  SMILES is a linear representation of chemical structures which encodes the
connection table, and the stereochemistry of a molecule as a line of text with
a grammar structure denoting atoms, bonds, rings and chains, and this
information can be used to predict chemical properties. Molecular fingerprints
are representations of chemical structures, successfully used in similarity
search, clustering, classification, drug discovery, and virtual screening and
are a standard and computationally efficient abstract representation where
structural features are represented as a bit string. Both SMILES and molecular
fingerprints are different representations for describing the structure of a
molecule. There exist several predictive models for learning chemical
properties based on either SMILES or molecular fingerprints. Here, our goal is
to build predictive models that can leverage both these molecular
representations. In this work, we present CheMixNet -- a set of neural networks
for predicting chemical properties from a mixture of features learned from the
two molecular representations -- SMILES as sequences and molecular fingerprints
as vector inputs. We demonstrate the efficacy of CheMixNet architectures by
evaluating on six different datasets. The proposed CheMixNet models not only
outperforms the candidate neural architectures such as contemporary fully
connected networks that uses molecular fingerprints and 1-D CNN and RNN models
trained SMILES sequences, but also other state-of-the-art architectures such as
Chemception and Molecular Graph Convolutions.
