Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge

@article{Ghaemi2022GenerativeES,
  title={Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge},
  author={Mohammad Sajjad Ghaemi and Karl Grantham and Isaac Tamblyn and Yifeng Li and Hsu Kiang Ooi},
  journal={ArXiv},
  year={2022},
  volume={abs/2204.02474}
}
Deploying generative machine learning techniques to generate novel chemical struc- tures based on molecular fingerprint representation has been well established in molecular design. Typically, sequential learning (SL) schemes such as hidden Markov models (HMM) and, more recently, in the sequential deep learning context, recurrent neural net-work (RNN) and long short-term memory (LSTM) were used extensively as generative models to discover unprecedented molecules. To this end, emission… 

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