AI Can Accelerate Discovery — Development Still Decides What Advances
AI will continue changing how small molecules are discovered, but the candidates that generate the most interest in silico still have to succeed under real development conditions.
AI will continue changing how small molecules are discovered, but the candidates that generate the most interest in silico still have to succeed under real development conditions.
AI can speed up drug discovery and decrease attrition rates in the clinic, but it is important to recognize that both are tall orders. The companies that will benefit most are those that stay grounded, set realistic expectations, and keep experienced scientists at the center of decisions that require genuine creativity and judgment.
More therapeutic options times more data per option times the same number of clinical hours equals something that breaks. The practices that will thrive when the next wave of longevity therapeutics arrives are the ones that have already solved this.
Brian Alexander feels the current system of drug development is shaped by biotech investor expectations. Valo Health is eschewing that path.
Elli Lilly has been in the news lately because of the announced $1 billion investment in a joint innovation lab with Nvidia, an AI powerhouse player. But AI drug discovery is only part of the story of where AI can be leveraged in biopharma.
At JPM, Nvidia unveiled partnerships with Eli Lilly, Thermo Fisher and others that show how the chipmaker is pushing its AI beyond models and into the core infrastructure of drug discovery and research labs. The moves highlight Nvidia’s ambitions to become a foundational technology provider for the pharma industry.
The healthcare industry is contending with a difficult question: how to properly wield AI without taking on too much risk? Inherent in this battle is the role of humans. Here's how Merck's chief data officer is viewing AI.
The real change with AI will happen not when everyone adopts the technology, which is happening quickly, but when these AIs can actually communicate and coordinate with one another.
We may be on the eve of the next breakthrough: a new combination of “old” algorithms that promises to radically accelerate the discovery and development of new medicines.
Eli Lilly is licensing rights to a Phase 1-ready antibody that startup Alchemab developed for amyotrophic lateral sclerosis and other neurodegenerative disorders. Lilly’s pipeline has ALS drug candidates from previous deals with QurAlis and Verge Genomics.
Etiome’s precision medicine approach introduces a temporal component to treating disease — drugs developed specifically to treat disease at particular points in time even before symptoms show. The Flagship Pioneering startup’s approach offers the potential to prevent disease; in cases where disease is already present, Etiome aims to halt or even reverse it.
Ampersand Biomedicines’ map of all tissue in the human body reveals “addresses,” identifying markers on disease targets that are not found on healthy tissue. The company aims to improve the targeting of biologic drugs in order to reduce on-target, off-tissue toxicity.
Memorial Sloan Kettering Cancer Center partnered with AWS to accelerate its research with AI. The collaboration seeks to speed up drug discovery and increase activity within the health system’s startup accelerator.
While legacy pharma companies battle in court with government agencies over how to address the costs that result from antiquated drug development paradigms, a growing cadre of compute-enabled life science companies are unlocking the nascent power of next-generation compute technologies to transform drug discovery and development.
A drug development approach that makes use of hybrid AI can de-risk drug development while simultaneously removing other barriers to success. In other words, it has the power to significantly reduce the drug development timeline, and ultimately, save more lives.