Molecular Graph Generation
26 papers with code • 3 benchmarks • 2 datasets
ZINC
MOSES
Most implemented papers
Junction Tree Variational Autoencoder for Molecular Graph Generation
wengong-jin/icml18-jtnn
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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
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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
Generative models are becoming a tool of choice for exploring the molecular space.
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
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
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Molecule generation is central to a variety of applications.
FastFlows: Flow-Based Models for Molecular Graph Generation
aspuru-guzik-group/selfies
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We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules.
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
bowenliu16/rl_graph_generation
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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
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
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We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.
Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
bioinfomachinelearning/bio-diffusion
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However, such methods are unable to learn important geometric and physical properties of 3D molecules during molecular graph generation, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which negatively impacts their ability to effectively scale to datasets of large 3D molecules.
