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Drug Discovery: Overview01:26

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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...
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Pharmacogenomics: Identification of New Drug Targets

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Predicting Products: SN1 vs. SN202:27

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Related Experiment Video

Updated: May 26, 2026

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

BDDCS class prediction for new molecular entities.

Fabio Broccatelli1, Gabriele Cruciani, Leslie Z Benet

  • 1Laboratory of Chemometrics, Department of Chemistry, University of Perugia, Via Elce di Sotto 10, I-60123 Perugia, Italy.

Molecular Pharmaceutics
|January 10, 2012
PubMed
Summary

A new computational model accurately predicts Biopharmaceutics Drug Disposition Classification System (BDDCS) classes for new molecular entities, aiding in early drug discovery and reducing R&D costs by forecasting potential drug-drug interactions.

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Last Updated: May 26, 2026

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:

  • Pharmacokinetics and Drug Metabolism
  • Computational Chemistry and Cheminformatics
  • Drug Discovery and Development

Background:

  • The Biopharmaceutics Drug Disposition Classification System (BDDCS) is crucial for predicting drug-drug interactions (DDIs) involving drug metabolizing enzymes and transporters.
  • BDDCS relies on the principle that extent of metabolism (EoM) correlates with intestinal permeability, excluding transporter-mediated or paracellular transport.
  • Accurate BDDCS classification of new molecular entities (NMEs) can anticipate their disposition and potential DDIs, guiding in vitro testing.

Purpose of the Study:

  • To develop and validate a computational procedure for predicting BDDCS class directly from molecular structures.
  • To integrate predicted BDDCS class with in vitro assays for enhanced prediction of NME disposition and DDIs.
  • To assess the utility of the model in early drug discovery for cost reduction and prioritizing experiments.

Main Methods:

  • A computational model was trained on 300 oral drugs and validated on 379 external oral drugs using 17 VolSurf+ descriptors.
  • The model predicted the probability of BDDCS class membership based on predicted extent of metabolism (EoM) and FDA solubility (FDAS).
  • Linear discriminant analysis (LDA) was used to confirm prediction accuracy across different BDDCS classes.

Main Results:

  • The model achieved 77-78% accuracy for FDAS prediction and 79-82% accuracy for EoM prediction in validation and training sets, respectively.
  • The correct BDDCS class was identified in 55% of validation cases, and within the top two classes for over 92% of cases.
  • Prediction accuracy was highest for BDDCS classes 2 and 3, with limited predictability observed for class 4 drugs.

Conclusions:

  • The developed computational model reliably predicts BDDCS class from molecular structures, aiding in early drug discovery.
  • The model can prioritize in vitro tests for NMEs, focusing on factors like transporter affinity, metabolism, absorption, and protein binding.
  • Solubility appears to be a more significant differentiator between NMEs and marketed drugs than permeability, suggesting a potential for significant R&D cost savings.