Cited by Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2

3DReact: Geometric Deep Learning for Chemical Reactions DOI

Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne

et al.

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(15), P. 5771 - 5785

Published: July 15, 2024

Geometric deep learning models, which incorporate the relevant molecular symmetries within neural network architecture, have considerably improved accuracy and data efficiency of predictions properties. Building on this success, we introduce 3DReact, a geometric model to predict reaction properties from three-dimensional structures reactants products. We demonstrate that invariant version is sufficient for existing sets. illustrate its competitive performance prediction activation barriers GDB7-22-TS, Cyclo-23-TS, Proparg-21-TS sets in different atom-mapping regimes. show that, compared models property prediction, 3DReact offers flexible framework exploits information, if available, as well geometries products (in an or equivariant fashion). Accordingly, it performs systematically across sets, regimes, both interpolation extrapolation tasks.

Language: Английский

Citations

Chemical Reaction Networks from Scratch with Reaction Prediction and Kinetics-Guided Exploration DOI

Michael Woulfe,

Brett M. Savoie

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

Algorithmic reaction explorations based on transition state searches can now routinely predict relatively short sequences involving small molecules. However, applying these algorithms to deeper chemical network (CRN) exploration still requires the development of more efficient and accurate policies. Here, an algorithm, which we name yet another kinetic strategy (YAKS), is demonstrated that uses microkinetic simulations nascent achieve cost-effective, deep exploration. Key features algorithm are automatic incorporation bimolecular reactions between intermediates, compatibility with short-lived but kinetically important species, rate uncertainty into policy. In validation case studies glucose pyrolysis, rediscovers pathways previously discovered by heuristic policies elucidates new for experimentally obtained products. The resulting CRN first connect all major experimental pyrolysis products glucose. Additional presented investigate role rules, uncertainty, reactions. These show naïve exponential growth estimates vastly overestimate actual number relevant in physical networks. light this, further improvements prediction make it feasible CRNs might soon be predictable some contexts.

Language: Английский

Citations

Computational Tools for the Prediction of Site- and Regioselectivity of Organic Reactions DOI

Lukas M. Sigmund,

Michele Assante,

Magnus J. Johansson

et al.

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

This article reviews computational tools for the prediction of regio- and site-selectivity organic reactions. It spans from quantum chemical procedures to deep learning models showcases application presented tools.

Language: Английский

Citations

Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2 DOI

Andrew L. Ferguson, Jim Pfaendtner

The Journal of Physical Chemistry B, Journal Year: 2024, Volume and Issue: 128(27), P. 6435 - 6438

Published: July 11, 2024

Language: Английский

Citations

Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2 DOI

Andrew L. Ferguson, Jim Pfaendtner

The Journal of Physical Chemistry A, Journal Year: 2024, Volume and Issue: 128(27), P. 5225 - 5228

Published: July 11, 2024