# Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models

arXiv: https://arxiv.org/abs/1706.06689 · HTML: https://mlsanity.com/paper/1706.06689/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2017.

## Built on by (most cited first, 19 of 19)

- [MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design](https://mlsanity.com/paper/2203.14500/) (2022)
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- [Deep Learning Methods for Small Molecule Drug Discovery: A Survey](https://mlsanity.com/paper/2303.00313/) (2023)
- [Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?](https://mlsanity.com/paper/2104.02604/) (2021)
- [End-to-End Attention-based Image Captioning](https://mlsanity.com/paper/2104.14721/) (2021)
- [Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction](https://mlsanity.com/paper/2411.13688/) (2024)
- [ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning](https://mlsanity.com/paper/2302.04917/) (2023)
- [MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures](https://mlsanity.com/paper/2311.16666/) (2023)
- [Multi-view biomedical foundation models for molecule-target and property prediction](https://mlsanity.com/paper/2410.19704/) (2024)
- [A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era](https://mlsanity.com/paper/2604.16586/) (2026)
- [ChemTime: Rapid and Early Classification for Multivariate Time Series Classification of Chemical Sensors](https://mlsanity.com/paper/2312.09871/) (2023)
- [Stacked ensemble-based mutagenicity prediction model using multiple modalities with graph attention network](https://mlsanity.com/paper/2409.01731/) (2024)
- [CROP: Integrating Topological and Spatial Structures via Cross-View Prefixes for Molecular LLMs](https://mlsanity.com/paper/2508.06917/) (2025)
- [Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction](https://mlsanity.com/paper/2511.05577/) (2025)
- [A decoupled alignment kernel for peptide membrane permeability predictions](https://mlsanity.com/paper/2511.21566/) (2025)
- [Back to Basics: Improving Molecular Understanding in LLMs via SMILES-Graph Translation](https://mlsanity.com/paper/2607.03007/) (2026)
- [Generic Vision and Cross-Attention for Reaction Yield Prediction](https://mlsanity.com/paper/2608.00776/) (2026)
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## Abstract (arXiv)

In the last few years, we have seen the transformative impact of deep learning in many applications, particularly in speech recognition and computer vision. Inspired by Google's Inception-ResNet deep convolutional neural network (CNN) for image classification, we have developed "Chemception", a deep CNN for the prediction of chemical properties, using just the images of 2D drawings of molecules. We develop Chemception without providing any additional explicit chemistry knowledge, such as basic concepts like periodicity, or advanced features like molecular descriptors and fingerprints. We then show how Chemception can serve as a general-purpose neural network architecture for predicting toxicity, activity, and solvation properties when trained on a modest database of 600 to 40,000 compounds. When compared to multi-layer perceptron (MLP) deep neural networks trained with ECFP fingerprints, Chemception slightly outperforms in activity and solvation prediction and slightly underperforms in toxicity prediction. Having matched the performance of expert-developed QSAR/QSPR deep learning models, our work demonstrates the plausibility of using deep neural networks to assist in computational chemistry research, where the feature engineering process is performed primarily by a deep learning algorithm.