The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions
Zhen Liu, Yurii S. Moroz, Olexandr Isayev
Highlight
A sensitive model captures the reactivity cliffs but overfit to yield outliers.
Abstract
A sensitive model captures the reactivity cliffs but overfit to yield outliers. On the other hand, a robust model disregards the yield outliers but underfits the reactivity cliffs.
Keywords
Cite This Paper
@article{Liu2023balancing,
author = {Liu, Zhen and Moroz, Yurii S. and Isayev, Olexandr},
title = {The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions},
year = {2023},
journal = {Chem. Sci.},
volume = {14},
number = {39},
pages = {10835--10846},
doi = {10.1039/d3sc03902a},
keywords = {yield prediction, benchmarking},
researchAreas = {reactions-reactivity},
highlight = {A sensitive model captures the reactivity cliffs but overfit to yield outliers.}
} Copied to clipboard!
Related Research Areas
Related Publications
Uncertainty-Aware Yield Prediction with Multimodal Molecular Features
, 38, 8274–8282 (2024)
Predicting chemical reaction yields is pivotal for efficient chemical synthesis, an area that focuses on the creation of novel compounds for diverse uses.
Teaching a neural network to attach and detach electrons from molecules
Nature Communications, 12 (2021)
Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations.
Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions
(2024)
Ring strain energy (RSE) is crucial for understanding molecular reactivity.
The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions
(2023)
Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis, but current models have failed to generalize to large literature datasets.
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
(2023)
Abstract Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery.