Use enhancer RNAs + AI to accelerate your drug discovery program and deliver better solutions for patients.

  • Predict patient response to a drug
  • Identify drug off-target effects earlier
  • Discover new homes for existing drugs

Arpeggio is the first to use enhancer RNAs and machine learning to predict the effects of a drug.

About Us

The drug discovery industry is currently experiencing the beginning of a shift away from traditional pharmacology towards AI-driven inferences. Our drug-screening platform combines two novel technologies: a custom laboratory assay that fingerprints a cell’s epigenome, and proprietary machine learning algorithms that match drugs to diseases based on their epigenetic fingerprints. We screen drugs with unrivaled precision and nominate clinically-relevant compounds faster and more effectively.

Our Science

The biology of our cells is a web of connections, and it is unfortunate that most drug discovery efforts start by targeting a single protein, gene, or metabolite in isolation. At Arpeggio, we understand that therapeutics should be designed around changes in the biological network as a whole.

Where existing biomarker selection strategies nominate thousands of potential markers, ours nominates fewer than ten.

Existing biomarker selection strategies routinely nominate thousands of downstream effects, making it difficult to select the right one. This shortcoming is due to the fact that traditional assays measure aggregate, long-term changes, rather than immediate impacts following treatment. Arpeggio’s science is based on a biological assay that measures immediate changes to the epigenome, the dynamic part of our DNA. Coupled with our proprietary machine learning algorithms, our technology enables accurate predictions about how a drug will affect the biological networks inside our cells.


AI-driven drug discovery demands a diverse skillset, spanning expertise in molecular biology, pharmacology, and machine learning. Arpeggio and its founders directly reflects this diversity.

  • Joey Azofeifa — is an expert in AI and machine learning
  • Tim Read — is a classically-trained molecular biologist, specializing in assay development
  • Robin Dowell — is a tenured professor and leading researcher in bioinformatics, transcriptional regulation
  • Daniel Weaver — is an industry leader in drug discovery with over 25 years of experience


Arpeggio enables our partners to select drug biomarkers, which is critical for patient selection in the clinic.


“We have been extremely impressed with the professionalism and thoroughness
of the Arpeggio team. Results from our collaboration support the validation of pharmacodynamic biomarkers and associated clinical development strategy.”

-FORMA Therapeutics