High-throughput transcriptomics and AI for drug discovery

Speaking of Mol Bio

Doing something complex and meaningful in a new way requires thinking and acting a bit differently. This is the case with how Dr. Joey Azofeifa, from Arpeggio Bio, is using systems biology to discover new drug candidates.

Join us in this Season 2 kickoff episode where we dive headlong into transcriptomics, systems biology, machine learning, and learn how they’re being used to innovate drug discovery. We learn about 3’-end mRNA barcoding and in-cell reverse transcription methods that allow the pooling of up to 1,536 samples so that only a single library preparation is required while still allowing the deconvolution of RNAseq results. This reduces their RNAseq costs by up to 400-fold, which enables them to generate enormous transcriptomic data sets. We also learn about how they’re using generative adversarial AI networks to use this transcriptomics data to design potential drug candidates. We even hear how one of their drug candidates, which targets iron homeostasis pathways, has progress to successful testing in mice.

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Speaking of Mol Bio Podcast | Thermo Fisher Scientific - US

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