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Related Concept Videos

Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
Basicity of Aliphatic Amines01:21

Basicity of Aliphatic Amines

Amines can behave as Brønsted–Lowry bases by accepting a proton from the acid to form corresponding conjugate acids. Due to a lone pair of nonbonding electrons, aliphatic amines can also act as Lewis bases by forming a covalent bond with an electrophile.
To measure the basicity of amines, two conventions are generally used. The first defines Kb as the basicity constant for the deprotonation reaction of water by the amine, as presented in Figure 1. Conventionally, lower Kb indicates higher...
Acid and Bases: Ka, pKa, and Relative Strengths02:35

Acid and Bases: Ka, pKa, and Relative Strengths

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.
Relative Strengths of Conjugate Acid-Base Pairs02:29

Relative Strengths of Conjugate Acid-Base Pairs

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
Protein Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

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Unifying pKa and Protonation Prediction with Sequence-Based Deep Learning.

Charlotte Infante1, Jieyu Lu1, Xiaolin Pan1

  • 1Department of Chemistry, New York University, New York, New York 10003, United States.

Journal of Chemical Information and Modeling
|May 29, 2026
PubMed
Summary
This summary is machine-generated.

Accurate prediction of acidity constants (pKa) is crucial for understanding molecular behavior. This study introduces T5pKa, a novel deep learning model that uses sequence-based methods and a curated dataset to predict microscopic pKa values, overcoming limitations of existing approaches.

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Area of Science:

  • Computational Chemistry
  • Machine Learning in Drug Discovery
  • Chemical Informatics

Background:

  • Accurate prediction of acidity constants (pKa) is vital for understanding molecular properties like solubility and binding affinity.
  • Scarcity of experimental microscopic pKa data and inconsistent terminology in existing datasets impede the development of reliable prediction models.
  • While graph-based neural networks dominate, sequence-based deep learning for pKa prediction remains an underexplored area.

Purpose of the Study:

  • To introduce a curated dataset, pKaCHU, containing 9000 experimentally derived microscopic pKa entries with ionization-state annotations.
  • To develop and present T5pKa, a novel text-based transformer model for small-molecule pKa prediction utilizing sequence-based deep learning.
  • To leverage a unified multitasking framework for both microstate enumeration and microscopic pKa prediction.

Main Methods:

  • Developed pKaCHU, a comprehensive dataset by combining, honing, and updating experimental microscopic pKa data.
  • Built T5pKa, a sequence-to-sequence model based on the T5Chem architecture, to predict molecular protonation/deprotonation.
  • Employed multitask learning within T5pKa to enumerate microstates and a separate regression model for pKa value prediction.

Main Results:

  • T5pKa successfully predicts microscopic pKa values by treating protonation/deprotonation as a language modeling task.
  • The model demonstrates performance comparable to existing state-of-the-art pKa prediction tools across benchmark datasets.
  • T5pKa offers a unified framework for microstate enumeration and pKa prediction, enhancing model development efficiency.

Conclusions:

  • T5pKa represents a significant advancement in small-molecule pKa prediction using sequence-based deep learning.
  • The pKaCHU dataset provides a valuable resource for training and benchmarking pKa prediction models.
  • This approach offers a promising direction for improving the accuracy and efficiency of predicting key molecular properties.