• Corpus ID: 238226566

MOLUCINATE: A Generative Model for Molecules in 3D Space

  title={MOLUCINATE: A Generative Model for Molecules in 3D Space},
  author={Michael J. Arcidiacono and David Ryan Koes},
Recent advances in machine learning have enabled generative models for both optimization and de novo generation of drug candidates with desired properties. Previous generative models have focused on producing SMILES strings or 2D molecular graphs, while attempts at producing molecules in 3D have focused on reinforcement learning (RL), distance matrices, and pure atom density grids. Here we present MOLUCINATE (MOLecUlar ConvolutIoNal generATive modEl), a novel architecture that simultaneously… 

Figures from this paper


3DMolNet: A Generative Network for Molecular Structures
This work proposes a new approach to efficiently generate molecular structures that are not restricted to a fixed size or composition, based on the variational autoencoder which learns a translation-, rotation-, and permutation-invariant low-dimensional representation of molecules.
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
A generative neural network for 3d point sets that respects the rotational invariance of the targeted structures is introduced and demonstrated its ability to approximate the distribution of equilibrium structures using spatial metrics as well as established measures from chemoinformatics.
Learning to design drug-like molecules in three-dimensional space using deep generative models
This work introduces Ligand Neural Network (L-Net), a novel graph generative model for designing drug-like molecules with high-quality 3D structures that directly outputs the topological and 3D structure of molecules (including hydrogen atoms), without the need for additional atom placement or bond order inference algorithm.
Generating stable molecules using imitation and reinforcement learning
This work learns basic chemical rules from imitation learning on the GDB-11 database to create an initial model applicable for all stoichiometries, and applies the model to larger molecules to show how RL further refines the IL model in domains far from the training data.
Junction Tree Variational Autoencoder for Molecular Graph Generation
The junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network, which allows for incrementally expand molecules while maintaining chemical validity at every step.
MolGAN: An implicit generative model for small molecular graphs
MolGAN is introduced, an implicit, likelihood-free generative model for small molecular graphs that circumvents the need for expensive graph matching procedures or node ordering heuris-tics of previous likelihood-based methods.
Generating equilibrium molecules with deep neural networks
A novel, autoregressive, convolutional deep neural network architecture that generates molecular equilibrium structures by sequentially placing atoms in three-dimensional space is introduced.
Molecular Generative Model Based On Adversarially Regularized Autoencoder
This work proposes a new type of model based on an adversarially regularized autoencoder (ARAE), which basically uses latent variables like VAE, but the distribution of the latent variables is estimated by adversarial training like in GAN.
NeVAE: A Deep Generative Model for Molecular Graphs
A novel variational autoencoder for molecular graphs is proposed, whose encoder and decoder are specially designed to account for the above properties by means of several technical innovations.
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration