De novo molecule design towards biased properties via a deep generative framework and iterative transfer learning
Kianoosh Sattari, Dawei Li, Bhupalee Kalita, Yunchao Xie, Fatemeh Barmaleki Lighvan, Olexandr Isayev, Jian Lin
Highlight
The RRCGAN, validated through DFT, demonstrates success in generating chemically valid molecules targeting energy gap values with 75% of the generated molecules have RE of <20% of the targeted values.
Abstract
The RRCGAN, validated through DFT, demonstrates success in generating chemically valid molecules targeting energy gap values with 75% of the generated molecules have RE of <20% of the targeted values.
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Cite This Paper
@article{Sattari2024,
author = {Sattari, Kianoosh and Li, Dawei and Kalita, Bhupalee and Xie, Yunchao and Lighvan, Fatemeh Barmaleki and Isayev, Olexandr and Lin, Jian},
title = {De novo molecule design towards biased properties via a deep generative framework and iterative transfer learning},
year = {2024},
journal = {Digital Discovery},
volume = {3},
number = {2},
pages = {410--421},
doi = {10.1039/d3dd00210a},
keywords = {generative models, molecular design, transfer learning},
researchAreas = {generative-ai},
researchArea = {generative-ai},
highlight = {The RRCGAN, validated through DFT, demonstrates success in generating chemically valid molecules targeting energy gap values with 75% of the generated molecules have RE of <20% of the targeted values.}
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