.png){kind=icon}
Return
Molecular design with automated quantum computing-based deep learning and optimization
.png){kind=icon}
OA
AI
DOI：10.1038/s41524-023-01099-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Computer-aided design of novel molecules and compounds is a challenging task that can be addressed with quantum computing (QC) owing to its notable advances in optimization and machine learning. Here, we use QC-assisted learning and optimization techniques implemented with near-term QC devices for molecular property prediction and generation tasks. The proposed probabilistic energy-based deep learning model trained in a generative manner facilitated by QC yields robust latent representations of molecules, while the proposed data-driven QC-based optimization framework performs guided navigation of the target chemical space by exploiting the structure-property relationships captured by the energy-based model. We demonstrate the viability of the proposed molecular design approach by generating several molecular candidates that satisfy specific property target requirements. The proposed QC-based methods exhibit an improved predictive performance while efficiently generating novel molecules that accurately fulfill target conditions and exemplify the potential of QC for automated molecular design, thus accentuating its utility.
Keywords:
GENETIC ALGORITHM
PREDICTION
DISCOVERY
LIBRARY
SEARCH
Journal
IF:
11.9
Papers:
2.4K
Citations:
1.7W
Organization
Cited Papers
Molecular generative model based on conditional variational autoencoder for de novo molecular design
OAAI
OAAIShareSaveShareSaveShareSaveShareSaveA graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space.png)
CHEMICAL SCIENCEOAAI
CHEMICAL SCIENCEIF7.4
OAAIShareSaveShareSaveShareSave
View more