IEEE Computational Intelligence Magazine - May 2022 - 18

smaller (107 versus 1060). (2) Identifying a fragment with certain
affinity to the target may lead to discovery of a pharmacophore.
(3) Fragment-based synthesis could be more efficient
than high-throughput screening. The majority of fragmentation
methods, which break a molecule into parts, are based on synthetic
accessibility. For example, RECAP is a method that
breaks bonds formed by chemical reactions [36]; BRICS generates
a more elaborated set of fragmentation rules along synthetically
accessible bonds and generates more fragments than
RECAP [37]. BRICS is used in [33] to chop a SMILES string
into several fragments. Then, fragment embeddings are produced
using Word2Vec [38]. Next, the sequences of fragments
are modeled by a GRU-based VAE. In our work, a multi-head
feedforward neural network component for predicting values
of properties is added to the original model such that the latent
representations can be regularized by properties of interest.
Additionally, we normalize the three loss terms using batch size,
and allow tuning of the weights among the loss terms. A crucial
implementation bug in the original VAE model was also corrected
(see Section S.2 in the Supplementary Materials). Hereafter,
this modified VAE model for fragments is named as
FragVAE whose architecture is displayed in Figure 2, using
an example molecule composed of 4 fragments.
Denoting a fragment-based molecule by x, its properties by
y, the stochastic encoder by ()p zx;
stochastic decoder by (| )xzpi
property predictor network by ()zf}
z
parameterized by ,z the
parameterized by ,i and the
parameterized by ,} the
loss function (to be minimized) of this DGM employed in
DEL is a weighted combination of three terms:
lp qp
f
zi}i zi
}
,, =- qz();zx [( )] + b
+ a qz();zx [( (),zy)],
EKL
EMSE
log xz
;; 
Latent
Representation
Vector
Encoder
GRUGRU
GRUGRU
Embedding
Fragment 1Fragment 2Fragment 3Fragment 4 
 Fragment 1′
MLP
Fragmentation
Property
Predictor
Properties
(e.g., QED, SAS, logP)
Fragment 2′ Fragment 3′ Fragment 4′
Encoder
GRU
Z
Softmax
Linear
GRU
Softmax
Linear
GRU
Softmax
Linear
GRU
Embedding
Softmax
Linear
GRU
Softmax
Linear
GRU
FIGURE 2 The deep generative model, FragVAE, integrated in DEL. The encoder encodes the sequence of discrete fragments into the latent representation
vector z which can be used as an individual for evolutionary operations in DEL. Vector z can be decoded to a sequence of discrete
fragments which are further assembled to a molecule. The property predictor implemented using a multi-head MLP is employed to regularize
the latent representation.
18 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2022

IEEE Computational Intelligence Magazine - May 2022

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