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Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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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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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

Weighted feature value based Drug Target Protein prediction.

Bo-ra Hyun1, Hwiesung Jung, Woo-Hyuk Jang

  • 1School of Engineering, Information and Communication University, Yuseong-gu, Daejeon, Korea. lovesun79ya@icu.ac.kr

International Journal of Computational Biology and Drug Design
|January 12, 2010
PubMed
Summary

This study introduces a new computational method for classifying drug target proteins (DT-P) using weighted features. The approach achieves high accuracy and specificity, aiding efficient drug discovery.

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Area of Science:

  • Biochemistry
  • Computational Biology
  • Pharmacology

Background:

  • Drug discovery is a lengthy and costly process with a low success rate.
  • Identifying drug target candidate proteins is time-consuming and resource-intensive.
  • Advancements in data accumulation enable computational approaches for drug target identification.

Purpose of the Study:

  • To develop a novel computational method for classifying drug target proteins (DT-P).
  • To improve the efficiency and accuracy of identifying potential drug targets.
  • To leverage existing drug information for predictive classification.

Main Methods:

  • A DT-P classification method was devised using the summation of weighted features.
  • Features were extracted from known DT-P datasets.
  • The method's performance was validated using Bayesian decision theory and Support Vector Machines (SVM).

Main Results:

  • The developed DT-P classification method demonstrated high performance.
  • The method achieved a specificity of 89.5%.
  • An overall accuracy of 88% was recorded.

Conclusions:

  • The proposed computational method effectively classifies drug target proteins.
  • This approach can significantly accelerate the early stages of drug discovery.
  • The validated method offers a promising tool for identifying therapeutic targets.