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Biopharmaceutical industries are making efforts to utilize AI to enhance the process of drug discovery, reduce expenses for research and development, decrease failure rates in clinical trials, and ultimately generate superior drugs. Immense statistics in life sciences and rapid growth of machine learning algorithms have led to the evolution of drug discovery-focused start-ups based on AI over the past few years. Recently, Pfizer and IBM Watson, Sanofi Genzyme and Recursion Pharmaceuticals have been declared much remarkable AI-biopharmaceutical alliance.
Artificial Intelligence will allow scientists to derive structured and unstructured data as never before from multiple sources. Strategic collaborations with AI-driven firms can help large pharmaceutical firms establish as part of their portfolios a robust AI-based pipeline and address new therapeutic areas.
Better drugs, more quickly discovered and delivered–AI sounds like an ideal lab partner. But while a chemistry-savvy AI system may in some respects outperform a human chemist by dealing with problems that the human mind is struggling with, it is no a silver bullet. In fact, the AI-assisted drug design expectations are too high. Machine intelligence will only learn meaningful relationships between drug molecules and their physiological effects when presented with appropriate data? AI models endorse their decision-making in drug discovery, but combining Artificial intelligence into an automated drug design process will require new thinking: it will change the setting, just as recent years' software and technology have done much more quickly in predicting properties to a high degree of accuracy than in a laboratory without automation.
AI seems to transform the healthcare field's future, but it still has to make an impact. There are currently no AI-inspired drugs approved by the FDA on the market. It is also vital to understand that although AI-based data analytics may introduce novelty in any drug discovery and development process, it cannot be used as a substitute for experimental processes which include chemical synthesis, clinical trials, and regulatory approvals. Nevertheless, AI may upgrade and accelerate research and development efforts, decrease the time and expense of early drug discovery, and assist in predicting potential toxicity risks/side effects in late-stage trials that may be very valuable in avoiding terrible events in clinical trials.