# Molecular Fingerprints for Robust and Efficient ML-Driven Molecular Generation

arXiv: https://arxiv.org/abs/2211.09086 · HTML: https://mlsanity.com/paper/2211.09086/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models](https://mlsanity.com/paper/1705.10843/) (2017)

## Built on by (most cited first, 2 of 2)

- [MolVision: Molecular Property Prediction with Vision Language Models](https://mlsanity.com/paper/2507.03283/) (2025)
- [NovoMolGen: Rethinking Molecular Language Model Pretraining](https://mlsanity.com/paper/2508.13408/) (2025)

## Abstract (arXiv)

We propose a novel molecular fingerprint-based variational autoencoder applied for molecular generation on real-world drug molecules. We define more suitable and pharma-relevant baseline metrics and tests, focusing on the generation of diverse, drug-like, novel small molecules and scaffolds. When we apply these molecular generation metrics to our novel model, we observe a substantial improvement in chemical synthetic accessibility (DeltabarSAS = -0.83) and in computational efficiency up to 5.9x in comparison to an existing state-of-the-art SMILES-based architecture.