Quantum Computing in Drug Discovery: Unleashing the Power

Drug discovery has always been a time-consuming process. On an average, it takes 10-15 years and investments worth USD 4-10 billion to commercially launch a drug. In order to address these concerns, developers are shifting their focus from traditional techniques to the use of novel discovery techniques. Quantum computing in drug discovery has emerged to be one of the prominent technologies that assists in selecting potential lead candidates, having the desired physiochemical and pharmacokinetic properties, without having to conduct extensive screening procedures. Quantum computing operates in a fundamentally distinct way from traditional binary computers, which rely on voltage-based principles. Unlike the discrete 0 and 1 states of individual bits in traditional computing. Quantum computing utilizes the principle of quantum mechanism, employing technique superconducting loops or ion traps. This enables them to achieve quantum superposition- a state where quantum bits (qubits) can exist in a simultaneous position of 0 and 1 states.

Various Clinical stages of Quantum Computing by Roots Analysis

Classification of Quantum Computing Approaches:

Currently, quantum computing employs a hybrid approach by using qubits for processing process large and complex problems in less time and more efficiently. With its ability to rapidly solve highly complex problems, quantum computing technology is overcoming the limitations of classical computers by working more precisely and giving quick solutions. With such approaches quantum computing has shown a potential to simulate very large and complex having a great impact on drug discovery and drug development in healthcare domain. There are various quantum computing software providers and quantum computing hardware providers that are collaborating on the development of quantum computing solutions for pharmaceutical applications. Key quantum computing hardware providers include Amazon, IBM, Google, Honeywell, Microsoft and many others that are using various quantum computing approaches.

Quantum Computing Approaches in drug discovery by Roots Analysis

Application of Quantum Computing in Drug Discovery Process:

  • Target Identification: This involves developing a new drug for target identification and validation. QC can be used to accurately predict the 3-D structures of proteins during target identification.
  • Target Validation: It includes the verification of a biological target that possesses therapeutic value. QC can be adopted to determine the structure-activity-relationship of analogs of the small molecule under evaluation against the target.
  • Hit Generation: Once a relevant molecular target has been discovered and validated, lead molecules are generated against the target in the hit discovery step. QC can be implemented to identify molecules that can interact with the target drug receptor.
  • Hit to Lead: This step is carried out to analyze the series of potential lead molecules (hits) obtained during hit discovery step. It involves using molecule hits from HTS and other approaches, such as pharmacokinetic modeling, QSAR and molecular docking. QC can be used to analyze both small molecules and biologics as it has the potential to deliver lead molecules that are easy and quick to optimize.
  • Lead Optimization: The process involves designing a drug candidate after the identification of initial lead compound. It helps to maintain the pharmacological properties of the lead. Quantum computing can be used to enhance the effectiveness and absorption of targeted biological molecules, thereby resulting in the reduction of toxicity.

Advantages of Quantum Computing in Drug Discovery Process:

  • Faster Computation: QC has the potential to perform computation at much higher pace. It uses quantum bits / qubits to measure and extract information, which analyzes large and complex molecules.
  • Cost Benefits: QC allows cost effective and timely analysis of complex molecules. By increasing the screening precision of the targeted molecules, it decreases the need for expensive lab testing.
  • Facilitate the Design of Clinical Trials: QC can be implemented to streamline clinical trials, reduce the timelines and improve the overall patient selection.
  • Reduce discovery timeline: QC accelerates the high throughput screening of multiple targeted molecules at a given time, thereby reducing the discovery timeline of drugs.
  • Enhanced modeling of complex molecules: Quantum Computing in Drug Discovery helps molecular modelers to achieve new levels of accuracy by modeling complex molecule interaction. Further, it can be used to target multiple therapeutic areas.

Challenges associated with the Use of Quantum Computing in Drug Discovery:

  • The monetary requirement in QC for establishing the necessary infrastructure and conducting R&D operations is high
  • Unavailability of skilled personnel in QC leading to delay and errors in drug discovery and development timelines
  • Shortage and delay in the manufacturing supply chain in biopharmaceutical industry across the globe
  • Integration of QC with other technologies may sometimes lead to inaccuracy due to incompatibility 
  • Due to the complexity associated with this domain, currently, there are not many established quantum computing software providers and quantum computing hardware providers.

Quantum Computing use cases:

Use Cases for Quantum Computing by Roots Analysis
  • Chemical Industry: QC use cases in chemical industry includes promising capability to increase the profit margins by discovering and designing optimal feed-stock routing, refining and marketing.
  • Cyber Security: QC use cases in cyber security involves the use of quantum computers as a potential solution to create or break the encryption for data protection.
  • Financial Modeling: QC use cases in financial modeling is gaining popularity in the financial sector due to structural similarities, increase in manifold, transactions and data processing.
  • Space Sciences: QC use cases in space sciences is attracting the mind by providing powerful solutions to solve more complex missions across the solar system, as well as advance research in the geoscience and aeronautics domain
  • Oil and Gas Industry: QC use cases in oil and gas industry have shown great potential by driving supply chain management and optimizing several upstream and downstream processes.
  • Weather Forecasting: QC use cases in weather forecasting has already shown the greater impact by analyzing huge data containing several dynamic variables, including air, temperature, pressure, and density. As a result, it can offer advantages in weather forecasting across both local and global scales.

Future Perspectives for Use of Quantum Computing in Drug Discovery Applications:

  • Quantum Computing in Drug Discovery has the potential to save high-cost investments as it reduces pre-clinical failure and accurately analyzes large and complex molecules in a shorter span of time
  • Various traditional drug discovery approaches were the preferred choice for drug development; however, due to cost and time-related benefits offered by quantum computing, the pharmaceutical industry is likely to witness a gradual shift
  • QC has enabled drug developers to address the market demand in a timely manner; therefore, a higher number of  industry stakeholders are anticipated to integrate this approach in drug discovery and development processes
  • As the adoption of quantum computing in drug discovery increases amongst innovators in the pharmaceutical and biopharmaceutical industries, the demand for quantum computers is likely to witness tremendous growth.

Also, refer to expert analysis done on Quantum Computing Software Providers by our in-house analysts panel in the key insights section.

You can also download the SAMPLE REPORT on Quantum Computing Drug Discovery Market report published by Roots Analysis.