Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Jaechang Lim"'
Publikováno v:
Advanced Science, Vol 10, Iss 8, Pp n/a-n/a (2023)
Abstract Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real‐world applications. Here, a novel graph‐based condition
Externí odkaz:
https://doaj.org/article/a49e26893ac2462ebc0eef0a6e9bcf00
Molecular generative model based on conditional variational autoencoder for de novo molecular design
Publikováno v:
Journal of Cheminformatics, Vol 10, Iss 1, Pp 1-9 (2018)
Abstract We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof o
Externí odkaz:
https://doaj.org/article/1240f5a37f354b07b37892209308ef3b
Autor:
Changjun Park, Jinhee Lee, Taehyoung Kim, Jaechang Lim, Jeyoung Park, Woo Youn Kim, Sang Youl Kim
Publikováno v:
Molecules, Vol 25, Iss 2, p 402 (2020)
Here, we report the formation of homochiral supramolecular thin film from achiral molecules, by using circularly polarized light (CPL) only as a chiral source, on the condition that irradiation of CPL does not induce a photochemical change of the ach
Externí odkaz:
https://doaj.org/article/815a1f18f69e44338613e098e194baad
Publikováno v:
Chemical Science
Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e33ffdb02eb9e852b1e9377141e42cd
https://doi.org/10.1039/d1sc05248a
https://doi.org/10.1039/d1sc05248a
Publikováno v:
Advanced Science. 10
Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real-world applications. Here, a novel graph-based conditional generative
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f314651acb26073bdb5860bf4444d8a
https://doi.org/10.1002/advs.202206674
https://doi.org/10.1002/advs.202206674
Publikováno v:
Chemical Science
Searching for new molecules in areas like drug discovery often starts from the core structures of known molecules. Such a method has called for a strategy of designing derivative compounds retaining a particular scaffold as a substructure. On this ac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67e213351021f085951c7bb40afac617
https://doi.org/10.1039/c9sc04503a
https://doi.org/10.1039/c9sc04503a
Publikováno v:
Journal of Chemical Information and Modeling. 60:29-36
Deep generative models are attracting great attention as a new promising approach for molecular design. A variety of models reported so far are based on either a variational autoencoder (VAE) or a generative adversarial network (GAN), but they have l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c7fadfaea19fa7b2bd90825e6de95f4
https://doi.org/10.1021/acs.jcim.9b00694
https://doi.org/10.1021/acs.jcim.9b00694
Publikováno v:
Journal of Chemical Information and Modeling. 59:3981-3988
We propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. We introduce a distance-aware graph attention algorithm to differentiate various types of intermolecular interactions. Furthermore, we extr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a50921b6323955f32732d8e5942d8d2
https://doi.org/10.1021/acs.jcim.9b00387
https://doi.org/10.1021/acs.jcim.9b00387
Recently, deep neural network (DNN)-based drug-target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models' insufficient generalization remains a challenging problem in the practice of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac6a7070ec22d28113e1efb967085e2f
Publikováno v:
Computer Physics Communications. 230:21-26
Real-space methods have not been suitable for hybrid density functional calculations due to high cost coming from the nonlocality of Fock operator. Here we propose a practical approach for fast computation. The key is to use a strictly local Kohn–S
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3652ba419123710a7db0f154e734a347
https://doi.org/10.1016/j.cpc.2018.04.010
https://doi.org/10.1016/j.cpc.2018.04.010