{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T07:43:35Z","timestamp":1783410215133,"version":"3.54.6"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T00:00:00Z","timestamp":1712361600000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009122","name":"Ministry of Education","doi-asserted-by":"publisher","award":["RG14\/23"],"award-info":[{"award-number":["RG14\/23"]}],"id":[{"id":"10.13039\/100009122","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Discovering hit molecules with desired biological activity in a directed manner is a promising but profound task in computer-aided drug discovery. Inspired by recent generative AI approaches, particularly Diffusion Models (DM), we propose Graph Latent Diffusion Model (GLDM)\u2014a latent DM that preserves both the effectiveness of autoencoders of compressing complex chemical data and the DM\u2019s capabilities of generating novel molecules. Specifically, we first develop an autoencoder to encode the molecular data into low-dimensional latent representations and then train the DM on the latent space to generate molecules inducing targeted biological activity defined by gene expression profiles. Manipulating DM in the latent space rather than the input space avoids complicated operations to map molecule decomposition and reconstruction to diffusion processes, and thus improves training efficiency. Experiments show that GLDM not only achieves outstanding performances on molecular generation benchmarks, but also generates samples with optimal chemical properties and potentials to induce desired biological activity.<\/jats:p>","DOI":"10.1093\/bib\/bbae142","type":"journal-article","created":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T13:24:42Z","timestamp":1712409882000},"source":"Crossref","is-referenced-by-count":21,"title":["GLDM: hit molecule generation with constrained graph latent diffusion model"],"prefix":"10.1093","volume":"25","author":[{"given":"Conghao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University , 50 Nanyang Ave, 639798 , Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiok Hian","family":"Ong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University , 50 Nanyang Ave, 639798 , Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunsuke","family":"Chiba","sequence":"additional","affiliation":[{"name":"School of Chemistry , Chemical Engineering and Biotechnology, , 21 Nanyang Link, 637371 , Singapore"},{"name":"Nanyang Technological University , Chemical Engineering and Biotechnology, , 21 Nanyang Link, 637371 , Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jagath C","family":"Rajapakse","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University , 50 Nanyang Ave, 639798 , Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"issue":"9","key":"2024040613243159900_ref1","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1001\/jama.2020.1166","article-title":"Estimated research and development investment needed to bring a new medicine to market, 2009-2018","volume":"323","author":"Wouters","year":"2020","journal-title":"JAMA"},{"issue":"2","key":"2024040613243159900_ref2","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","article-title":"Automatic chemical design using a data-driven continuous representation of molecules","volume":"4","author":"G\u00f3mez-Bombarelli","year":"2018","journal-title":"ACS Cent Sci"},{"issue":"1\u20132","key":"2024040613243159900_ref3","doi-asserted-by":"crossref","first-page":"1700123","DOI":"10.1002\/minf.201700123","article-title":"Application of generative autoencoder in de novo molecular design","volume":"37","author":"Blaschke","year":"2018","journal-title":"Mol Inform"},{"key":"2024040613243159900_ref4","article-title":"Objective-Reinforced Generative Adversarial Networks (ORGAN) for sequence generation models","author":"Guimaraes","year":"2017"},{"issue":"9","key":"2024040613243159900_ref5","doi-asserted-by":"crossref","first-page":"3098","DOI":"10.1021\/acs.molpharmaceut.7b00346","article-title":"DruGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico","volume":"14","author":"Kadurin","year":"2017","journal-title":"Mol Pharm"},{"issue":"3","key":"2024040613243159900_ref6","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1021\/acs.jcim.8b00751","article-title":"De novo molecular design by combining deep autoencoder recurrent neural networks with generative topographic mapping","volume":"59","author":"Sattarov","year":"2019","journal-title":"J Chem Inf Model"},{"issue":"10","key":"2024040613243159900_ref7","doi-asserted-by":"crossref","first-page":"4398","DOI":"10.1021\/acs.molpharmaceut.8b00839","article-title":"Entangled conditional adversarial autoencoder for de novo drug discovery","volume":"15","author":"Polykovskiy","year":"2018","journal-title":"Mol Pharm"},{"key":"2024040613243159900_ref8","article-title":"Penalized variational autoencoder for molecular design","volume-title":"ChemRxiv","author":"Mohammadi","year":"2019"},{"key":"2024040613243159900_ref9","doi-asserted-by":"crossref","first-page":"269","DOI":"10.3389\/fphar.2020.00269","article-title":"Molecular generation for desired transcriptome changes with adversarial autoencoders","volume":"11","author":"Shayakhmetov","year":"2020","journal-title":"Front Pharmacol"},{"issue":"1","key":"2024040613243159900_ref10","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/s41467-019-13807-w","article-title":"De novo generation of hit-like molecules from gene expression signatures using artificial intelligence","volume":"11","author":"M\u00e9ndez-Lucio","year":"2020","journal-title":"Nat Commun"},{"issue":"1","key":"2024040613243159900_ref11","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"Smiles, a chemical language and information system. 1. 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