  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 physical properties.
Lack of supervised domain knowledge can mislead the learning procedure to be
relatively biased to the prevalent molecules observed in the training data that
are not necessarily of interest. We alleviated this drawback by augmenting the
training data with domain knowledge, e.g. quantitative estimates of the
drug-likeness score (QEDs). As such, our experiments demonstrated that with
this subtle trick called enriched sequential learning (ESL), specific patterns
of particular interest can be learnt better, which led to generating de novo
molecules with ameliorated QEDs.
