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RIYADH: Using traditional discovery processes, a staggering 90 percent of drug development trials are unsuccessful. But what if there is a future in which quantum technologies could revolutionize that process to achieve unprecedented efficiency?

The race to develop quantum computers has been surging worldwide. In April, IBM announced a $150 billion investment plan to strengthen US technologies and innovation over the next five years, including a push for quantum computer development.

Additionally, the UK’s National Quantum Technologies Programme has invested more than £1 billion in quantum technology since its establishment in 2014, with facilities such as the National Quantum Computing Centre.

PASQAL in France is also a leading company in quantum research. In 2024, Saudi Aramco signed an agreement with PASQAL to deploy the first quantum computer in the Kingdom, scheduled to be up and running by the end of this year.

If the promise of quantum computing holds, the pharmaceutical industry could be looking at faster, more accurate, and less costly drug discovery and development.

The World Health Organization predicts that antimicrobial resistance to existing drugs will lead to 10 million human deaths by 2050. To stop the timer, the pharmaceutical industry must adopt new and innovative technologies.

Artificial intelligence has already had a huge impact on the efficiency and success of clinical trials, generating new materials and computationally predicting their performance rather than relying on scientists’ intuition for molecular hypotheses that then must be synthesized and tested repeatedly.

Quantum computing, however, has the potential to take it one step further. It uses special units called qubits (quantum bits), which can exist in multiple states at once and can link together in unique ways, to perform computations much more efficiently than classical computers.

In layman’s terms, quantum computers solve complicated problems quicker while AI simplifies the problem and then solves it. Quantum computers understand the problem. AI does not.

A research scientist focusing on computational catalysis with a doctoral degree in chemical engineering spoke to Arab News about the current feasibility of this tool.

“We are talking about mature technology (AI) versus very immature technology (quantum),” he said.

According to the World Economic Forum, in synergy, quantum computing and AI can lead to enhanced molecular understanding.

Although our expert heeds that “we are not at the stage where we can actually do that, we can only do it on very specific problems because there are many physical limitations… you need to be able to manipulate atoms in a very precise way that we currently cannot do.”

Quantum physics allows scientists to predict the behavior of electrons in molecules, producing detailed three-dimensional structural insight into new drug designs.

Rather than adopting traditional laborious methods such as X-ray crystallography, quantum principles and AI provide virtual simulations.

AI further accelerates this process by quickly analyzing datasets and clinical outcomes to pin down favorable drug targets and predict a compound’s efficacy.

A novel tool called quantum machine learning combines AI’s power of data analysis and pattern recognition with quantum computing’s ability to simulate complex molecular behavior throughout trillions of possibilities.

This paves the way to more accurate and faster predictions in drug binding orientation, absorption, and metabolic pathways.

QML makes it possible to sift through vast chemical spaces holding trillions of potential drug candidates in weeks or days in contrast to the years that classical computers would need.

With the integration of quantum computing and AI, compound screening traditionally executed “in vitro,” meaning outside of a living organism, can be done “in silico” instead, meaning carried out in virtual simulations.

A new quantum-AI model developed by Qubit Pharmaceuticals with Sorbonne University and announced in May of this year called FeNNix-Bio1, reportedly leverages unprecedented computational power and very accurate molecular databases.  

Employing the principles of quantum mechanics (such as superposition and entanglement), quantum computers can model molecular and atomic behavior with great accuracy and speed.

This is critical to understanding relevant properties such as molecular stability, binding affinity, and how drugs could interact with target proteins in real-world conditions.

Structural optimization and docking — determining how a drug candidate fits into a biological target, can be simplified using QML and quantum-powered algorithms.

These algorithms rapidly evaluate orientations of molecules against target structures to identify optimal configurations, and which molecules will bind most effectively. This enhances drug absorption and metabolic stability.

Quantum computing and AI models are then able to streamline the preclinical phase, delivering only the most promising compounds to laboratory validation, significantly reducing tedious lab work and enabling researchers to conduct faster and cheaper work.

And with more accurate early-stage predictions, overall success of clinical trials is boosted, lab to market time is reduced, and the possibility of delivering targeted treatments for unmet patient needs is increased.

“You do patient trials to reduce the risk of anything going wrong with the patient, imagine if you are able to accurately predict how the drug will affect people without doing a trial. This will create a leap in how we produce drugs and how we can commercialize drugs,” our expert said.

Meeting specific patient needs based on their biological profiles rather than producing drugs for a wide demographic can drastically change our healthcare systems and how we consume products.

Patients will be able to get a drug for diseases such as Alzheimer’s, diabetes, cancer, and more without having to wait ten years for a trial to decide their fate.

You can also anticipate what conditions or illnesses people are at high risk of developing later in life and treat them early on, such as joint pain and hair loss.

It comes down to significant time reduction and improved chances of success.

“A quantum computer can significantly increase my accuracy. My chances of success are very dependent on my prediction of the performance.

“The quantum computer can make more accurate calculations that can make my predictions of the performance much more accurate. By doing that, my chances of success will be higher.

“Another way is that a quantum computer will be much faster in performing tasks, generating structures and predicting their performance than AI, and by that I will reduce my time further.”

Although we are still a long way from achieving this, the functionality of quantum computing and AI theorizes that personalized medicine and treatments for patients is possible.

“If (specific patient information) becomes accessible to those companies… then they match that information to their database, hypothetically speaking it is possible.”

Although this all sounds like the realms of sci-fi, there have been significant strides in this area of quantum research.

Pfizer and its partner XtalPi, a US-China pharmaceutical tech company, reportedly used quantum-inspired algorithms and AI cloud computing to reduce 3D structure prediction time of new molecules from months to days, enabling rapid assessment of candidate molecules and their drug-likeness.

Additionally, it is said that Qubit Pharmaceuticals’ FeNNix-Bio1 quantum AI model could be used for QML applications such predicting toxicity, side effects, and drug metabolism with greater speed and accuracy.

Taking it into perspective, our expert said: “Three years ago, no one would have thought we would have a large language model that can perform as well as ChatGPT does today, it came out of left field. A breakthrough could happen.”

However, WEF warns that before this technology can become the new commercial norm, certain guardrails need to be put in place to ensure the safe, effective, and responsible use of this novel tool.

Data integrity and avoiding bias, ethical and regulatory oversight, workforce readiness training, and a shared vision for applying best practices all must be upheld industry wide.