A Note On Quantum Computing in Drug Discovery

Drug discovery has been regarded as a miracle since the discovery of the first synthetic drug in 1869, and computers have shown to be invaluable for pharmaceutical companies, in the process. 

 

Many artificial intelligence and machine learning techniques are employed for this, although they are frequently limited to calculating molecules up to a certain size. Quantum computing and quantum machine learning have been incorporated into the field of biogen to overcome this restriction and undertake drug development.

 

(Related read: How Does Quantum Computing Improve Machine Learning?)

 

Quantum Computing

 

Quantum computing is the process of computing that employs the notion of quantum physics, or, to put it another way, it employs quantum mechanics to solve specific problems with a significant leap forward in processing. Quantum computers study the behavior of energy and work at atomic or subatomic levels. 

 

Unlike traditional computers, which rely on on-or-off bits, quantum computers employ qubits, which may be on, off, or both — a phenomenon known as superposition. This superposition enables quantum computers to do several calculations at once, considerably more efficient than is possible with classical technology. 

 

Qubits can process a lot more data than traditional computers. Qubits employ quantum-mechanical properties to solve complicated equations in a probabilistic way, allowing a computation done with a quantum method to sample from a probability distribution of correctness. 

 

Quantum computing is ideally suited to a subset of computer needs and applications, such as optimization, chemical modeling, and artificial intelligence, due to its mix of increased speed and probabilistic answers.

 

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Advantages of Quantum Computing:

 

There are many advantages of quantum computing over classical computing, in the pharmaceuticals. Although the use of quantum computers is still in its initial stage, the ability of quantum computers to dramatically speed up and improve testing and forecasts is revolutionizing the pharmaceutical industry. 

 

According to this article written by Pharmaceutical Technology, QuPharm was founded in 2020 by major pharmaceutical companies to accelerate the use of quantum computing in the sector. 

 

Quantum Brilliance, an Australian-German start-up, is one firm striving to make this goal of quantum-powered medication discovery a reality. The company’s co-founder Marcus Doherty said that,

 

 “Quantum computers behave by the same laws as the molecules themselves, which means that you can be much more efficient at simulating a molecule using a quantum computer than you can with a classical computer. If you want to simulate two atoms, then for a quantum computer you only need, let’s say, two qubits to do that simulation. But for a classical computer, you would need at least four bits to simulate. This is an example of how the resources for a classical computer explode if they want to simulate something quantum mechanical like a molecule, compared to a quantum computer.”

 

(Recommended read: What does Googles’ Quantum Supremacy describe?)

 

 

Role of Quantum Computing in Drug Discovery:

 

Pharma invests a whopping 15 percent of its revenues on research and development, which accounts for more than 20% of overall R&D investment across all businesses in the global economy (Source). This investment goes hand in hand with innovation. 

 

The pharma firms have been early users of computational chemistry's digital tools, such as molecular dynamics (MD) simulations and density functional theory, for decades, in their quest to enhance the R&D process (DFT). 

 

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Artificial intelligence has lately been used in pharmaceutical R&D. (AI). Quantum computing is the next digital frontier. 

 

Some of the ways how Quantum computing helps in drug discovery are given below:

 

1. Pharmaceutical firms' main job is to find and create tiny molecules and macromolecules that might help cure ailments and disorders. 

 

Pharma is a natural contender for quantum computing because it concentrates on molecule formations. The molecules (including those that may be utilized to make medicines) are quantum systems or systems based on quantum physics.

 

2. Quantum Computing is predicted to be more successful than traditional computers at predicting and simulating the structure, characteristics, and behavior (or reactivity) of these molecules. 

 

When interactions at the atomic level are important, as they are in many compounds, exact approaches are computationally intractable for ordinary computers, while approximation methods are frequently insufficiently precise.

 

3. Quantum computers are capable of effectively stimulating the whole issue, including atomic-level interactions. The value of these quantum computers will skyrocket as they get more powerful. 

 

While quantum computing's technology is difficult to grasp intuitively, its significance is clear: it will perform certain types of computational jobs tenfold quicker than today's conventional computers. 

 

Quantum Computing may thus contribute value throughout the whole therapeutic value chain, from discovery to development to registration and postmarketing, once completely matured.

 

(Related blog: Business applications of quantum computing)

 

How does Quantum Computing work in drug discovery?

 

Currently, pharma companies use non-Quantum Computing technologies like Molecular Dynamics (MD) and density functional theory (DFT) to process compounds in a process known as computer-assisted drug development (CADD).

