Multi-Objective De Novo Drug Design with Conditional Graph Generative Model

TL;DR

This paper introduces a scalable, graph-based generative model for de novo drug molecule design that outperforms SMILES-based models, especially in validity and multi-objective generation, demonstrated on various drug design tasks.

Contribution

A novel sequential graph generator without atom-level recurrent units, scaled for larger molecules, and a flexible conditional model for multi-objective drug design tasks.

Findings

Outperforms SMILES models in validity and diversity metrics.

Successfully generates molecules with specific scaffolds and properties.

High enrichment rates for target-specific drug design outputs.

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 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.