@inproceedings{
wu2024instructorinspired,
title={Instructor-inspired Machine Learning for Robust Molecular Property Prediction},
author={Fang Wu and Shuting Jin and Siyuan Li and Stan Z. Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=j7sw0nXLjZ}
}
Machine learning catalyzes a revolution in chemical and biological science. However, its efficacy is heavily dependent on the availability of labeled data, and annotating biochemical data is extremely laborious. To surmount this data sparsity challenge, we present an instructive learning algorithm named InstructMol to measure pseudo-labels' reliability and help the target model leverage large-scale unlabeled data. InstructMol does not require transferring knowledge between multiple domains, which avoids the potential gap between the pretraining and fine-tuning stages. We demonstrated the high accuracy of InstructMol on several real-world molecular datasets and out-of-distribution (OOD) benchmarks.
Instructor-inspired Machine Learning for Robust Molecular Property Prediction
Fang Wu Shuting Jin Siyuan Li Stan Z. Li