AI in Drug Discovery: Advancing Healthcare

Artificial Intelligence have demonstrated intelligent behavior through their ability to learn, communicate with its users and solve complex problems using highly sophisticated algorithms. AI in drug discovery technologies have continued to evolve rapidly, with several industries increasingly deploying such solutions across key aspects of value chain. Furthermore, artificial intelligence in drug discovery is also made with same idea of making tasks simpler to humans that earlier finds difficult and time consuming. Most of the industries are now disrupted from the new information age cutting-edge technologies. A number of initiatives have been undertaken in the AI in drug discovery domain in the past few decades, and multiple breakthrough instances were witnessed.

historical overview of artificial intelligence

With respect to healthcare sector, artificial intelligence has shown success in various aspects. Several studies conducted by pharmaceutical companies and research institutions have demonstrated the potential of AI in drug discovery, disease diagnosis, clinical drug development and remote patient monitoring. In fact, certain AI-based algorithms are known to outperform radiologists at detecting malignant tumors and are capable of guiding researchers to construct cohorts for clinical trials.  Further, it is estimated that application of AI in drug discovery can offer savings worth USD 150 billion by 2026. Various applications of AI in healthcare domain are highlighted below.

Application of AIDD in Drug Discovery

AI technology has transformed the drug discovery process. The process of discovery of potential drug candidate is a challenging part of drug development. As per requirements, the process of lead compound discovery usually takes more than five years of effort. In addition, the conventional approaches used in the drug discovery process have relatively higher risks of failure and are inefficient in generating valuable real-time insights. The drawbacks associated with using traditional drug discovery methods have led researchers to explore the potential of AI and cloud computing technologies in this field. It is worth mentioning that more than 40% of the pharmaceutical companies currently deploy AI-based solutions to address multiple business needs across drug discovery and lead identification processes. AI-powered drug discovery efforts have enabled pharmaceutical companies to identify novel, high-quality organic molecules that exhibit higher success rate for receiving approval. Further, the use of AI and machine learning techniques allows players to optimize safety and efficacy profiles of drug candidates, and forecast patient response to new drugs. AI-based platforms are also capable of evaluating drug pharmacology and transcriptomic data for drug repurposing.

It is worth mentioning that data integration, evolutionary modelling and pattern recognition using predictive AI models enables researchers to analyze large volumes of data in an efficient manner. However, certain concerns related to integration of AI in healthcare practices, including regulatory barriers, lack of specialists and strict data privacy regulations, have restricted its widespread adoption. In order to enable extensive application of AI in healthcare, it is important for regulatory authorities to recognize AI-based solutions and allow their integration. In addition, evaluating AI-based technologies through prospective studies can help researchers understand the true potential of AI in the drug development process. We are led to believe that the ongoing advances in the field of AI-based healthcare are likely to expand its market potential in the foreseen future.

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