Recently, there are signs that pharmaceutical companies are increasingly leaning towards adopting quantum technology. Earlier in January 2021, Boehringer Ingelheim, the world’s largest private drug company, announced a partnership with Google. Roche, the largest pharmaceutical company in the world, also announced it was working with Cambridge Quantum Computing (CQC) slightly later. They will develop quantum algorithms for drug discovery and development.
Quantum computing can simulate molecules and chemical reactions better than classical computers. Classical computers can simulate caffeine as their limit, which has 24 atoms. However, proteins have thousands of atoms, and pharmaceutical companies need quantum computers to be able to simulate these.
CQC only works with quantum software, providing it to users who buy quantum computers from companies such as IBM, Honeywell, Microsoft, and Google. However, many users aren’t sure how to fit quantum software into their company, so they give advice as well. CQC is currently working with 5 of the top 10 pharma companies.
One of the areas CQC works in is quantum chemistry, which includes a variety of problems. These are figuring which molecules will bind best with a certain protein, molecule crystal structures, how many states molecules can adopt across a range of energies, and many more.
The main algorithm used in research like this is the variational quantum eigensolver, used to find the most optimal solution to a problem. The eigensolver is a hybrid algorithm, where classical computers perform the brunt of the work and quantum processors solve the sections their classical counterparts cannot. Edwards says that this is now the industry standard.
A new algorithm CQC is now using for quantum chemistry uses imaginary time evolution. It is more resource-efficient and less likely to pursue almost-optimal solutions.
Quantum computers today can simultaneously perform calculations with molecules 5 to 10 atoms large, but small drug molecules are around 30 to 40 atoms. Quantum chemistry usually separates them into fragments before using a method called density matrix embedding theory to put the pieces together. This allows the quantum computer to understand how the fragments work together as a single larger molecule.
People think that quantum computing is faster than classical computing in pharmaceutical research. This is mostly true, but the companies are aiming for accuracy instead.
Nowadays, big pharma is committed to using quantum computing. The major companies have formed a consortium called QuPharm. Its objective is to advance quantum computing for drug production. QuPharm itself is working with the Quantum Economic Development Consortium (QED-C), founded to helping develop commercial applications for quantum science and engineering. It is also working with the Pistoia Alliance, another consortium aiming to discover innovations in life science.
Quantum computing will also significantly benefit bioinformatics, helping solve problems in gene sequencing and gene annotation. CQC is partnering with Crown Bioscience and JSR Life Sciences to work on cancer drugs.
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