In a new scientific paper, AI-driven biotech Gero has demonstrated the feasibility of applying quantum computing to drug design and generative chemistry.
The research, published in Scientific Reports, outlines how a hybrid quantum-classical machine-learning model was used to interface between classical and quantum computational devices with the goal of generating novel chemical structures for potential drugs.
The vast structural space of all possible drug-like molecules presents a monumental challenge in drug discovery. The number of realistic drug-like molecules is estimated to be between 10^23 and 10^60 – and only about 10^8 substances have ever been synthesised.
The team explored whether a hybrid generative AI system – a deep neural network working in conjunction with commercially available quantum hardware – could suggest unique chemical structures that are synthetically feasible and possess drug-like properties.
“These breakthroughs pave the way for a dramatic acceleration of the drug discovery process,” said Peter Fedichev, CEO of Gero. “Drug design operates at the intersection of the realms of classical and quantum phenomena, and requires simultaneous determination of quantum properties of drug-like molecules and their effects on living systems described by classical physics. This is why quantum computing will significantly augment our capacity to develop transformative treatments for the most challenging diseases and conditions, including ageing itself.”
The hybrid quantum/classical generative model suggested 2,331 novel chemical structures with properties typical for biologically active compounds. Encouragingly, less than 1% of the generated molecules had a high similarity to any molecule in the training set, indicating a high level of novelty in the generated compounds.
