Balancing Exploration and Exploitation: Disentangled $\beta$-CVAE in De
Novo Drug Design
- URL: http://arxiv.org/abs/2306.01683v2
- Date: Fri, 18 Aug 2023 03:11:43 GMT
- Title: Balancing Exploration and Exploitation: Disentangled $\beta$-CVAE in De
Novo Drug Design
- Authors: Guang Jun Nicholas Ang, De Tao Irwin Chin and Bingquan Shen
- Abstract summary: We propose a molecular-graph $beta$-CVAE model for de novo drug design.
We optimised the octanol-water partition coefficient (ClogP), molar refractivity (CMR), quantitative estimate of drug-likeness (QED), and synthetic accessibility score (SAS)
Our model generated an average of 30.07% $pm$ 0.01% molecules for both desired properties.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep generative models have recently emerged as a promising de novo drug
design method. In this respect, deep generative conditional variational
autoencoder (CVAE) models are a powerful approach for generating novel
molecules with desired drug-like properties. However, molecular graph-based
models with disentanglement and multivariate explicit latent conditioning have
not been fully elucidated. To address this, we proposed a molecular-graph
$\beta$-CVAE model for de novo drug design. Here, we empirically tuned the
value of disentanglement and assessed its ability to generate molecules with
optimised univariate- or-multivariate properties. In particular, we optimised
the octanol-water partition coefficient (ClogP), molar refractivity (CMR),
quantitative estimate of drug-likeness (QED), and synthetic accessibility score
(SAS). Results suggest that a lower $\beta$ value increases the uniqueness of
generated molecules (exploration). Univariate optimisation results showed our
model generated molecular property averages of ClogP = 41.07% $\pm$ 0.01% and
CMR 66.76% $\pm$ 0.01% by the Ghose filter. Multivariate property optimisation
results showed that our model generated an average of 30.07% $\pm$ 0.01%
molecules for both desired properties. Furthermore, our model improved the QED
and SAS (exploitation) of molecules generated. Together, these results suggest
that the $\beta$-CVAE could balance exploration and exploitation through
disentanglement and is a promising model for de novo drug design, thus
providing a basis for future studies.
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