How is Quantum AI Accelerating the Drug Discovery Process?

 Artificial intelligence consists of a synergistic assembly of enhanced optimization strategies with wide application in drug discovery and development, providing advanced tools for promoting cost-effectiveness throughout the drug life cycle. AI specifically brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients comply with their treatments. Accelerated pharmaceutical development and drug product approval rates further benefit from quantum computing technology, which enables larger profits from patent-protected industry exclusivity.

Accelerating the discovery process with AI and quantum mechanics

In October 2024, Researchers at Southern Methodist University developed an innovative tool, SmartCADD, that combines artificial intelligence, quantum mechanics, and computer-assisted drug design to accelerate the discovery of new treatments for various diseases. This open-source virtual platform shifts through billions of chemical compounds in just one day, significantly reducing the time needed to identify promising drug candidates. The potential impact of SmartCADD extends beyond HIV drug discovery, as it can be applied to other drug discovery pipelines.

This innovative tool has the potential to accelerate the development of new treatments for various diseases, ultimately improving human health outcomes.  POLARISqb, a biotech company founded in 2020, is utilizing D-Wave’s quantum annealing technology to speed up the drug discovery process dramatically. Their innovative approach compresses what traditionally takes years into mere weeks, potentially transforming the pharmaceutical industry. This in turn has significantly contributed to the growth of the Quantum AI market.

The Centre for Trustworthy Technology’s recently released paper, A New Frontier for Drug Discovery and Development: Artificial Intelligence and Quantum Technology, provides a realistic overview of the technologies’ limitations and the challenges that pharmaceutical companies inevitably face as they implement novel techniques in legacy systems and address ethical discourse. To manage this potentially transformative technology and make the most of its opportunities, the Centre for Trustworthy Technology has created a framework to promote the trust, efficacy, and proper use of these emerging tools.

D-Wave and Japan Tobacco join forces to boost the drug discovery process 

D-Wave Quantum Inc., a global leader in quantum computing systems announced its acquisition of Japan Tobacco Inc., a renowned tobacco company in October 2024. It aimed to develop an innovative drug discovery process. The companies are targeted to work together on a joint project by utilizing quantum computing technology and AI to produce drugs. In this agreement, Japan Tobacco has used D-Wave's computing solutions to accelerate the speed and quality of drug development. Moreover, companies also envisioned developing a new method for discovering excellent compounds used in pharmaceutical products. 

Addressing the drug discovery complications with Quantum AI and crystallography

Traditional drug discovery processes fail to address current and future global healthcare needs. This long process, ranging from initial R&D to clinical trials, often spans a decade and fails during drug approval. More than 90% of drug development attempts are unsuccessful. Unfortunately, this often means humans are left defenseless against new and progressing viruses and infections. The United Nations’ World Health Organization predicts 10 million human deaths by 2050 from antimicrobial resistance, which is the microbial resistance to existing drugs. Advancements in AI and quantum technologies, however, could change that. These emerging technologies are working together to enhance existing drug discovery systems.

Quantum computing increases scientists’ comprehension of intricate biological systems, thus expediting the drug discovery trajectory, especially when targeting proteins previously considered undruggable. The impact of quantum-inspired algorithms and methodologies to target these undruggable proteins is going to be significant on a large population of people who are impacted by these diseases. The ability to accurately model large, complicated biological molecules and chemical reactions is crucial for drug discovery. Quantum computers process such interactions simultaneously. According to Kristin M. Gilkes, EY Global Innovation Quantum Leader, Quantum computers allow us to navigate these complex interactions more efficiently, providing a detailed view of the protein structure and how a drug might interact.

When a new molecule is a potential drug candidate, scientists want to learn as much about the molecule as possible; its shape, size, and other properties down to the electron level. To do this they have traditionally used a technique called X-ray crystallography. In a multi-step, time-consuming process the compound is converted into crystal form, and then an X-ray beam is shot through it to determine its 3-D structure. However, in the past few years, scientists have begun using a computer modeling technique known as crystal structure prediction (CSP) to do virtual crystallography. They predict the behavior of electrons in a molecule by applying quantum physics to determine its 3-D structure. CSP involves very many complicated mathematical calculations that take up a lot of computing power and up to four months.

SandboxAQ and Nvidia’s initiative to transform healthcare

In August 2024, SandboxAQ, an advanced computing enterprise collaborated with Nvidia, an American-based technology corporation. The company planned to incorporate quantum and AI technology with this initiative to enhance drug discovery procedures, material science matters such as the chemical composition of batteries, and treatment methods for various diseases. 

Boosting drug discovery with quantum machine learning

Coupled with machine learning, quantum computing enables even more powerful analysis. “Quantum machine learning can assist in analyzing complex biological data, such as genomes and proteomics data, to identify potential drug targets and predict drug interactions,” Gilkes explains. This combination allows for fast, accurate analysis of massive datasets that would be impractical with classical techniques alone.

Consider the human genome: it comprises about three billion base pairs. Quantum computers can process such vast amounts of data quickly and accurately, transforming drug discovery. This speed and precision allow researchers to identify promising drug targets faster, predict drug interactions more accurately, and design better treatments.

Should quantum computing facilitate an enhancement of even 20% in accuracy rates for target identification in drug discovery, the ripple effect could be a substantial reduction in the related development expenditures. Interestingly, the hybrid approach of ML with quantum computing is now used as a powerful tool in predictive analysis. Although the reversibility of the quantum gates is guaranteed, the lower power consumption is not a bonus that comes along with reversibility. Only specific designs of quantum circuits allow you to save some energy.

Quantum circuits perform quadratic, polynomial, or exponential tasks faster. Hybrid quantum ML uses QC to perform ML algorithms or acquire the processing of quantum information into ML. It includes supervised, unsupervised, and RL for drug discovery. Google LLC has an open-access quantum ML framework for Python that can be used for varied applications. Various hybrid-quantum MLs are likely to be released soon for pharmaceutical applications.

The crux

AI and quantum technologies offer immense benefits for the pharmaceutical industry. Yet, the industry must approach the transition with care, ethical diligence, and a comprehensive understanding of drug development's inherent challenges and responsibilities. Aligning this technological integration with established principles and ethical guidelines is essential to harness its transformative power while upholding the trust and safety fundamental to the industry.

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