Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction

TL;DR

This paper introduces ChemNet, a transferable deep neural network trained with rule-based knowledge in a weak-supervised manner, which outperforms traditional models in chemical property prediction and is effective across different neural network architectures.

Contribution

The paper presents a novel weak-supervised training approach for ChemNet using rule-based knowledge, enhancing transferability and accuracy in chemical property prediction.

Findings

ChemNet outperforms conventional supervised DNN models.

Pre-training ChemNet improves accuracy on small datasets.

The approach is effective across CNN and RNN architectures.

Abstract

With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small and fragmented. In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable deep neural network for chemical property prediction that learns in a weak-supervised manner from large unlabeled chemical databases. When coupled with transfer learning approaches to predict other smaller datasets for chemical properties that it was not originally trained on, we show that ChemNet's accuracy outperforms contemporary DNN models that were trained using conventional supervised learning. Furthermore, we demonstrate that the ChemNet pre-training approach is equally effective on both CNN (Chemception) and RNN (SMILES2vec) models, indicating that this approach is network architecture agnostic and is effective across multiple data modalities. Our results indicate a pre-trained ChemNet that incorporates chemistry domain knowledge, enables the development of generalizable neural networks for more accurate prediction of novel chemical properties.