Generalizable, fast, and accurate DeepQSPR with fastprop
Name
s13321-025-01013-4.pdf
Description
Published version
Size
1.2 MB
Format
Adobe PDF
Checksum (MD5)
a313f1b6baa9f6bda1b2a46a44eb785e
Author(s)
Burns, Jackson W
Green, William H
Date Issued
May 13, 2025
Journal
Journal of Cheminformatics
Publisher
Springer Science and Business Media LLC
Citation
Burns, J.W., Green, W.H. Generalizable, fast, and accurate DeepQSPR with fastprop. J Cheminform 17, 73 (2025).
Version
Final published version
Abstract
Quantitative Structure–Property Relationship studies (QSPR), often referred to interchangeably as QSAR, seek to establish a mapping between molecular structure and an arbitrary target property. Historically this was done on a target-by-target basis with new descriptors being devised to specifically map to a given target. Today software packages exist that calculate thousands of these descriptors, enabling general modeling typically with classical and machine learning methods. Also present today are learned representation methods in which deep learning models generate a target-specific representation during training. The former requires less training data and offers improved speed and interpretability while the latter offers excellent generality, while the intersection of the two remains under-explored. This paper introduces fastprop, a software package and general Deep-QSPR framework that combines a cogent set of molecular descriptors with deep learning to achieve state-of-the-art performance on datasets ranging from tens to tens of thousands of molecules. fastprop provides both a user-friendly Command Line Interface and highly interoperable set of Python modules for the training and deployment of feedforward neural networks for property prediction. This approach yields improvements in speed and interpretability over existing methods while statistically equaling or exceeding their performance across most of the tested benchmarks. fastprop is designed with Research Software Engineering best practices and is free and open source, hosted at github.com/jacksonburns/fastprop.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s13321-025-01013-4
