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Artificial intelligence is increasingly being integrated into drug discovery and development, generating both enthusiasm and skepticism. While AI platforms like AlphaFold have shown promise in predicting protein structures, the high failure rate of new drugs in clinical trials raises questions about AI's effectiveness in this field. The current drug development process, which can take over a decade and cost billions, may benefit from AI, but its true impact remains uncertain.
AI has been applied at various stages of drug development, from identifying targets to predicting toxicity. Despite some successes, the majority of AI-generated drug candidates still struggle in clinical trials, highlighting the limitations of current AI applications. Addressing the root causes of drug failures through improved AI strategies could streamline the lengthy approval process and enhance the chances of success.
• AI's potential in drug development is both promising and uncertain.
• 90% of drugs fail in clinical trials, highlighting challenges in AI applications.
Machine learning is used to predict drug candidates' dosage, safety, and efficacy based on various features.
AI-driven platforms like AlphaFold demonstrate the potential to accelerate drug development through protein structure prediction.
Clinical trials are the final testing phase for drug candidates, where most failures occur despite AI's involvement.
Bristol-Myers Squibb is involved in pharmaceutical research and development, utilizing AI to enhance drug discovery processes.
Celgene Corporation focuses on innovative therapies and employs AI to improve drug development efficiency.
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