Related Experiment Video

Updated: Apr 17, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

2.1K

Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the CANDO

Geetika Sethi, Gaurav Chopra, Ram Samudrala1

  • 1Department of Biomedical Informatics, School of Medicine and Biomedical Sciences, State University of New York (SUNY), 923 Main Street, Buffalo, NY 14203, USA. ram@compbio.org.

Mini Reviews in Medicinal Chemistry
|February 20, 2015
PubMed

Insights

The CANDO drug discovery platform shows improved accuracy when considering multiple protein classes and multitargeting. Combining diverse protein classes enhances drug repurposing and discovery protocols for specific indications.

Area of Science:

  • Computational drug discovery
  • Bioinformatics
  • Pharmacology

Background:

  • The CANDO platform analyzes drug-target interactions for drug discovery and repurposing.
  • Understanding protein class contributions is crucial for optimizing drug discovery protocols.

Purpose of the Study:

  • To evaluate the impact of eight distinct protein classes on the CANDO platform's benchmarking performance.
  • To assess the role of multitargeting and protein class combinations in drug discovery accuracy.

Main Methods:

  • Utilized the CANDO platform, processing over one billion predicted interactions between proteins and compounds.
  • Benchmarking accuracy was calculated across 1439 indications with multiple approved drugs.
  • Compared performance across eight protein classes against control sets and random matrices.

Main Results:

  • A significant positive correlation (0.99) was found between the number of proteins and benchmarking accuracy, highlighting multitargeting's importance.
  • Average accuracies ranged from 6.2% to 7.6% for individual protein classes, significantly outperforming random matrices (0.2%).
  • Highest accuracies (up to 11.7%) were achieved using a combination of protein classes with diverse protein folds.

Conclusions:

  • The CANDO platform demonstrates utility in drug discovery and repurposing.
  • Incorporating diverse protein classes and multitargeting strategies enhances prediction accuracy.
  • Tailoring protocols based on protein class combinations can optimize indication-specific drug discovery and repurposing.

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.6K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.4K

Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
2.2K
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...
95
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...
319
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...
13.5K
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
93
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...
130