Leveraging the Power of Quantum Computing and Machine Learning to Disrupt Drug Development
Samantha Chen
Universitas Nusantara, Indonesia
Aditya Rajawali
Universitas Puncak Jaya, Indonesia
Keywords: Drug development, Quantum computing, Machine learning, Molecular simulation, Preclinical studies, Clinical trials
Abstract
Drug development is a lengthy and expensive process, taking 10-15 years and costing over $2 billion per approved drug. Two emerging technologies - quantum computing and machine learning - have the potential to significantly accelerate and improve the drug discovery and development pipeline. In this paper, we discuss how these technologies can be applied to key challenges in drug development: target identification, molecular design, preclinical studies, and clinical trials. Quantum computing can simulate chemical reactions and protein folding at an atomic level to reveal new drug targets. Machine learning excels at analyzing large and complex biological and chemical datasets to uncover patterns and generate predictive models. Together, they enable high-throughput in silico screening of drug candidates. Quantum machine learning integrates both approaches to develop more powerful algorithms. In preclinical studies, quantum simulations can determine drug toxicity and machine learning can optimize trial design. For clinical trials, machine learning can identify eligible patients, minimize dropout rates, and improve trial efficiency. Overall, these technologies can reduce the time and costs of each phase, as well as failure rates between phases. However, there are still significant technical and adoption challenges that must be overcome. If harnessed properly, quantum computing and machine learning have the potential to accelerate drug discovery and development, leading to faster delivery of safe and effective medicines to patients.
Author Biography
Aditya Rajawali, Universitas Puncak Jaya, Indonesia
