Unnatural Selection: AI Meets Synthetic Biology
Making Insulin in the Lab Originally sourced from pig pancreases, the synthesis of insulin in 1979 by Genentech marked a major scientific…
Analog Intelligence
AI Strategy in Bio and Health
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Guidelines for AI Research in Medicine
Medical Research and Guidelines Medical research in the 70’s and 80’s suffered significantly from poor, or at best mediocre, methodological…
AI Beyond the Clinic: Labs, Telemedicine, and Consumer Healthcare
Unstructured Data Silos & Protocol Deviations Healthcare systems are large and complex structures, both in terms of sheer number of…
AI Startups in Pathology: A Meta-Review
Patient Story An abnormal finding is detected on a mammogram during routine annual screening. To investigate further, the physician orders a…
Pre-2012 Startups and AI Products Across Medical Specialities
A Tale of Two Startups First, we have the acquirer IDx with diagnostic products for diabetic retinopathy - a diabetes complication that…
AI clinical deployment contexts in radiology
Radiology ML models can be deployed in one of three clinical contexts: within the image acquisition hardware, within the data storage system…
Biotech: Navigating the double funnel
It is no surprise that developing a drug is estimated to cost $2.6B spanning an entire decade. With success rates in the single digits for…
Four models for delivering AI value in drug development
Drug discovery and development is a multi stakeholder process. For early stage AI startups, it is imperative that they work with relatively…
Machine learning is just one part of the story
While many biotech companies may identify as “AI-first”, machine learning is often one of three components that come together to create…
Flagship pioneering: Four forces shaping biotech
Four main forces shaping biotech as outlined in Flagship pioneering`s 2022 annual letter↗︎. For context, flagship pioneering is a life…
ML-readiness across 3 data sources: Preclinical research, clinical research, and clinical practice
The state of being ML-ready can vary widely across different data types, primarily influenced by their source. The distinction here is…
The promises of AI in small molecule drug discovery
An immediately obvious application is repurposing existing molecules for new indications. This is particularly evident in phenotypic drug…
Recursion: A biotech company scaling more like a tech company
Only a handful of ML-centric companies in the health/bio space have gone public. The regulatory documents from these filings provide a good…
The triangular spectrum of research data teams
Data teams in preclinical research operate within a triangular spectrum spanning three core areas: infrastructure, machine learning, and…
Novel proteins across sequence, structure, and function
The new generation of antibodies, enzymes, peptides and other proteins will be designed and engineered - not discovered or screened. ML is…