# Application of generative autoencoder in de novo molecular design

arXiv: https://arxiv.org/abs/1711.07839 · HTML: https://mlsanity.com/paper/1711.07839/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2017.

## Built on by (most cited first, 30 of 32)

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- [Domain-Agnostic Molecular Generation with Chemical Feedback](https://mlsanity.com/paper/2301.11259/) (2023)
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- [Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention](https://mlsanity.com/paper/2103.00213/) (2021)
- [DrugLLM: Open Large Language Model for Few-shot Molecule Generation](https://mlsanity.com/paper/2405.06690/) (2024)
- [Polygrammar: Grammar for Digital Polymer Representation and Generation](https://mlsanity.com/paper/2105.05278/) (2021)
- [Autoencoding Hyperbolic Representation for Adversarial Generation](https://mlsanity.com/paper/2201.12825/) (2022)
- [Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design](https://mlsanity.com/paper/2105.02637/) (2021)
- [Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations](https://mlsanity.com/paper/2106.04464/) (2021)
- [Graph Machine Learning for Design of High-Octane Fuels](https://mlsanity.com/paper/2206.00619/) (2022)
- [Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model](https://mlsanity.com/paper/2112.01625/) (2021)
- [Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge](https://mlsanity.com/paper/2204.02474/) (2022)
- [RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design](https://mlsanity.com/paper/2305.11699/) (2023)
- [Overcoming Order in Autoregressive Graph Generation](https://mlsanity.com/paper/2402.03387/) (2024)
- [NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Libraries](https://mlsanity.com/paper/2407.03428/) (2024)
- [Phenotypic Profile-Informed Generation of Drug-Like Molecules via Dual-Channel Variational Autoencoders](https://mlsanity.com/paper/2506.02051/) (2025)
- [Improving RNA Secondary Structure Design using Deep Reinforcement Learning](https://mlsanity.com/paper/2111.04504/) (2021)
- [Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia](https://mlsanity.com/paper/2406.04039/) (2024)
- [GP-MoLFormer-Sim: Test Time Molecular Optimization through Contextual Similarity Guidance](https://mlsanity.com/paper/2506.05628/) (2025)
- [Improving Molecule Properties Through 2-Stage VAE](https://mlsanity.com/paper/2212.02750/) (2022)
- [Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning](https://mlsanity.com/paper/2309.15130/) (2023)
- [TSSR: Two-Stage Swap-Reward-Driven Reinforcement Learning for Character-Level SMILES Generation](https://mlsanity.com/paper/2601.04521/) (2026)
- [Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds](https://mlsanity.com/paper/2103.06867/) (2021)
- [Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision](https://mlsanity.com/paper/2209.10029/) (2022)
- [CHA2: CHemistry Aware Convex Hull Autoencoder Towards Inverse Molecular Design](https://mlsanity.com/paper/2302.11000/) (2023)
- [Objective-Agnostic Enhancement of Molecule Properties via Multi-Stage VAE](https://mlsanity.com/paper/2308.13066/) (2023)
- [Learning to generate feasible graphs using graph grammars](https://mlsanity.com/paper/2501.06003/) (2025)
- [A Comprehensive Benchmarking Platform for Deep Generative Models in Molecular Design](https://mlsanity.com/paper/2505.12848/) (2025)

## Abstract (arXiv)

A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used to map molecule structures into a continuous latent space and vice versa and their performance as structure generator was assessed. Our results show that the latent space preserves chemical similarity principle and thus can be used for the generation of analogue structures. Furthermore, the latent space created by autoencoders were searched systematically to generate novel compounds with predicted activity against dopamine receptor type 2 and compounds similar to known active compounds not included in the training set were identified.