Abstract
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 available, current graph generative models are are often too general and computationally expensive. In this work, a new de novo molecular design framework is proposed based on a type of 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 has 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 highe flexibility and is suitable for generation based on multiple objectives. The results have demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold, compounds with specific drug-likeness and synthetic accessibility requirements, as well as dual inhibitors against JNK3 and GSK-3β.
Highlights
The ultimate goal of drug design is the discovery of new chemical entities with desirable pharmacological properties
The best performance in each metric is highlighted in italics face
For MolMP, this will hurt the overall performance of Kullback–Leibler divergence (DKL) and Jensen–Shannon divergence (DJS), while for MolRNN, this will inprove the performance for molecular weight, but will significantly decrease the performance of log-partition coefficient (LogP)
Summary
The ultimate goal of drug design is the discovery of new chemical entities with desirable pharmacological properties Achieving this goal requires medicinal chemists to explore the chemical space for new molecules, which is proved to be extremely difficult, mainly due to the size and complexity of the chemical space. Works have developed various algorithms to produce new molecular structures, such as atom based elongation or fragment based combination [3, 4] Those algorithms are often coupled with global optimization techniques such as ant Recent developments in deep learning [10] have shed new light on the area of de novo molecule generation. Segler et al [12] applied SMILES language model (LM) on the task of generating focused molecule libraries by fine-tuning the trained network with a smaller set of molecules with desirable properties. Gómez–Bombarelli et al [13] used variational autoencoder (VAE) [17] to generate drug-like compounds from ZINC database [18]
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