In-house Experiments in AI Drug Development
David Shaywitz suggests that in-house experiments could help drug developers select new targets as AI becomes widely available, though a competitive advantage remains unproven in the supplied material.
In-house experiments could offer an edge in AI drug development
Daphne Zohar, co-founder of biotech company Karuna Therapeutics, asked what advantage a drug developer gains when artificial intelligence models are widely available to competitors and change rapidly. In March 2024, pharmaceutical company Bristol Myers Squibb acquired all Karuna shares for about $14 billion. Zohar expects AI to have a large impact on the industry, but asks whether it will give individual companies an advantage or help everyone in the market.
Biotech investor Bruce Booth predicts that faster molecule design will intensify competition around known drug targets over the next three to five years. These include proteins that a medicine is intended to act on. Many models train on data about known molecules and biological mechanisms, making it easier for more teams to develop drugs against the same targets. In Booth's forecast, companies compete for contractors, trial sites, and eligible patients. Trial recruitment slows and financial returns fall: many teams incur costs, while only some drugs demonstrate an advantage over existing treatments.
Physician-scientist David Shaywitz proposes seeking an advantage in understanding the causes of disease. A company can compare disease manifestations with results from its own experiments and test which interventions change disease-related processes. He suggests that the resulting data could help companies select new drug targets with greater confidence.
Shaywitz cites drug developer insitro as an example. According to the company's description, human genetic and health data help researchers select cells for experiments and decide what to measure. For example, researchers compare fat accumulation in the human liver with fat accumulation in cultured liver cells. By altering gene activity in these cells, they test how each gene affects fat accumulation. AI compares the results with human genetic and health data to propose a drug target. The experimental data then feed back into the models to help select subsequent experiments.
This approach provides a basis for target selection: researchers test how changing a gene's activity affects a disease-related process. However, the supplied material describes insitro's approach, while its competitive advantage remains Shaywitz's hypothesis. Whether a resulting drug helps patients still requires testing in humans.
Open the related Eternal Search page
Sources
[1] x.com
[2] investegate.co.uk
[3] lifescivc.com
[4] x.com
[5] t.me
[6] deepphenotype.substack.com
[7] x.com
The item connects a current debate about competitive advantage in AI drug discovery with Bristol Myers Squibb through Daphne Zohar, whose company Karuna Therapeutics it acquired in March 2024; the linked Eternal Search page gives readers a concrete organization to inspect without attributing the discussed experimental strategy to that company.