Molecular Graph Generation
26 papers with code • 3 benchmarks • 2 datasets
ZINC
MOSES
Latest papers with no code
Overcoming Order in Autoregressive Graph Generation
Graph generation is a fundamental problem in various domains, including chemistry and social networks.
Will More Expressive Graph Neural Networks do Better on Generative Tasks?
Moreover, we show that GCPN and GraphAF with advanced GNNs can achieve state-of-the-art results across 17 other non-GNN-based graph generative approaches, such as variational autoencoders and Bayesian optimisation models, on the proposed molecular generative objectives (DRD2, Median1, Median2), which are impor- tant metrics for de-novo molecular design.
Molecular Graph Generation by Decomposition and Reassembling
Designing molecular structures with desired chemical properties is an essential task in drug discovery and material design.
Conditional β-VAE for De Novo Molecular Generation
Deep learning has significantly advanced and accelerated de novo molecular generation.
Interpretable Molecular Graph Generation via Monotonic Constraints
Recent advances in deep graph generative models treat molecule design as graph generation problems which provide new opportunities toward the breakthrough of this long-lasting problem.
Target-aware Molecular Graph Generation
Building on the recent advantages of flow-based molecular generation models, we propose SiamFlow, which forces the flow to fit the distribution of target sequence embeddings in latent space.
Molecular Graph Generation via Geometric Scattering
We guide the latent representation of an autoencoder by capturing graph structure information with the geometric scattering transform and apply penalties that structure the representation also by molecular properties.
Spanning Tree-based Graph Generation for Molecules
In this paper, we explore the problem of generating molecules using deep neural networks, which has recently gained much interest in chemistry.
GraphEBM: Towards Permutation Invariant and Multi-Objective Molecular Graph Generation
In this work, we propose GraphEBM, a molecular graph generation method via energy-based models (EBMs), as an exploratory work to perform permutation invariant and multi-objective molecule generation.
Realistic molecule optimization on a learned graph manifold
Deep learning based molecular graph generation and optimization has recently been attracting attention due to its great potential for de novo drug design.
