Artificial intelligence is rapidly changing how new drugs are discovered and this past week saw two important developments in this space. The first is the Series A funding announcement for Genesis Therapeutics. The Genesis team is at the forefront of applying AI to the field of drug discovery, using novel graph neural networks for molecular property prediction. Their platform for drug discovery was a key differentiator in securing a multi-target partnership with Genentech. Radical Ventures participated in this funding round alongside Rock Springs Capital, Andreessen Horowitz and others, to support Genesis in its search to cure human disease through the discovery of small molecule drugs.
The second development was Google’s DeepMind announcement of a breakthrough towards solving the protein folding problem – an innovation that holds significant promise for the design of protein therapeutics. Their AlphaFold system, which uses machine learning to iteratively predict the physical structure of a protein represented as a “spatial graph”, has performed 1.7 times better than the closest competitor in an internationally ranked competition.
The results are impressive, and have prompted some members of the computer science community to ask whether this is the “ImageNet moment for biology”, referring to the landmark “AlexNet” results achieved by students of Geoff Hinton on the ImageNet dataset in 2012 – the watershed moment that led to the wide adoption of deep learning and the current ‘Age of AI’.
While these results are potentially groundbreaking, the impact is more likely to be seen in the medium to long term as the methods used in AlphaFold are adapted for use with medicinal chemists and finely tuned wet lab processes. The development of any advanced material, including protein or small molecule therapeutics, requires a close link between the material development and the forming process of an application.
What makes Genesis such an exciting company is its multidisciplinary and fully integrated approach. CEO Evan Feinberg has assembled a team of drug developers, AI researchers and software engineers to create transformative therapies for patients. By pairing best in class research methods with expert drug developers and very fast experimentation loops, novel therapies can be created much more quickly. As this week’s news illustrates, this graph-based approach may open up entirely new ways of solving some of humanity’s important challenges.