 

However, the traditional computers on which they rely are severely restricted, and simple calculations predicting the behavior of medium-sized pharmacological molecules might take a lifetime to complete properly. 

 

By eliminating some of the research-related "dead ends," which add significant time and cost to the discovery phase, CADD on quantum computers could expand the scope of biological mechanisms amenable to CADD, shorten screening time, and reduce the number of times an empirically based development cycle must be run.

 

(Suggested reading: AI applications in chemistry)

 

Future of Pharmaceuticals with Quantum Computing:

 

Quantum Computing might improve the effectiveness of current CADD technologies by assisting in the accurate prediction of molecular characteristics. This can have an impact on the development process in a variety of ways, including simulating protein folding and drug candidate interactions with physiologically relevant proteins. 

 

Researchers may be able to evaluate computational libraries against several possible target structures in parallel using Quantum Computing in this case. Due to a lack of computing power and time, current methods generally limit the structural flexibility of the target molecule. These limitations may make it more difficult to find the best medication options.

 

In the long run, Quantum Computing might enhance hypothesis development and validation by utilizing machine-learning (ML) techniques to discover novel structure-property correlations. 

 

Once it has matured enough, Quantum Computing technology may be able to produce new sorts of drug-candidate libraries that comprise peptides and antibodies in addition to small compounds. It might also pave the way for a more automated drug discovery process in which a vast structural library of physiologically relevant targets is automatically screened against drug-like compounds using high-throughput methods.

 

One may even imagine quality control sparking a paradigm change in pharmaceutical R&D, moving away from today's technologically enabled R&D and toward simulation-based or in silico drug discovery, a trend that has already been observed in other sectors.

 

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Quantum Computing Use Cases in Drug Discovery:

 

The following Quantum Computing use cases on various stages of drug development and will appear at various times over a long period:

 

1. Target Identification and Validation:

 

QC can be used to predict the 3-D structures of proteins reliably during target identification. Obtaining high-quality structural data is a time-consuming procedure that frequently yields poor results. AlphaFold, created by Google's 

 

DeepMind, was a milestone in AI-driven protein folding, but it still doesn't solve all of the problems that traditional computing-based simulations have, such as protein complex creation, protein-protein interactions, and protein-ligand interactions. The interactions are the most difficult to solve traditionally, therefore QC, which allows for the explicit treatment of electrons, may help.

 

2. Hit generation and Validation:

 

The capacity of QC to handle complicated phenomena in parallel would be very useful during hit creation and validation. Pharma firms may only utilize CADD on small to medium-sized drug candidates and in a sequential way using current technology. 

 

Pharma firms would be able to expand all use cases to chosen biologics, such as semi-synthesized biologics or fusion proteins, and conduct in silico search and validation studies at higher throughput with powerful enough QC.

 

(Also read: What does Googles’ Quantum Supremacy describe?)

 

3. Lead Optimization:

 

QC may enable for improved absorption, distribution, metabolism, and excretion (ADME); more accurate activity and toxicity estimates for organ systems; dosage and solubility optimization; and other safety problems during lead optimization, which is a top-three parameter to increase R&D productivity.

 

4. Clinical Trials:

 

Patient identification and classification, as well as population pharmacogenetic modeling, might help to improve clinical trials. 3 QC might improve the selection of trial venues during trial planning and execution. 

 

To improve active safety surveillance, QC might be used to supplement causality analyses for adverse events. 

 

5. Data linkage and generation:

 

The metalevel of R&D is mostly concerned with connecting suitable data—for example, by using effective (semantic) management to create sensible relationships between data points. 

 

The larger the graphs that guide the drug discovery research process gets, the more complicated the biological information that can be processed. QC might be used to fake missing data points throughout the study process, generating fake data using machine learning algorithms. 

 

This might be especially beneficial in situations when there is a lack of data, such as in uncommon diseases, which can be alleviated by using fake data sets. In terms of speed in training ML models, QC will establish a new standard.

 

Conclusion

 

Quantum algorithms will be imported into conventional computer systems as quantum computing develops as a field. The pharmaceutical industry is ideally positioned to capitalize on this potential. 

 

CADD, AI, ML, and non-QC DFT- and MD-simulation technologies already play a major role in the sector's R&D because of its tech-ready culture. Furthermore, pharma companies are already using quantum-chemical simulations, so the entry barrier is minimal.

 

Pharmaceutical firms should evaluate quality control today so that they may build the basis for reaping the benefits of the technology later. Many of them may see QC as a significant potential, but each pharma company must determine how much exposure it has and the magnitude of its QC opportunity concerning its present development speed. In this article, we have discussed everything that could relate quantum computing to drug discovery.