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The AI-powered tool scans through public biomedical literature databases to identify and rank descriptions of gene-gene and drug-gene regulatory relationships and predict undiscovered relationships.
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Novel AI-Based Predictive Tool May Accelerate Drug Discovery

The tool “learns” from existing gene-gene or gene-drug interactions and predicts novel relationships

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Cole Deisseroth, a graduate student enrolled in the MD/PhD program—mentored by Huda Zoghbi, MD, and Zhandong Liu, PhD, at the Jan and Duncan Neurological Research Institute (Duncan NRI) at Texas Children’s Hospital and Baylor College of Medicine—led a study to generate a natural language processing (NLP) tool called PARsing ModifiErS via Article aNnotations (PARMESAN). This new tool can search for up-to-date information, assemble it into a central knowledge base, and even predict likely drugs that could correct specific protein imbalances to aid the discovery of new treatments for genetic disorders. A description of the tool and its capabilities was published recently in the American Journal of Human Genetics.

“PARMESAN offers a wonderful opportunity for scientists to speed up the pace of their research and, thus, accelerate drug discovery and development,” added Howard Hughes Medical Institute investigator Zoghbi, who is also the founding director of Duncan NRI and distinguished service professor at Baylor College.

This AI-powered tool scans through public biomedical literature databases, like PubMed and PubMed Central, to identify and rank descriptions of gene-gene and drug-gene regulatory relationships. However, what stands out about PARMESAN is its ability to leverage curated information to predict undiscovered relationships.

“The unique feature of PARMESAN is that it not only identifies existing gene-gene or drug-gene interactions based on the available literature but also predicts putative novel drug-gene relationships by assigning an evidence-based score to each prediction,” noted Liu, chief of Computation Sciences at Texas Children’s Hospital and associate professor at Baylor College of Medicine.

PARMESAN’s AI algorithms analyze studies that describe the contributions of various players involved in a multistep genetic pathway. Then it assigns a weighted numerical score to each reported interaction. Interactions that are consistently and frequently reported in the literature receive higher scores, whereas interactions that are either weakly supported or appear to be contradicted between different studies are assigned lower scores.

PARMESAN currently provides predictions for more than 18,000 target genes, and benchmarking studies have suggested that the highest-scoring predictions are over 95 percent accurate.

"By pinpointing the most promising gene and drug interactions, this tool will allow researchers to identify the most promising drugs at a faster rate and with greater accuracy," said Deisseroth.

- This press release was originally published on the Texas Children’s Duncan NRI website