# Synergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction

arXiv: https://arxiv.org/abs/1812.04439 · HTML: https://mlsanity.com/paper/1812.04439/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2018.

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

- [Geometric Deep Learning on Molecular Representations](https://mlsanity.com/paper/2107.12375/) (2021)
- [Improving VAE based molecular representations for compound property prediction](https://mlsanity.com/paper/2201.04929/) (2022)
- [A Hitchhiker's Guide to Deep Chemical Language Processing for Bioactivity Prediction](https://mlsanity.com/paper/2407.12152/) (2024)
- [Look the Other Way: Designing 'Positive' Molecules with Negative Data via Task Arithmetic](https://mlsanity.com/paper/2507.17876/) (2025)

## Abstract (arXiv)

In our study, we demonstrate the synergy effect between convolutional neural networks and the multiplicity of SMILES. The model we propose, the so-called Convolutional Neural Fingerprint (CNF) model, reaches the accuracy of traditional descriptors such as Dragon (Mauri et al. [22]), RDKit (Landrum [18]), CDK2 (Willighagen et al. [43]) and PyDescriptor (Masand and Rastija [20]). Moreover the CNF model generally performs better than highly fine-tuned traditional descriptors, especially on small data sets, which is of great interest for the chemical field where data sets are generally small due to experimental costs, the availability of molecules or accessibility to private databases. We evaluate the CNF model along with SMILES augmentation during both training and testing. To the best of our knowledge, this is the first time that such a methodology is presented. We show that using the multiplicity of SMILES during training acts as a regulariser and therefore avoids overfitting and can be seen as ensemble learning when considered for testing.