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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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This lesson delves into a critical aspect of the relative strengths of acids and bases. The strength of an acid is evaluated by the acid dissociation into its conjugate base and a hydronium ion in water. The complete dissociation of a strong acid is confirmed with a very high concentration of hydronium ions. As a result, an incomplete dissociation process affirms a weak acid. Therefore, the equilibrium is in the forward direction for strong acids and backward for weak acids in these reactions.

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Brønsted-Lowry acid-base chemistry is the transfer of protons; thus, logic suggests a relation between the relative strengths of conjugate acid-base pairs. The strength of an acid or base is quantified in its ionization constant, Ka or Kb, which represents the extent of the acid or base ionization reaction. For the conjugate acid-base pair HA / A−, the ionization equilibrium equations and ionization constant expressions are

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Related Experiment Video

Updated: Feb 4, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Extracting chemical-protein relations using attention-based neural networks.

Sijia Liu^1,2, Feichen Shen¹, Ravikumar Komandur Elayavilli¹

¹Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

Database : the Journal of Biological Databases and Curation

|October 9, 2018

Summary

We developed deep neural network models for extracting chemical-protein interactions. Attention-based recurrent neural networks (ATT-RNNs), particularly the ATT-gated recurrent unit (ATT-GRU), achieved the best performance in identifying these crucial biological relationships.

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Last Updated: Feb 4, 2026

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Area of Science:

Natural Language Processing
Bioinformatics
Computational Biology

Background:

Relation extraction is a key challenge in natural language processing.
Accurate identification of chemical-protein interactions is vital for drug discovery and understanding biological pathways.

Purpose of the Study:

To develop and evaluate deep neural network models for text mining chemical-protein interactions.
To compare the performance of various deep learning architectures, including CNNs, RNNs, and attention-based RNNs.

Main Methods:

Investigated multiple deep neural network (DNN) models.
Implemented convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention-based RNNs (ATT-RNNs).
Utilized word-level attention mechanisms for enhanced relation extraction.

Main Results:

Attention-based RNN models outperformed models without attention.
The ATT-gated recurrent unit (ATT-GRU) achieved the highest micro-average F1 score of 0.527 on the test set.
Attention mechanisms effectively identified important trigger words without semantic parsing or feature engineering.

Conclusions:

Deep learning, particularly ATT-RNNs and ATT-GRUs, demonstrates strong performance in chemical-protein relation extraction.
Attention mechanisms enhance model interpretability and performance by focusing on relevant textual cues.
The developed approach offers a robust method for automated text mining of chemical-protein interactions.