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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...
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
Therapeutic Drug Monitoring: Overview and Classification01:16

Therapeutic Drug Monitoring: Overview and Classification

Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood at designated intervals to ensure the drug concentration stays within a therapeutic range. This monitoring is crucial for optimizing individual dosage regimens, enhancing therapeutic efficacy, and minimizing drug-related toxicity. TDM is vital for drugs with narrow therapeutic windows, significant variability in pharmacokinetics, and a clear correlation between plasma levels and...
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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Related Experiment Video

Updated: Jun 20, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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SuperPred 3.0: drug classification and target prediction-a machine learning approach.

Kathleen Gallo1, Andrean Goede1, Robert Preissner1

  • 1Charité - Universitätsmedizin Berlin, Institute of Physiology and Science IT, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, 10117 Berlin, Germany.

Nucleic Acids Research
|May 7, 2022
PubMed
Summary

The SuperPred webserver now offers improved drug classification and target prediction using machine learning models and filtered datasets. This update enhances prediction accuracy for Anatomical Therapeutic Chemical (ATC) classes and drug targets.

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Area of Science:

  • Pharmacology
  • Computational Chemistry
  • Bioinformatics

Background:

  • The SuperPred webserver, last updated in 2014, provides drug classification and target prediction.
  • Previous versions relied on structural similarity, potentially limiting accuracy.
  • Comparing prediction methods is challenging due to variations in training datasets.

Purpose of the Study:

  • To present the updated SuperPred webserver (version 3.0) with enhanced drug classification and target prediction capabilities.
  • To introduce thoroughly filtered datasets for Anatomical Therapeutic Chemical (ATC) and target prediction.
  • To improve the accuracy and interpretability of drug property predictions.

Main Methods:

  • Development of machine learning models for both ATC and target prediction, emphasizing functional groups.
  • Creation of a rigorously filtered ATC dataset for reliable predictions.
  • Expansion of the target prediction dataset to include non-binding substances to minimize false positives.

Main Results:

  • Achieved an accuracy of 80.5% for ATC prediction, an improvement of nearly 5% over the previous version.
  • Implemented a new scoring function for easily interpretable prediction values.
  • Enhanced dataset filtering reduces false positives in target predictions.

Conclusions:

  • SuperPred 3.0 offers state-of-the-art drug classification and target prediction.
  • The use of machine learning and refined datasets significantly improves prediction accuracy.
  • The webserver is publicly accessible for research and drug discovery.