Apple Tree Partners (ATP) – a leader in life sciences venture capital – recently announced $52 million in Series A funding for its portfolio company Deep Apple Therapeutics, created and incubated by ATP to rapidly discover novel small molecule therapeutics for high-value targets through virtual screening of AI-generated virtual libraries. Through a powerful discovery engine that combines ensemble cryo-EM, deep learning, and molecular docking screens of ultra-large libraries, Deep Apple could go from target identification to lead optimization in less than 12 months – a fraction of the industry standard time – and can pursue biological target signaling inaccessible to conventional discovery approaches.
Deep Apple’s discovery engine is broadly applicable across disease areas, and it is particularly well-suited to expedited hit-finding against integral membrane proteins. And the company is currently advancing multiple programs focused on GPCR modulators, a proven target class with applications in metabolic disorders, inflammation, immunology, and endocrine diseases.
Deep Apple’s drug discovery engine builds on leading expertise and technologies from its academic co-founders: Georgios Skiniotis, Ph.D., of Stanford University, a world leader in cryo-EM and GPCR structural biology; Brian Shoichet, Ph.D., of the University of California San Francisco (UCSF), a pioneer of virtual screening; and John Irwin, Ph.D., of UCSF, the computational library authority who created the widely used ZINC free virtual library of more than 10 billion synthesizable compounds.
Deep Apple offers in silico screening of billions of synthesizable compounds against orthosteric and allosteric binding sites in mere hours, and then computationally generates vast project-specific virtual libraries to discover proprietary chemotypes with desirable dockable and druggable properties. And wet-lab interrogations of the chosen virtual compounds feed back into the company’s deep learning models to continually improve predictive performance.
KEY QUOTES:
“ATP created Deep Apple to revolutionize drug discovery in terms of speed, cost, and effectiveness. We brought together unique capabilities from our founders to build a true deep learning discovery engine that stands apart from the pack of AI-driven approaches to protein structure elucidation and drug discovery. Machine-learning enabled processing of cryo-EM data allows us to reveal biologically relevant conformations in the context of interactions with signaling partners – transient binding pockets that may be missed by static models and empirical screening methods. And our virtual large-scale docking enables us to quickly home in on the right drug for the right target.”
- Spiros Liras, Ph.D., founding CEO of Deep Apple and a Venture Partner at ATP
“Deep Apple exclusively uses virtual screening for hit identification, and we have achieved high-quality hits against difficult-to-drug targets at an extremely fast pace. Since we commenced operations last year, we have initiated multiple GPCR programs, including non-peptide/non-GLP-1 programs in obesity and weight management, as well as promising programs in inflammation and inflammatory disorders. And with the versatility of our discovery platform, GPCRs are only the tip of the iceberg.”
- Paul Da Silva Jardine, Ph.D., Chief Scientific Officer at Deep Apple and a Venture Partner at ATP
“ATP founds and builds companies that bring together critical technologies and efficient research plans for translation. In my more than three decades of investing in biotech startups, particularly in early-stage drug discovery platform technology, I have never seen a company to move as rapidly as Deep Apple has, from chemical biological idea to development candidate, for high-value, difficult-to-address targets.”
- Seth Harrison, M.D., ATP Founder and Managing Partner
