Machine Learning and Artificial Intelligence in Pharmaceutical R&D: A New Era
Artificial intelligence (AI) and machine learning (ML) have emerged as the breakthrough technologies most expected to have a massive influence on pharmaceutical research and development over the last ten years (R&D). This is being primarily driven by groundbreaking advancements in computer technology, as well as the simultaneous dissipation of preceding limitations to the collection/processing of massive quantities of data. All whilst, the cost of getting new medicines to market and into the hands of patients has become absurdly high. Recognizing these challenges, AI/ML techniques are gaining traction in the pharmaceutical industry due to their automated nature, predictive capabilities, and anticipated increase in efficiency. ML approaches have been used in drug discovery with increasing sophistication over the last 15–20 years.
The latest area of pharmaceutical research where AI/ML is causing favourable disruption is ongoing clinical structure, behaviour, and analysis. Because of the elevated reliance on digital technology in clinical testing conduct, the COVID-19 pandemic may hasten the use of AI/ML in clinical trials. As we move toward a world where AI/ML is increasingly being integrated into R&D, it is critical to cut through the associated buzzwords and noise. It is also critical to recognise that the scientific method is not obsolete when drawing conclusions from data. This will aid in distinguishing between hope and hype and will lead to more informed decisions about the best use of AI/ML in drug development.
ML algorithms have been applied successfully to increase the probability of drug research achievement by introducing substantial improvements in multiple fields of R&D, including: identification of novel targeting, target-disease associations and understanding it, the selection of drug candidate, predictions of protein structure, the design of molecular compound as well as its optimization, understanding of mechanisms of diseases, developing new biomarkers; both prognostic and predictive, and biometrics data analysis from wearable devices. Because of the increased reliance on digital technology for data collection and site monitoring, the impact of the COVID-19 pandemic on clinical trial execution could potentially accelerate the use of AI and ML in clinical trial execution.