Unlocking the power of machine learning for small molecule drug discovery

Rick Wagner of ZebiAI and Patrick Riley of Google Accelerated Science (GAS) discuss the development and benefits of a new machine learning drug discovery platform. A collaborative study between ZebiAI, Google Accelerated Science (GAS) and X-Chem has used the power of machine learning to improve the drug discovery process. The paper, published in the Journal of Medicinal Chemistry, describes an effective machine learning platform with the ability to accelerate drug discovery based on DNA-encoded small molecule library (DEL) selection data. According to the researchers, their findings demonstrate the efficacy of the programme to predict highly potent small molecule inhibitors within a virtual library of compounds across three diverse protein targets. "We envision artificial intelligence (AI) and machine learning will be a leading source of novel, small molecule drug candidates. These technologies will become indispensable as a means for leveraging large datasets to understand disease biology and identify the best candidates to address intractable diseases," said Founder and Director of ZebiAI, Rick Wagner, when speaking to Drug Target Review.