The Impact of Generative AI on the Efficiency and Accuracy of Drug Discovery

Drug discovery traditionally is a long and expensive process. It requires huge financial investment and can possibly take more than a decade to develop a single drug. In this paper, It has been explored how generative AI is changing this process. By using advanced models, generative AI can design new drug molecules, predict their properties, improve efficiency by reducing both time and cost required for research to get precise and accurate data. These models can generate drug candidates with better binding affinity and drug like properties, making the selection process more reliable. It is also being used across different stages of drug discovery, from identifying targets to monitoring drug safety. Although generative AI has great potential to transform drug discovery by making it faster, more efficient and accurate, there are still some challenges like the need for high quality data, lack of model transparency, regulatory concerns and more real world testing and comparison. These improvements are still needed for its full adoption.

Predictive Modeling, Cost Reduction, Time Optimization, Molecular Design, Drug Discovery, Generative AI

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