insitro Strengthens Machine Learning-Based Drug Discovery Capabilities with Acquisition of Haystack Sciences – Business Wire

SAN FRANCISCO--(BUSINESS WIRE)--insitro, a machine learning driven drug discovery and development company, today announced the acquisition of Haystack Sciences, a private company advancing proprietary methods to drive machine-learning enabled drug discovery. Haystacks approach focuses on synthesizing, breeding and analyzing large, diverse combinatorial chemical libraries encoded by unique DNA sequences called DNA-encoded libraries, or DELs. Financial details of the acquisition are not disclosed.

insitro is building the leading company at the intersection of machine learning and biological data generation at scale, with a core focus on applying these technologies for more efficient drug discovery. With the acquisition of Haystack, insitro will leverage the companys DEL technology to collect massive small molecule data sets that inform the construction of machine learning models able to predict drug activity from molecular structure. With the addition of the Haystack technology and team, insitro has taken a significant step towards building in-house capabilities for fully integrated drug discovery and development. insitros capabilities in this space are being further developed via a collaboration with DiCE Molecules, a leader in the DEL field. The collaboration, executed earlier this year, is aimed at combining the power of machine learning with high quality DEL datasets to address two difficult protein-protein interface targets that DiCE is pursuing.

We are thrilled to have the Haystack team join insitro, said Daphne Koller, Ph.D., founder and chief executive officer of insitro. For the past two years, insitro has been building a company focused on the creation of predictive cell-based models of disease in order to enable the discovery of novel targets and evaluate the benefits of new or existing molecules in genetically defined patient segments. This acquisition enables us to expand our capabilities to the area of therapeutic design and advances us towards our goal of leveraging machine learning across the entire process of designing and developing better medicines for patients.

Haystacks platform combines multiple elements, including the capability to synthetize broad, diverse, small molecule collections, the ability to execute rapid iterative follow-up, and a proprietary semi-quantitative screening technology, called nDexer, that generates higher resolution datasets than possible through conventional panning approaches. These capabilities will greatly enable insitros development of multi-dimensional predictive models for small molecule design.

The nDexerTM capabilities we have advanced at Haystack, combined with insitros state of the art machine learning models, will enable us to build a platform at the forefront of applying DEL technology to next-generation therapeutics discovery, said Richard E. Watts, co-founder and chief executive officer of Haystack Sciences who will be joining insitro as vice president, high-throughput chemistry. I am excited by the opportunity to join a company with such a uniquely open and collaborative culture and to work with and learn from colleagues in data science, machine learning, automation and cell biology. The capabilities enabled by joining our efforts are considerably greater than the sum of the parts, and I look forward to helping build core drug discovery efforts at insitro.

Haystacks best-in-class DEL technology is uniquely aligned with insitros philosophy of addressing the critical challenges in pharmaceutical R&D through predictive machine learning models, all enabled by producing quality data at scale, said Vijay Pande, Ph.D., general partner at Andreessen Horowitz and member of insitros board of directors. This investment will power insitros swift prosecution of the multiple targets emerging from their platform, as well as the creation of a computational platform for molecule structure and function optimization. Having seen the field of computationally driven molecule design mature over the past twenty years, I look forward to the next chapter in therapeutics design written by the combined efforts of insitro and Haystack.

About insitro

insitro is a data-driven drug discovery and development company using machine learning and high-throughput biology to transform the way that drugs are discovered and delivered to patients. The company is applying state-of-the-art technologies from bioengineering to create massive data sets that enable the power of modern machine learning methods to be brought to bear on key bottlenecks in pharmaceutical R&D. The resulting predictive models are used to accelerate target selection, to design and develop effective therapeutics, and to inform clinical strategy. The company is located in South San Francisco, CA. For more information on insitro, please visit the companys website at http://www.insitro.com.

About Haystack Sciences

Haystack Sciences seeks to inform and speed drug discovery by acquiring data of best-in-class accuracy and dimensionality from DNA Encoded Libraries (DELs). This is enabled by proprietary technologies for in vitro evolution of fully synthetic small molecules and high throughput mapping of structure-activity relationships for selection of molecules with drug-like properties. The companys technologies, including their nDexer platform, allow for generation of better libraries and quantification of binding affinities of entire DELs against a given target in parallel. The combination of these approaches with machine learning has the potential to greatly accelerate the discovery of optimized drug candidates. Haystack Sciences is based in South San Francisco, California. It was incubated at the Illumina Accelerator and is backed by leading investors including Viking Global Investors, Nimble Ventures, HBM Genomics, and Illumina. More information is available at: http://www.haystacksciences.com/

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insitro Strengthens Machine Learning-Based Drug Discovery Capabilities with Acquisition of Haystack Sciences - Business Wire