Quantum computing leverages AI to discover new cancer drug candidates

The discovery of new cancer-fighting drugs has long been hindered by certain proteins considered “undruggable,” but researchers have now developed an innovative approach combining quantum computing with artificial intelligence. Scientists at the University of Toronto and Insilico Medicine have demonstrated a new method for creating anti-cancer molecules that target previously unreachable proteins, marking a significant advancement in drug discovery.

The big picture: A groundbreaking study published in Nature Biotechnology showcases how a hybrid quantum-classical AI system can generate potential drug candidates targeting the KRAS gene, a major driver of multiple cancer types.

• The research team developed a hybrid quantum-classical generative AI algorithm that successfully created small molecules targeting KRAS mutations
• KRAS gene mutations are responsible for approximately one-third of all cancers, including 90% of pancreatic cancers, 40% of colorectal cancers, and 32% of lung cancers
• Two particularly promising drug candidates were identified among the top 15 molecules selected for laboratory assessment

Technical innovation: The hybrid approach combines classical computing methods with quantum computing capabilities to achieve superior results in drug candidate generation.

• Researchers created a training dataset of over 1.1 million molecules, including 250,000 screened compounds and 650 validated KRAS inhibitors
• The hybrid model demonstrated a 21.5% higher success rate in meeting drug design criteria compared to conventional machine learning models
• Quantum computing elements allow for simultaneous processing of multiple molecular states, while classical computing handles other aspects of the algorithm

Expert insights: Industry leaders view this development as a crucial steppingstone for quantum computing applications in drug discovery.

• Alex Zhavoronkov, founder and CEO of Insilico Medicine, compares quantum computing’s current state to where generative AI in chemistry was in 2015-2016
• Major tech companies including Microsoft and Amazon are expected to scale up quantum computing services by 2026-27
• Commercial quantum computing services are already available in some regions, particularly in China

Cancer context: The research addresses a critical global health challenge that affects millions of people worldwide.

• Cancer is projected to affect 35 million cases globally by 2050, according to the American Cancer Society
• Approximately one in five Americans will develop cancer in their lifetime
• While cancer has genetic origins, 90-95% of cases are non-hereditary and influenced by lifestyle and environmental factors

Future implications: While this research represents an important proof-of-concept, its real-world impact on cancer treatment remains to be determined.

• The success of this hybrid quantum-classical approach could pave the way for targeting other previously “undruggable” proteins
• As quantum computing capabilities expand and become more accessible, similar drug discovery methods may become more prevalent
• The development of effective KRAS inhibitors could significantly impact treatment options for multiple types of cancer

A quantum leap forward: While the researchers are careful not to oversell their results, this study demonstrates the viable integration of quantum computing in drug discovery workflows, potentially opening new pathways for developing cancer treatments that were previously considered impossible.