  Recently, deep generative models have revealed itself as a promising way of
performing de novo molecule design. However, previous research has focused
mainly on generating SMILES strings instead of molecular graphs. Although
current graph generative models are available, they are often too general and
computationally expensive, which restricts their application to molecules with
small sizes. In this work, a new de novo molecular design framework is proposed
based on a type sequential graph generators that do not use atom level
recurrent units. Compared with previous graph generative models, the proposed
method is much more tuned for molecule generation and have been scaled up to
cover significantly larger molecules in the ChEMBL database. It is shown that
the graph-based model outperforms SMILES based models in a variety of metrics,
especially in the rate of valid outputs. For the application of drug design
tasks, conditional graph generative model is employed. This method offers
higher flexibility compared to previous fine-tuning based approach and is
suitable for generation based on multiple objectives. This approach is applied
to solve several drug design problems, including the generation of compounds
containing a given scaffold, generation of compounds with specific
drug-likeness and synthetic accessibility requirements, as well as generating
dual inhibitors against JNK3 and GSK3$\beta$. Results show high enrichment
rates for outputs satisfying the given requirements.
