
The AI Drug Discovery Boom Is Coming. We Are Not Ready.
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.
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.
Genesis Therapeutics recently joined the growing list of AI-powered drug discovery startups receiving venture funding this year. The company closed a $200 million Series B financing round, taking its total funding to date to more than $280 million.
Human tissues, coupled with AI that can deal with terabytes of data will blow mice models out of the water. With animal testing requirements finally removed, the pharma industry and its constituents can hope for faster innovation.
Traditionally, pharmaceutical companies have been slow to adopt these newer technologies, instead relying on well-established and proven - but usually complex - manufacturing processes. However, the time to begin investing in – and exploring - AI, machine learning, and big data is now.
BioNTech says its InstaDeep acquisition will help it expand its use of artificial intelligence in drug discovery and development. The startup specializes in a type of machine learning called reinforcement learning.
Sanofi will use Insilico Medicine’s tech to advance drug development candidates for up to six new targets. Disease indications were not disclosed, but the pharmaceutical giant said the new agreement will boost its drug discovery research in China.
Nested Therapeutics uses computational techniques to map mutations and protein structures, identifying previously unseen places where a cancer drug can bind. The Versant Ventures-founded startup is now out of stealth with two programs on the path to the clinic.
The CEO of Anagenex believes that the company's AI engine trained on billions of data points and iterative testing will blow other AI drug discovery startups out of the water.