Models capable of generating novel molecular structures recently got a lot of attention.
This is an attempt to track the relevant publications regarding the topic. Please let me know if I forgot something.
To get an overview you can have a look at the following review articles:
Deep learning for molecular generation and optimization - a review of the state of the art (2019)
Inverse molecular design using machine learning: Generative models for matter engineering (2018)
Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery (2018)
GuacaMol: Benchmarking Models for De Novo Molecular Design (2018)
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models (2018)
Conditional Molecular Design with Deep Generative Models (2019)
Mol-CycleGAN - a generative model for molecular optimization (2019)
Exploring the GDB-13 chemical space using deep generative models (2019)
Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks (2018)
Molecular generative model based on conditional variational autoencoder for de novo molecular design (2018)
Deep reinforcement learning for de novo drug design (2018)
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search (2018)
Learning Multimodal Graph-to-Graph Translation for Molecule Optimization (2018)
Constrained Graph Variational Autoencoders for Molecule Design (2018)
Generative Recurrent Networks for De Novo Drug Design (2018)
Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design (2018)
Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies (2018)
De Novo Design of Bioactive Small Molecules by Artificial Intelligence (2018)
Molecular Hypergraph Grammar with its Application to Molecular Optimization (2018)
h-detach: Modifying the LSTM Gradient Towards Better Optimization (2018)
NeVAE: A Deep Generative Model for Molecular Graphs (2018)
Reinforced Adversarial Neural Computer for de Novo Molecular Design (2018)
De Novo Generation of Hit-like Molecules from Gene Expression Signatures Using Artificial Intelligence (2018)
Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery (2018)
Improving Chemical Autoencoder Latent Space and Molecular De Novo Generation Diversity with Heteroencoders (2018)
Learning Deep Generative Models of Graphs (2018)
Optimization of Molecules via Deep Reinforcement Learning (2018)
Junction Tree Variational Autoencoder for Molecular Graph Generation (2018)
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models (2018)
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (2018)
MolGAN: An implicit generative model for small molecular graphs (2018)
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (2018)
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation (2018)
Adversarial Threshold Neural Computer for Molecular de Novo Design (2018)
Syntax-Directed Variational Autoencoder for Structured Data (2018)
Learning Continuous and Data-Driven Molecular Descriptors by Translating Equivalent Chemical Representations (2018)
Multi-Objective De Novo Drug Design with Conditional Graph Generative Model (2018)
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders (2018)
Deep Generative Models for Molecular Science (2018)
Designing Random Graph Models Using Variational Autoencoders With Applications to Chemical Design (2018)
Prototype-Based Compound Discovery Using Deep Generative Models (2018)
Mol-CycleGAN - a generative model for molecular optimization (2018)
Population-based de novo molecule generation, using grammatical evolution (2018)
Application of generative autoencoder in de novo molecular design (2017)
Constrained Bayesian Optimization for Automatic Chemical Design (2017)
Grammar Variational Autoencoder (2017)
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models (2017)
In silico generation of novel, drug-like chemical matter using the LSTM neural network (2017)
druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico (2017)
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks (2017)
De novo drug design with deep generative models : an empirical study (2017)
Molecular de-novo design through deep reinforcement learning (2017)
ChemTS: an efficient python library for de novo molecular generation (2017)
Molecular Generation with Recurrent Neural Networks (RNNs) (2017)
Bayesian molecular design with a chemical language model (2017)
Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC) (2017)
Learning a Generative Model for Validity in Complex Discrete Structures (2017)
ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? (2017)
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control (2016)
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology (2016)
Molpher: a software framework for systematic chemical space exploration (2014)
Generative Models for Chemical Structures (2010)