Recurrent Neural Networks (RNNs) trained with a set of molecules
represented as unique (canonical) SMILES strings, have shown the capacity to
create large chemical spaces of valid and meaningful structures. Herein we
perform an extensive benchmark on models trained with subsets of GDB-13 of
different sizes (1 million , 10,000 and 1,000), with different SMILES variants
(canonical, randomized and DeepSMILES), with two different recurrent cell types
(LSTM and GRU) and with different hyperparameter combinations. To guide the
benchmarks new metrics were developed that define the generated chemical space with
respect to its uniformity, closedness and completeness. Results show that
models that use LSTM cells trained with 1 million randomized SMILES, a
non-unique molecular string representation, are able to generate larger
chemical spaces than the other approaches and they represent more accurately
the target chemical space. Specifically, a model was trained with randomized
SMILES that was able to generate almost all molecules from GDB-13 with a quasi-uniform
probability. Models trained with smaller samples show an even bigger improvement
when trained with randomized SMILES models. Additionally, models were trained
on molecules obtained from ChEMBL and illustrate again that training with
randomized SMILES lead to models having a better representation of the
drug-like chemical space. Namely, the model trained with randomized SMILES was
able to generate at least double the amount of unique molecules with the same
distribution of properties comparing to one trained with canonical SMILES.
Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling
