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Related Concept Videos

Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
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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Related Experiment Video

Updated: Jun 23, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Beyond Random Splits: Assessing the Generalization of Graph and Vector Models for WT-Structure-Only Drug Resistance

Zongrui Cheng1, Haoxin Wu1, Dengming Ming1

  • 1College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, China.

Computational and Structural Biotechnology Journal
|June 22, 2026
PubMed
Summary

Predicting drug resistance mutations is challenging when only wild-type (WT) structures are available. Current methods overestimate their ability to generalize to new proteins, highlighting the need for better evaluation strategies.

Related Experiment Videos

Last Updated: Jun 23, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Drug discovery

Background:

  • Predicting mutation-induced changes in binding free energy (ΔΔG) is crucial for understanding drug resistance and prioritizing variants.
  • Mutant complex structures are often unavailable in clinical settings, necessitating methods that do not rely on them.
  • Existing prediction methods often use paired wild-type (WT) and mutant structures and may have protein overlap in training and testing data, inflating performance estimates.

Purpose of the Study:

  • To evaluate the generalization performance of computational methods for predicting mutation-induced changes in binding free energy (ΔΔG) using only wild-type (WT) complex structures.
  • To compare graph-based and vector-based modeling approaches under realistic, protein-disjoint evaluation settings.
  • To identify factors limiting the prediction accuracy of mutation-induced drug resistance.

Main Methods:

  • Established a WT-complex-structure-only prediction setting, excluding mutant structural coordinates.
  • Compared graph- and vector-based configurations using both random and strict UniProt-based data splits.
  • Conducted ablation studies analyzing graph context, message passing, representation bias, and dataset noise.

Main Results:

  • Strict UniProt-based evaluation (simulating unseen proteins) revealed a significant drop in performance (Pearson R ≈ 0.15) compared to random splits (Pearson R ≈ 0.55), indicating overestimation by current practices.
  • Graph-based modeling showed a weak, non-significant improvement over vector-based methods.
  • Full-protein structural context was more beneficial than local pocket information, but message passing did not offer clear advantages.

Conclusions:

  • Predicting mutation-induced drug resistance using only WT static structures is not yet solved for protein-disjoint generalization.
  • Performance limitations may stem from experimental label inconsistency, sparse protein coverage, and lack of dynamic structural information.
  • There is a critical need for split-aware evaluation protocols and incorporation of stronger physical principles in predictive models.