Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge. (arXiv:2204.02474v1 [q-bio.BM])

Deploying generative machine learning techniques to generate novel chemical
structures 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 network (RNN) and long short-term memory
(LSTM) were used extensively as generative models to discover unprecedented
molecules. To this end, emission probability between two states of atoms plays
a central role without considering specific chemical or …

arxiv bio design domain knowledge esl knowledge learning