# GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks

arXiv: https://arxiv.org/abs/1909.04825 · HTML: https://mlsanity.com/paper/1909.04825/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Large Language Models on Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2312.02783/) (2023)
- [Language models in molecular discovery](https://mlsanity.com/paper/2309.16235/) (2023)
- [De-novo Chemical Reaction Generation by Means of Temporal Convolutional Neural Networks](https://mlsanity.com/paper/2310.17341/) (2023)
- [SHA-256 Infused Embedding-Driven Generative Modeling of High-Energy Molecules in Low-Data Regimes](https://mlsanity.com/paper/2510.25788/) (2025)

## Abstract (arXiv)

Recurrent neural networks have been widely used to generate millions of de novo molecules in a known chemical space. These deep generative models are typically setup with LSTM or GRU units and trained with canonical SMILEs. In this study, we introduce a new robust architecture, Generative Examination Networks GEN, based on bidirectional RNNs with concatenated sub-models to learn and generate molecular SMILES with a trained target space. GENs autonomously learn the target space in a few epochs while being subjected to an independent online examination mechanism to measure the quality of the generated set. Here we have used online statistical quality control (SQC) on the percentage of valid molecules SMILES as an examination measure to select the earliest available stable model weights. Very high levels of valid SMILES (95-98%) can be generated using multiple parallel encoding layers in combination with SMILES augmentation using unrestricted SMILES randomization. Our architecture combines an excellent novelty rate (85-90%) while generating SMILES with a strong conservation of the property space (95-99%). Our flexible examination mechanism is open to other quality criteria.