Drug Discovery using Quantum Simulation

Abstract

Health care Analysis spread wider throughout the globe enables proper structured resolutions for preventing the avalanche of health-related issues. The foremost goal lies in ensuring the proper safety and security of the patients. Talking about Patient safety, early diagnosis is the predominant key in finding any deadly diseases at an earlier stage, reducing the treatment costs, and increasing the overall survival rates. Also, an important factor that comes in parallel along with patient safety is ‘Drug Discovery’. In current day scenario, Pharma industries are striving a lot to improvise clinical development productivity by examining important potential strategies. The main point of optimization lies in the ‘Drug Discovery’ process. Drug Discovery plays a crucial role in clinical programs, as it helps towards diagnosis of illnesses. When a drug of satisfactory evidence is administered to a target it should ensure sufficient safety of the target. One of the foremost risks here is the availability of better drugs for a disease, or in other words appropriate potency should be ensured by the drug over a target. Despite investing very huge amount over a drug, 90% of it gets failed in clinical trials. In order to overcome the above-mentioned issue with Drug discovery, many pharma industries are relying on AI technologies to ensure, the drug actively engages the target and produce expected therapeutic effect. Along AI another promising technology which is set to revolutionize a wide range of health industry is Quantum Computing. As pharma industries are in urge to provide quality efficient and easily accessible drugs within reach, Quantum computing with built in Quantum mechanics perfectly fits in to derive proper compositions of drugs by trying out various drug-protein-gene interactions and in forecasting drug’s development using quantum algorithms and simulation techniques.