IEEE Computational Intelligence Magazine - May 2022 - 22

and logP in samples generated using the standard Normal prior.
A large b value can lead to the sample SAS distribution
appearing at the right side of the actual SAS distribution. Interestingly,
the property distributions when using
b 00 . 1=
b # 01 .
do not
resemble actual data, implying that using a very small value of
b would lead to latent representations that deviate from the
standard Normal distribution. To summarize,
can prevent
the model training from posterior collapse and can form a
structured latent representation space which is useful for latent
space exploration using optimization techniques.
In addition to using the distributional similarity between
training samples and generated samples to inspect the learning
of FragVAE, we further investigated the training and test performance
in terms of reconstruction error (CE), KullbackLeibler
(KL) divergence, and mean squared error (MSE), i.e.,
the three terms in FragVAE's loss function. A test set of 6,400
molecules was produced through modifying some training
molecules by randomly replacing some of its fragments in a
training molecule with a fragment from the fragment vocabulary.
From the results in Table I, one can observe that, (1) a
smaller value of b allows a smaller reconstruction error and a
larger KL divergence thus richer latent space landscape, (2) a
larger value of b reduces stress on the reconstruction error but
pushes the posterior closer to the prior, (3) the property prediction
error is insensitive to the value change of ,b and (4) the

IEEE Computational Intelligence Magazine - May 2022

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