CDATA[multi-view interactive encoder]]></category>
		<category><![CDATA[personalized cancer therapy biomarkers]]></category>
		<category><![CDATA[Personalized Medicine]]></category>
		<category><![CDATA[SADRP]]></category>
		<category><![CDATA[structural drug encoding]]></category>
		<category><![CDATA[Structurally]]></category>
		<category><![CDATA[topological and chemical bond encoding]]></category>
		<guid isPermaLink="false">https://scienmag.com/?p=331183</guid>

					<description><![CDATA[SADRP combines drug topology, chemical-bond information, gene expression and DNA methylation to predict drug sensitivity across cancer cell lines.]]></description>
										<content:encoded><![CDATA[<p>A structurally aware multi-modal framework called SADRP consistently outperformed state-of-the-art computational methods for predicting drug sensitivity across cancer cell lines, according to the research summary. The framework combines molecular features of drug compounds with two cellular data types—gene expression and DNA methylation—to model the drug and cancer-cell context within a unified system.</p>
<p>Drug response prediction requires characterizing both the compound being tested and the cellular sample in which it is evaluated. SADRP addresses these two components through separate representations. For each drug, it uses Shortest Distance Path encoding to represent topological relationships and edge encoding to capture the chemical semantics associated with bond types. These structural encodings form the drug side of the framework’s multi-modal design.</p>
<p>The cancer-cell representation is produced by a multi-view interactive encoder. It integrates gene-expression and DNA-methylation profiles from the same cell-line sample, allowing the model to capture cross-omics complementarity between the two molecular layers. SADRP therefore predicts sensitivity using both structural information about a drug and a multi-omic profile of the cancer cell line.</p>
<p>The researchers report that SADRP also identified potential therapeutic candidates and biomarkers, providing information intended to support mechanistic insights and applications in drug discovery and personalized medicine. The supplied evidence does not specify the benchmark size, individual performance measurements, or results from prospective laboratory or clinical validation. Accordingly, the reported results concern computational drug-response prediction in cancer cell lines and do not establish patient outcomes or clinical effectiveness. The work is a modeling framework for evaluating drug sensitivity, generating potential therapeutic candidates and biomarkers, and supporting further investigation in drug discovery and personalized medicine.</p>
<p><strong>Subject of Research:</strong> Multi-modal computational prediction of drug sensitivity in cancer cell lines</p>
<p><strong>Article Title:</strong> SADRP: A structurally aware multi-modal framework for drug response prediction</p>
<p><strong>Article References:</strong> Qi, R., Liu, S., Quan, T., Wu, T., Shaytan, A. K., &amp; Liu, B. (2026). SADRP: A structurally aware multi-modal framework for drug response prediction. <em>PLOS Computational Biology, 22</em>(10), e1014861. <a href="https://doi.org/10.1371/journal.pcbi.1014861" rel="noopener noreferrer">https://doi.org/10.1371/journal.pcbi.1014861</a></p>
<p><strong>Image Credits:</strong> AI Generated</p>
<p><strong>DOI:</strong> <a href="https://doi.org/10.1371/journal.pcbi.1014861" rel="noopener noreferrer">10.1371/journal.pcbi.1014861</a></p>
<p><strong>Keywords:</strong> SADRP, drug response prediction, cancer cell lines, multi-omics, gene expression, DNA methylation, molecular structure, drug discovery, personalized medicine, biomarkers, structurally, aware</p>
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