Because sifting through millions of potential chemical reactions can be time-consuming, researchers have created an AI framework called G2Retro to automatically generate reactions for any given molecule. The new study showed that compared to current manual-planning methods, the framework was able to cover an enormous range of possible chemical reactions as well as accurately and quickly discern which reactions might work best to create a given drug molecule.
The team trained G2Retro on a dataset that contains 40,000 chemical reactions collected between 1976 and 2016. The framework “learns” from graph-based representations of given molecules and uses deep neural networks to generate possible reactant structures that could be used to synthesize them. Its generative power is so impressive that, according to Xia Ning, lead author of the study, once given a molecule, G2Retro could come up with hundreds of new reaction predictions in only a few minutes.
Having such a dynamic and effective device at scientists’ disposal could enable the industry to manufacture stronger drugs at a quicker pace – but despite the edge AI might give scientists inside the lab, Ning emphasizes the medicines G2Retro or any generative AI creates still need to be validated – a process that involves the created molecules being tested in animal models and later in human trials.