Apple Tree Partners Unveils Deep Apple Therapeutics to Dramatically Accelerate Drug Discovery with Structure-Based, Ultra-Large Library Virtual Screening and Deep Learning Models

Conducting entirely virtual screens of multi-billion molecule virtual libraries, Deep Apple can quickly identify hits that promote specific signaling in GPCRs and other target classes

San Francisco-based company created by ATP with academic co-founders Georgios Skiniotis, Brian Shoichet, and John Irwin is discovering novel small molecules in a vastly expanded virtual chemical space, to tackle a wide range of diseases

Deep Apple emerges from stealth with a Series A commitment of $52 million from ATP

SAN FRANCISCO and NEW YORK, Dec. 14, 2023 /PRNewswire/ -- Apple Tree Partners (ATP), a leader in life sciences venture capital, today 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. With a powerful discovery engine that combines ensemble cryo-EM, deep learning, and molecular docking screens of ultra-large libraries, Deep Apple can 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 is particularly well-suited to expedited hit-finding against integral membrane proteins. The company currently is 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 upon 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.

"ATP created Deep Apple to revolutionize drug discovery in terms of speed, cost, and effectiveness," said Spiros Liras, Ph.D., founding CEO of Deep Apple and a Venture Partner at ATP. "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."

Deep Apple conducts 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. Wet-lab interrogations of the chosen virtual compounds feed back into the company's deep learning models to continually improve predictive performance.

"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," said Paul Da Silva Jardine, Ph.D., Chief Scientific Officer at Deep Apple and a Venture Partner at ATP. "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."

"ATP founds and builds companies that bring together critical technologies and efficient research plans for translation," said Seth Harrison, M.D., ATP Founder and Managing Partner. "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."

About Deep Apple Therapeutics

In a vast, unexplored virtual chemical space, Deep Apple Therapeutics moves with unprecedented speed to discover novel small molecules for high-value targets. Deep Apple combines ensemble cryo-EM to explore receptor conformations; deep learning; and the docking of multi-billion compound libraries generated using the company's proprietary Orchard.aitm algorithm. Deep Apple's deep learning discovery engine is built upon founder expertise in computational modeling, protein and target structure determination, large-scale molecular docking, and the elucidation of protein dynamics and conformational transitions. This structure-based, machine-learning driven drug design approach enables the company to rapidly advance novel candidates addressing well-validated targets in inflammatory, metabolic, and endocrine diseases. For more information, visit deepappletx.com.

About Apple Tree Partners

Apple Tree Partners (ATP) is a leader in life sciences venture capital, with a 25-year track record of translating emerging scientific advances into novel innovative treatments. ATP creates companies starting at various stages, from pre-IP ideas to asset spinouts, investing in them from seed stage through IPO and beyond. The core of ATP's strategy is providing flexible capital and access to a world-class team of venture partners and EIRs, to build sustainable, research-driven enterprises that deliver therapeutics to improve human lives. For more information, visit www.appletreepartners.com.

SOURCE Deep Apple