Herein, we present JANUS, a genetic algorithm inspired by parallel tempering.  JANUS is augmented by a deep neural network that approximates molecular properties and relies on active learning for enhanced molecular sampling.  As neither most of the benchmarks nor the structure generator in JANUS account for synthesizability, a significant fraction of the proposed molecules is synthetically infeasible demonstrating that this aspect needs to be considered when evaluating the performance of molecular generative models.
JANUS
Herein, we present JANUS, a genetic algorithm inspired by parallel tempering.  JANUS is augmented by a deep neural network that approximates molecular properties and relies on active learning for enhanced molecular sampling.  As neither most of the benchmarks nor the structure generator in JANUS account for synthesizability, a significant fraction of the proposed molecules is synthetically infeasible demonstrating that this aspect needs to be considered when evaluating the performance of molecular generative models.
JANUS
It uses the SELFIES representation and the STONED algorithm for the efficient generation of structures, and outperforms other generative models in common inverse molecular design tasks achieving state-of-the-art target metrics across multiple benchmarks. 
SELFIES
It uses the SELFIES representation and the STONED algorithm for the efficient generation of structures, and outperforms other generative models in common inverse molecular design tasks achieving state-of-the-art target metrics across multiple benchmarks. 
STONED