The recent pandemic has already caused damage to economies around the world and requires a fast and accurate resolution. There are several ways of tackling the virus that range from blocking its entry into cells to inhibiting its replication. Either way, a treatment is urgently needed. Considering the length of time required for a new drug to be approved, repurposing approved drugs is a valuable option to accelerate the drug discovery process.
Virtual screening plays an important role at the early stages of drug discovery. This process generally takes a long time to execute since it typically relies on measuring similarities among molecules. This is a computationally heavy and expensive exercise, and a major challenge for today’s computers. Most of the well-known methods for this type of evaluation use 2D molecular fingerprints to encode structural information. Although they are efficient in terms of execution times, these methods lack the consideration of relevant aspects of molecular structures.
Considering 3D structural properties of molecules increases the accuracy of the results, at the expense of higher computing times. By using Digital Annealer, the mathematical model is able to manage this kind of information while having shorter executing times. Additionally, the solutions provided by Digital Annealer consider the percentage of similarity between the molecules being compared as well as the specific domains that are similar. The latter information is key to help experts to review the results, and better inform decision making for further validation, therefore significantly reducing times and optimizing the entire process.
In order to accelerate this research, the project has been carried out in collaboration with the Dept. Infectious Diseases within King’s College London. King’s college and Fujitsu are collaborating using their Quantum-Inspired technology, Digital Annealer to find similarities among already approved molecules and desired properties for future COVID-19 treatments