• Chemprop v2: An Efficient, Mod...

Treffer: Chemprop v2: An Efficient, Modular Machine Learning Package for Chemical Property Prediction.

Title:
Chemprop v2: An Efficient, Modular Machine Learning Package for Chemical Property Prediction.
Authors:
Graff DE; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States., Morgan NK; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Burns JW; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Doner AC; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Li B; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Li SC; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Manu J; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Menon A; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Pang HW; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Wu H; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Zalte AS; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Zheng JW; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Coley CW; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Green WH; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States., Greenman KP; Department of Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.; Department of Chemical Engineering, Catholic Institute of Technology, Cambridge, Massachusetts 02142, United States.; Department of Chemistry, Catholic Institute of Technology, Cambridge, Massachusetts 02142, United States.
Source:
Journal of chemical information and modeling [J Chem Inf Model] 2026 Jan 12; Vol. 66 (1), pp. 28-33. Date of Electronic Publication: 2025 Dec 26.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: American Chemical Society Country of Publication: United States NLM ID: 101230060 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-960X (Electronic) Linking ISSN: 15499596 NLM ISO Abbreviation: J Chem Inf Model Subsets: MEDLINE
Imprint Name(s):
Original Publication: Washington, D.C. : American Chemical Society, c2005-
MeSH Terms:
Machine Learning* , Software* , Cheminformatics*/methods, Neural Networks, Computer ; Algorithms
Entry Date(s):
Date Created: 20251226 Date Completed: 20260112 Latest Revision: 20260112
Update Code:
20260112
DOI:
10.1021/acs.jcim.5c02332
PMID:
41453060
Database:
MEDLINE

Weitere Informationen

Accurate prediction of molecular properties is essential for computational design in many areas of chemistry. Deep learning has been used in these prediction tasks for a wide variety of molecular properties, and the availability of user-friendly open-source software implementing such architectures has democratized access to these methods. chemprop is one of the most popular examples of such software in this field. It implements a directed message-passing neural network (D-MPNN) architecture, enabling end-to-end learning of molecular properties directly from molecular graphs without the need for handcrafted descriptors or fingerprints. The original chemprop release was intended for use primarily via a command line interface, rather than programmatic use via a Python API. As the field has evolved, the need for increased modularity and usability in Python-based workflows has become clear. We completed a ground-up rewrite of chemprop that addresses this need, providing improvements in speed, extensibility, and overall user experience. We have conducted extensive benchmarking to demonstrate algorithmic parity with the original implementation, while seeing improvements of about a factor of 2 in execution time and a factor of 3 in memory usage. chemprop v2 effectively scales to multiple GPUs, which enables the training of more and larger models. chemprop v2 also includes some new features. Extensive Jupyter notebook tutorials and new documentation for all major functionality were also added. chemprop v2 preserves the predictive accuracy of its predecessor and enhances modularity, speed, and usability, empowering researchers to pursue computational molecular design more effectively.