QH Feature : Drug Discovery with Quantum Computers

The key constraint in today’s drug research and development is finding the right alternative by evaluating all possible alternatives which is extremely time intensive. On average, it takes over a decade and billions of dollars to bring a life-saving new drug to market. The current process involving crude screening of huge molecular databases means that many good drug candidates are missed, and poor drug candidates are sometimes selected. Replacing this with an instantaneous and accurate screen would be enormously advantageous in terms of speed, cost and precision. By expanding the search for new chemicals, Quantum computers promise to revolutionize this expensive, difficult, and lengthy process.

The number of calculations that would be required for simulating even small molecules grows exponentially and the computation quickly becomes intractable.For example, in order to model the structure of penicillin, it would require a computer that contains around 1086 classical bits, more than the number of atoms in the observable. So, today’s chemical researchers use computational chemistry programs that can only offer rough approximations and then apply a trial-and-error testing to see if it really works. Definitely, if someone can find a better way, there is a huge payoff. The road to this better way was initially proposed in the early 1980s by physicist Richard Feynman. His idea was to build a quantum computer that would use the quantum mechanics concepts to perform calculations in a new manner. This contributes to the potential for a fair period of time to model chemical reactions and to do so for chemical and material design.

Quantum computers are extremely good at problems with optimisation. This is due to their ability to leverage parallel quantum superposition states, which allows them to simultaneously model all possible outcomes of a problem, including the quantum interactions that occur on a particle-level. Theoretically, when they attain their promised computational capacity, quantum computers would be able to process mass data volumes quickly. Quantum simulation will enable molecular systems to be characterized faster and more accurately than existing quantium chemistry methods.

In addition, algorithmic developments in quantum machine learning offer effective alternatives to classical machine learning techniques which can also be useful for the biochemical efforts involved in early phases of drug discovery. Instead of finding new compounds through trial and error, the ability to simulate a chemical reaction can save billions of dollars as well as dramatically shorten the time for discovery. Quantum computers could not only be used to help discover new materials, they can also be used on the manufacturing side to identify ways of optimizing chemical processes to improve yields and minimize the generation of undesirable by-products in the production.

With major companies including IBM, Microsoft , Google, Intel and many small startups creating prototype systems, the past five years have seen rapid development of quantum computers. Today ‘s state-of-the-art includes quantum computers with 53 qubits size available from both IBM and Google with an estimated doubling in the number of qubits every two years. Scientists estimate that a quantum computer with 286 qubits could simulate the penicillin molecule that would have required 1086 classical bits. If the current pace of progress continues, there will be quantum computers of this size later this decade.

In the pharmaceutical research space, there are numerous young companies emerging looking at the computational boost and projected accuracy that quantum computing could lend to a variety of diagnostic, personalized medicine and treatment challenges. Here are some notable computational start-ups that apply quantum computing (combined with other methods) for drug discovery and promise to boost pharmaceutical research’s success rates.

ApexQubit – A startup based in Berkeley which was founded in 2018. Combining reinforcement learning, generative models and quantum computing allows them to hunt for the most promising undiscovered small molecules and peptides, with the ultimate aim of discovering personalized medicine without side effects.

Polaris Quantum Biotech – This latest start-up, also known as Polarisqb, was established in 2020, with headquarters in North Carolina. Polarisqb intends to speed up the drug discovery process from 5 years to 4 months by using a combination of artificial intelligence and quantum computing.

ProteinQure – Established in 2017, ProteinQure, a Toronto-based startup, combines quantum computing, reinforcement learning and atomic simulations to design novel protein drugs. Using this mix of technologies they model essential processes such as protein folding, as well as the underlying physics of biomolecular interactions.

GTN LTD – The London-based startup is developing technology that combines the processing power of quantum computing with machine learning algorithms to sort chemical data by mass quantities in search of new molecules that can be used to treat and prevent diseases. capable of analyzing large chemical spaces of small molecules in order to identify promising starting points for drugs discovery.

The advancement of quantum computing technology has the potential to have a profoundly positive effect on society, both alone and in combination with other technologies. The trickle-down effects of the quantum revolution could improve the lives of many, with proper care taken to ensure ethical research, development , and application.

  • By Team QuantumHermit

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