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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Drug Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

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Related Experiment Video

Updated: Jun 14, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D

Nazife Çevik1, Taner Çevik2, Ahmet Gürhanlı3

  • 1Department of Computer Engineering, Istanbul Arel University, Istanbul, Turkey. nazifecevik@arel.edu.tr.

Scientific Reports
|June 12, 2026
PubMed
Summary

This study introduces a novel sequence-based framework for predicting drug-target interactions (DTIs), eliminating the need for structural data. The method achieves high accuracy, offering a scalable and interpretable alternative for drug discovery.

Keywords:
Deep learningDrug–target interactionEnsemble learningMachine learningSMOTESequence-based prediction

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Protein Target Prediction and Validation of Small Molecule Compound
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Protein Target Prediction and Validation of Small Molecule Compound

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Last Updated: Jun 14, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Protein Target Prediction and Validation of Small Molecule Compound
10:21

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

Area of Science:

  • Computational Biology
  • Drug Discovery
  • Bioinformatics

Background:

  • Accurate prediction of drug-target interactions (DTIs) is crucial for early-stage drug discovery.
  • Traditional methods often rely on 3D structural information, which is not always available.
  • Developing structure-independent DTI prediction methods is a significant challenge.

Purpose of the Study:

  • To propose a fully sequence-based framework for DTI prediction.
  • To eliminate the dependence on 3D structural data in DTI prediction.
  • To achieve docking-comparable predictive performance using only sequence information.

Main Methods:

  • Developed a unified representation integrating protein physicochemical descriptors, 3-gram sequence motifs, and drug sequence encodings.
  • Evaluated diverse machine learning, deep learning, and ensemble classifiers using stratified five-fold cross-validation.
  • Employed Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance correction.
  • Utilized a stacking ensemble classifier combining Random Forest, Support Vector Machine, and Logistic Regression.

Main Results:

  • Achieved robust performance with ROC-AUC values exceeding 0.90, reaching a maximum AUC of 0.914.
  • Identified biologically meaningful protein sequence motifs associated with binding interactions through feature importance analysis.
  • Molecular docking experiments validated the predicted drug-target pairs, showing agreement between docking scores and predicted binding probabilities.

Conclusions:

  • Sequence-derived representations and ensemble learning offer a scalable, interpretable, and computationally efficient alternative to structure-dependent DTI prediction.
  • The proposed framework successfully predicts DTIs without requiring 3D structural information.
  • This approach holds significant potential for accelerating early-stage drug discovery.