# SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties

arXiv: https://arxiv.org/abs/1712.02034 · HTML: https://mlsanity.com/paper/1712.02034/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2017.

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

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- [Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches](https://mlsanity.com/paper/2408.09461/) (2024)
- [Unlocking Potential Binders: Multimodal Pretraining DEL-Fusion for Denoising DNA-Encoded Libraries](https://mlsanity.com/paper/2409.05916/) (2024)
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- [G-MATT: Single-step Retrosynthesis Prediction using Molecular Grammar Tree Transformer](https://mlsanity.com/paper/2305.03153/) (2023)
- [A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era](https://mlsanity.com/paper/2604.16586/) (2026)
- [Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction](https://mlsanity.com/paper/2309.01788/) (2023)
- [Multimodal Interpretable Data-Driven Models for Early Prediction of Antimicrobial Multidrug Resistance Using Multivariate Time-Series](https://mlsanity.com/paper/2402.06295/) (2024)
- [Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction](https://mlsanity.com/paper/2406.06553/) (2024)
- [Accelerating Drug Safety Assessment using Bidirectional-LSTM for SMILES Data](https://mlsanity.com/paper/2407.18919/) (2024)
- [Functional Groups are All you Need for Chemically Interpretable Molecular Property Prediction](https://mlsanity.com/paper/2509.09619/) (2025)
- [Molecular Representations for AI in Chemistry and Materials Science: An NLP Perspective](https://mlsanity.com/paper/2603.05525/) (2026)

## Abstract (arXiv)

Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is structural information that can be used to predict complex chemical properties. In this work, we develop SMILES2vec, a deep RNN that automatically learns features from SMILES to predict chemical properties, without the need for additional explicit feature engineering. Using Bayesian optimization methods to tune the network architecture, we show that an optimized SMILES2vec model can serve as a general-purpose neural network for predicting distinct chemical properties including toxicity, activity, solubility and solvation energy, while also outperforming contemporary MLP neural networks that uses engineered features. Furthermore, we demonstrate proof-of-concept of interpretability by developing an explanation mask that localizes on the most important characters used in making a prediction. When tested on the solubility dataset, it identified specific parts of a chemical that is consistent with established first-principles knowledge with an accuracy of 88%. Our work demonstrates that neural networks can learn technically accurate chemical concept and provide state-of-the-art accuracy, making interpretable deep neural networks a useful tool of relevance to the chemical industry.