Inverse design of 3d molecular structures with conditional generative neural networks

@article{Gebauer2022InverseDO,
  title={Inverse design of 3d molecular structures with conditional generative neural networks},
  author={Niklas W. A. Gebauer and Michael Gastegger and Stefaan S. P. Hessmann and Klaus-Robert M{\"u}ller and Kristof T. Sch{\"u}tt},
  journal={Nature Communications},
  year={2022},
  volume={13}
}
The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional generative neural network for 3d molecular structures with specified chemical and structural properties. This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions, even in domains… 

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References

SHOWING 1-10 OF 82 REFERENCES

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.

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.

Inverse molecular design using machine learning: Generative models for matter engineering

Methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality, are reviewed.

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.

Symmetry-Aware Actor-Critic for 3D Molecular Design

A novel actor-critic architecture for 3D molecular design that can generate molecular structures unattainable with previous approaches is proposed by exploiting the symmetries of the design process through a rotationally covariant state-action representation based on a spherical harmonics series expansion.

LEARNING NEURAL GENERATIVE DYNAMICS FOR MOLECULAR CONFORMATION GENERATION

A novel probabilistic framework to generate valid and diverse conformations given a molecular graph, enjoying a high model capacity to estimate the multimodal conformation distribution and explicitly capturing the complex long-range dependencies between atoms in the observation space is proposed.

3D-Scaffold: Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds

The proposed 3D-Scaffold framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set and generalizes to new scaffolds, making it applicable to other domains.

3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds.

The deep learning model performs well with relatively small 3D structural training data and quickly learns to generalize to new scaffolds, highlighting its potential application to other domains for generating target specific candidates.

Molecular Geometry Prediction using a Deep Generative Graph Neural Network

A conditional deep generative graph neural network that learns an energy function by directly learning to generate molecular conformations that are energetically favorable and more likely to be observed experimentally in data-driven manner is proposed.

Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network

With AIMNet, a modular and chemically inspired deep neural network potential, a new dimension of transferability is shown: the ability to learn new targets using multimodal information from previous training.
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