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Decoding Disease: GANs in AI-Driven Medical Diagnosis

Decoding Disease: GANs in AI-Driven Medical Diagnosis

Kiran Sree Pokkuluri, Usha Devi N, Alex Khang
Copyright: © 2024 |Pages: 11
ISBN13: 9798369332184|EISBN13: 9798369332191
DOI: 10.4018/979-8-3693-3218-4.ch010
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MLA

Pokkuluri, Kiran Sree, et al. "Decoding Disease: GANs in AI-Driven Medical Diagnosis." AI-Driven Innovations in Digital Healthcare: Emerging Trends, Challenges, and Applications, edited by Alex Khang, IGI Global, 2024, pp. 200-210. https://doi.org/10.4018/979-8-3693-3218-4.ch010

APA

Pokkuluri, K. S., Usha Devi N, & Khang, A. (2024). Decoding Disease: GANs in AI-Driven Medical Diagnosis. In A. Khang (Ed.), AI-Driven Innovations in Digital Healthcare: Emerging Trends, Challenges, and Applications (pp. 200-210). IGI Global. https://doi.org/10.4018/979-8-3693-3218-4.ch010

Chicago

Pokkuluri, Kiran Sree, Usha Devi N, and Alex Khang. "Decoding Disease: GANs in AI-Driven Medical Diagnosis." In AI-Driven Innovations in Digital Healthcare: Emerging Trends, Challenges, and Applications, edited by Alex Khang, 200-210. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-3218-4.ch010

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Abstract

Generative adversarial networks (GANs) are a cutting-edge technique in drug development that provides a fresh method for optimising and designing molecules. Under this paradigm, GANs transform the conventional drug development pipeline by learning from existing datasets to produce molecular structures with desired features. This work emphasises how artificial intelligence might transform and accelerate the discovery of novel therapeutic compounds and create data-driven drugs. The use of GANs in drug discovery is examined in this chapter, with a focus on their contributions to de novo drug design, property prediction, and molecular generation. GANs speed up the exploration of chemical space and make it easier to find promising therapeutic candidates by enabling the construction of diverse and chemically viable molecular structures. The chapter goes into further detail about the assessment criteria that are essential for determining the caliber, variety, and usefulness of molecules generated by GANs.

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