Academic Journal
Chemprop: A Machine Learning Package for Chemical Property Prediction
| العنوان: | Chemprop: A Machine Learning Package for Chemical Property Prediction |
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| المؤلفون: | Esther Heid, Kevin P. Greenman, Yunsie Chung, Shih-Cheng Li, David E. Graff, Florence H. Vermeire, Haoyang Wu, William H. Green, Charles J. McGill |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Biophysics, Biochemistry, Medicine, Astronomical and Space Sciences not elsewhere classified, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, transfer learning workflows, passing neural networks, octanol partition coefficients, improved hyperparameter optimization, frequently employed tool, calibration methods along, atomic partial charges, versatile software solutions, reaction barrier heights, property prediction tasks, machine learning package, learned molecular properties, new chemprop functionalities, new reaction, molecular properties, multimolecule properties, thus creating, source software, related metrics, problem settings, offers simple, observe state, mpnn models |
| الوصف: | Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multimolecule properties, reactions, atom/bond-level properties, and spectra. Further, we incorporate various uncertainty quantification and calibration methods along with related metrics as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level, and spectra functionality on a variety of property prediction data sets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in fast, user-friendly, and open-source software. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| Relation: | https://figshare.com/articles/journal_contribution/Chemprop_A_Machine_Learning_Package_for_Chemical_Property_Prediction/24905344 |
| DOI: | 10.1021/acs.jcim.3c01250.s001 |
| الاتاحة: | https://doi.org/10.1021/acs.jcim.3c01250.s001 https://figshare.com/articles/journal_contribution/Chemprop_A_Machine_Learning_Package_for_Chemical_Property_Prediction/24905344 |
| Rights: | CC BY-NC 4.0 |
| رقم الانضمام: | edsbas.D1C9B4A |
| قاعدة البيانات: | BASE |
| DOI: | 10.1021/acs.jcim.3c01250.s001 |
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