Molecular Graph Generation

26 papers with code • 3 benchmarks • 2 datasets

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Most implemented papers

Junction Tree Variational Autoencoder for Molecular Graph Generation

wengong-jin/icml18-jtnn ICML 2018

We evaluate our model on multiple tasks ranging from molecular generation to optimization.

Optimization of Molecules via Deep Reinforcement Learning

google-research/google-research 19 Oct 2018

We present a framework, which we call Molecule Deep $Q$-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double $Q$-learning and randomized value functions).

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

molecularsets/moses 29 Nov 2018

Generative models are becoming a tool of choice for exploring the molecular space.

GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

pfnet-research/chainer-chemistry 28 May 2019

We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model.

Molecule Generation by Principal Subgraph Mining and Assembling

thunlp-mt/ps-vae 29 Jun 2021

Molecule generation is central to a variety of applications.

FastFlows: Flow-Based Models for Molecular Graph Generation

aspuru-guzik-group/selfies 28 Jan 2022

We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules.

Geometry-Complete Diffusion for 3D Molecule Generation and Optimization

bioinfomachinelearning/bio-diffusion 8 Feb 2023

Importantly, we demonstrate that the geometry-complete denoising process of GCDM learned for 3D molecule generation enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn.

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

bowenliu16/rl_graph_generation NeurIPS 2018

Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.

MolecularRNN: Generating realistic molecular graphs with optimized properties

shubhamguptaiitd/GraphRNN 31 May 2019

Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development.

High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

huawei-noah/hebo 7 Jun 2021

We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.