Generative AI for Drug Discovery: Accelerating Pharmaceutical Research with Deep Learning
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Abstract
The application of generative AI in drug discovery has the potential to revolutionize pharmaceutical research by expediting the identification of novel compounds. This paper presents a generative adversarial network (GAN)-based approach to molecular generation, combined with reinforcement learning techniques for drug optimization. We evaluate the performance of our model in predicting molecular properties, docking affinities, and toxicity profiles using benchmark datasets. The study highlights how generative AI can reduce time and cost in drug development while addressing challenges related to model generalization, data bias, and regulatory compliance.
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References
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