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A Web Tool for Generating High Quality Machine-readable Biological Pathways
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Comprehensive exploration of graphically defined reaction spaces.

Qiyuan Zhao1, Sai Mahit Vaddadi1, Michael Woulfe1

  • 1Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA.

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|March 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the Reaction Graph Depth 1 (RGD1) dataset, the largest and most diverse collection of organic reaction transition states (TS) to date. RGD1 aids in developing machine learning models for predicting chemical reaction properties.

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Area of Science:

  • Computational Chemistry
  • Chemical Informatics
  • Machine Learning

Background:

  • Existing transition state (TS) databases are limited in size and chemical diversity.
  • A comprehensive characterization of reaction spaces is needed for advancing chemical predictions.

Purpose of the Study:

  • To address the data gap in existing TS databases by creating a large and chemically diverse dataset.
  • To provide validated TS, activation energies, and other properties for organic reactions.
  • To enable the development of novel machine learning models for reaction property prediction.

Main Methods:

  • Utilized a graphically-defined model reaction approach to explore reaction spaces.
  • Generated a dataset of 176,992 organic reactions with validated TS and associated properties.
  • Employed GFN2-xTB and B3LYP-D3/TZVP levels of theory, with higher-level calculations for error assessment.
  • Performed conformational sampling to identify multiple TS for a subset of reactions.

Main Results:

  • The Reaction Graph Depth 1 (RGD1) dataset comprises 176,992 organic reactions with validated TS, activation energies, and atom-mapping.
  • Conformational sampling identified multiple TS for 33,032 reactions, enabling assessment of TS prediction errors.
  • The dataset provides data at multiple levels of theory, including GFN2-xTB, B3LYP-D3/TZVP, CCSD(T)-F12, and ωB97X-D2.

Conclusions:

  • RGD1 is the largest and most chemically diverse TS dataset published to date.
  • This dataset fills a critical gap and is expected to facilitate the development of advanced machine learning models for chemical reactions.
  • The availability of RGD1 will accelerate progress in computational chemistry and reaction prediction.