{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:49:56Z","timestamp":1783021796477,"version":"3.54.6"},"reference-count":73,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:00:00Z","timestamp":1657756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>We construct a protein\u2013protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration from published molecular generative models, uses the key features associated with PPI inhibitors as input and develops deep molecular generative models for de novo molecular design of PPI inhibitors. For the first time, quantitative estimation index for compounds targeting PPI was applied to the evaluation of the molecular generation model for de novo design of PPI-targeted compounds. Our results estimated that the generated molecules had better PPI-targeted drug-likeness and drug-likeness. Additionally, our model also exhibits comparable performance to other several state-of-the-art molecule generation models. The generated molecules share chemical space with iPPI-DB inhibitors as demonstrated by chemical space analysis. The peptide characterization-oriented design of PPI inhibitors and the ligand-based design of PPI inhibitors are explored. Finally, we recommend that this framework will be an important step forward for the de novo design of PPI-targeted therapeutics.<\/jats:p>","DOI":"10.1093\/bib\/bbac285","type":"journal-article","created":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T22:48:05Z","timestamp":1657752485000},"source":"Crossref","is-referenced-by-count":57,"title":["<i>De novo<\/i> molecular design with deep molecular generative models for PPI inhibitors"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8910-0929","authenticated-orcid":false,"given":"Jianmin","family":"Wang","sequence":"first","affiliation":[{"name":"The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University , Incheon 21983, Republic of Korea"},{"name":"Bioinformatics and Molecular Design Research Center (BMDRC) , Incheon 21983, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4969-3931","authenticated-orcid":false,"given":"Yanyi","family":"Chu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University , Shanghai 200030, P.R. 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