دورية أكاديمية
A community-powered search of machine learning strategy space to find NMR property prediction models.
| العنوان: | A community-powered search of machine learning strategy space to find NMR property prediction models. |
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| المؤلفون: | Lars A Bratholm, Will Gerrard, Brandon Anderson, Shaojie Bai, Sunghwan Choi, Lam Dang, Pavel Hanchar, Addison Howard, Sanghoon Kim, Zico Kolter, Risi Kondor, Mordechai Kornbluth, Youhan Lee, Youngsoo Lee, Jonathan P Mailoa, Thanh Tu Nguyen, Milos Popovic, Goran Rakocevic, Walter Reade, Wonho Song, Luka Stojanovic, Erik H Thiede, Nebojsa Tijanic, Andres Torrubia, Devin Willmott, Craig P Butts, David R Glowacki |
| المصدر: | PLoS ONE, Vol 16, Iss 7, p e0253612 (2021) |
| بيانات النشر: | Public Library of Science (PLoS), 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published 'in-house' efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1932-6203 |
| العلاقة: | https://doaj.org/toc/1932-6203Test |
| DOI: | 10.1371/journal.pone.0253612 |
| الوصول الحر: | https://doaj.org/article/7bd641a416ff403b90f3c0a0e9726773Test |
| رقم الانضمام: | edsdoj.7bd641a416ff403b90f3c0a0e9726773 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 19326203 |
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| DOI: | 10.1371/journal.pone.0253612 |