Molecular Graph Generation
22 papers with code • 3 benchmarks • 2 datasets
Most implemented papers
Junction Tree Variational Autoencoder for Molecular Graph Generation
We evaluate our model on multiple tasks ranging from molecular generation to optimization.
Optimization of Molecules via Deep Reinforcement Learning
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.
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
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
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.
Molecule Generation by Principal Subgraph Mining and Assembling
Molecule generation is central to a variety of applications.
FastFlows: Flow-Based Models for Molecular Graph Generation
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
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.