{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Abbasi M"],"funding":["Fundação para a Ciência e a Tecnologia"],"pagination":["40"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9233801"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(1)"],"pubmed_abstract":["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 la"],"journal":["Journal of cheminformatics"],"pubmed_title":["Designing optimized drug candidates with Generative Adversarial Network."],"pmcid":["PMC9233801"],"funding_grant_id":["CENTRO-01-0145-FEDER-029266"],"pubmed_authors":["Pereira TC","Brito RMM","Arrais JP","Santos BP","Abbasi M","Oliveira JL","Simoes CJV","Sofia R","Monteiro NRC","Ribeiro B"],"additional_accession":[]},"is_claimable":false,"name":"Designing optimized drug candidates with Generative Adversarial Network.","description":"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 la","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jun","modification":"2025-04-26T02:47:26.584Z","creation":"2024-10-18T20:23:46.184Z"},"accession":"S-EPMC9233801","cross_references":{"pubmed":["35754029"],"doi":["10.1186/s13321-022-00623-6"]}}