The concept of deoxyribonucleic acid (DNA)-encoded chemical libraries was invented three decades ago, but only recently it has become a mainstream screening paradigm in drug discovery -- primarily due to exponential progress in our ability to manipulate and sequence DNA. Another key technology that was instrumental during the rise of DNA-encoded libraries is combinatorial chemistry.
DNA-encoded libraries (DEL) technology provides an opportunity to screen chemical spaces of unprecedented size -- not only billions but trillions of molecules in a single vial. For example, HitGen’s libraries contain more than 1 trillion molecules, X-Chem provides a collection of libraries covering over 7.5 billion compounds for screening, while Nuevolution assembled a collection of 40 trillions compounds (in 2019, Neuvolution was acquired by Amgen for $167 million).
Since DEL technology offers access to essentially the largest chemical space available on the market, this big data technology is a natural fit for big data analytics and modeling technologies offered by the field of artificial intelligence -- and some companies have already been chasing this opportunity.
A notable deal took place in 2020, when Insitro, a leading player in the application of machine learning for drug discovery, founded by Daphne Koller, acquired Haystack Sciences. Haystack’s machine learning-based platform combined multiple elements of their DEL technology, including the capability to synthetize broad, diverse, small molecule collections, the ability to execute iterative follow-up, and a proprietary semi-quantitative screening technology, called nDexer™, that generates higher resolution datasets than possible through conventional ‘panning’ approaches.
In 2020, Google Research published results of their collaborative effort with X-Chem, one of the leading players in the DEL technology space, in an article “”, where they demonstrate an effective new method for finding biologically active molecules using a combination of physical screening with DNA-encoded small molecule libraries and virtual screening using a graph convolutional neural network (GCNN). This research has led to the creation of the Chemome initiative, a cooperative project between our Accelerated Science team and ZebiAI, a platform that applies massive experimental DNA encoded library data sets to power machine learning for drug discovery.
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