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

TL;DR

Chemception is a deep convolutional neural network that predicts chemical properties from 2D molecular images without explicit chemistry knowledge, matching expert-developed models' performance across various property predictions.

Contribution

This work introduces Chemception, a novel deep CNN architecture that predicts chemical properties solely from molecular images, eliminating the need for traditional feature engineering.

Findings

Chemception slightly outperforms MLP models with ECFP fingerprints in activity and solvation prediction.

Chemception underperforms slightly in toxicity prediction compared to traditional models.

The model achieves comparable results to expert-designed QSAR/QSPR models.

Abstract

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.