biostudies-literatureAbbasi MFundação para a Ciência e a Tecnologia40https://www.ebi.ac.uk/biostudies/studies/S-EPMC9233801biostudies-literatureUnknown14(1)Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder-Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder-Decoder converts the string notations of molecules into laJournal of cheminformaticsDesigning optimized drug candidates with Generative Adversarial Network.PMC9233801CENTRO-01-0145-FEDER-029266Pereira TCBrito RMMArrais JPSantos BPAbbasi MOliveira JLSimoes CJVSofia RMonteiro NRCRibeiro BfalseDesigning optimized drug candidates with Generative Adversarial Network.Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder-Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder-Decoder converts the string notations of molecules into la2022-01-01T00:00:00Z2022 Jun2025-04-26T02:47:26.584Z2024-10-18T20:23:46.184ZS-EPMC92338013575402910.1186/s13321-022-00623-6