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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
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Related Experiment Video

Updated: Sep 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Empowering Reactivity Predictions through Noise-Based Data Augmentation.

Julian A Hueffel1, Quentin P Bindschaedler1, Francesco Sala1

  • 1Institute of Organic Chemistry, RWTH Aachen University, Landoltweg 1, 52074 Aachen, Germany.

Journal of the American Chemical Society
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

Data scarcity hinders Artificial Intelligence (AI) in molecular chemistry. Data augmentation, by adding noise to existing data, significantly improves AI model performance for predicting chemical reactions, even with limited data.

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Area of Science:

  • Computational chemistry
  • Machine learning in chemistry

Background:

  • Data scarcity is a major challenge for Artificial Intelligence (AI) in molecular science.
  • Data augmentation is a common technique in other fields but its applicability to molecular reactivity is unknown.

Purpose of the Study:

  • To evaluate the effectiveness of data augmentation for molecular reactivity prediction.
  • To determine if data augmentation can improve AI model performance in low-data scenarios for chemical reactions.

Main Methods:

  • Systematic evaluation of data augmentation on diverse reactivity problems.
  • Application of Gaussian noise to existing data points for data augmentation.
  • Training AI models with augmented and original datasets.

Main Results:

  • Data augmentation significantly enhances predictive performance for molecular reactivity.
  • Models trained with augmented data achieve accuracy comparable to models trained on full datasets.
  • Data augmentation enables meaningful model training in low-data regimes.

Conclusions:

  • Data augmentation is a powerful strategy for overcoming data scarcity in AI for molecular reactivity.
  • This approach reduces the need for extensive experimental data, saving time and resources.
  • Data augmentation accelerates the integration of machine learning in chemical research.