Publications.

Herophilus Orchestra: the best Drug Discovery Operating System you’ve never heard of

Neuroimmune cortical organoids overexpressing C4A exhibit multiple schizophrenia endophenotypes

Herophilus developed the first complex in vitro model of C4A induced schizophrenia. Read about it in our bioRxiv preprint.

Genomics… and AI… then what?

Every data/AI-driven drug discovery company must eventually do real-world biology experiments. A new class of human in vitro experimental models is ripe to serve this need.

Data science, human intelligence, and therapeutics discovery: An interview with Sean Escola, Saul Kato, and Pavan Ramkumar

Herophilus leaders explain the importance of data science in their research, exploring a score that identifies hierarchical confounder effects in raw data and machine learning-derived data embeddings.

Getting real about curing polygenic brain disease.

Lone single-target approaches, are doomed to fail in the quest to cure polygenic neurological diseases such as Alzheimer’s, schizophrenia, Parkinson’s, and autisms — ironically, common neurological diseases of high prevalence. A new approach is needed.

RTG: New methods for machine-learning confounder analysis.

Explore how Herophilus approaches hierarchical confounder discovery in the experiment–machine learning cycle in recent paper in Cell Patterns.

Demuxalot: scaled up genetic demultiplexing for single-cell sequencing.

Herophilus is working on new methods in transcriptomic analysis. Read about our bioRxiv preprint and open source code release of Demuxalot.

Scaling brain organoids for disease phenotyping.

Learn how Herophilus optimizes the scaling of patient-derived brain organoids to uncover deep phenotypes of disease in our bioRxiv preprint.