Instructor-Inspired Machine Learning for Robust Molecular Property Prediction

Abstract

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.

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Cite

Text

Wu et al. "Instructor-Inspired Machine Learning for Robust Molecular Property Prediction." Neural Information Processing Systems, 2024. doi:10.52202/079017-3690

Markdown

[Wu et al. "Instructor-Inspired Machine Learning for Robust Molecular Property Prediction." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/wu2024neurips-instructorinspired/) doi:10.52202/079017-3690

BibTeX

@inproceedings{wu2024neurips-instructorinspired,
  title     = {{Instructor-Inspired Machine Learning for Robust Molecular Property Prediction}},
  author    = {Wu, Fang and Jin, Shuting and Li, Siyuan and Li, Stan Z.},
  booktitle = {Neural Information Processing Systems},
  year      = {2024},
  doi       = {10.52202/079017-3690},
  url       = {https://mlanthology.org/neurips/2024/wu2024neurips-instructorinspired/}
}