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Related Concept Videos

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...
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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...

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Related Experiment Video

Updated: Jul 12, 2026

Preparing Protein Producing Synthetic Cells using Cell Free Bacterial Extracts, Liposomes and Emulsion Transfer
09:37

Preparing Protein Producing Synthetic Cells using Cell Free Bacterial Extracts, Liposomes and Emulsion Transfer

Published on: April 27, 2020

Virtual cell construction for artificial intelligence-driven drug discovery.

Yuran Jia1, Xiao Xing2, Haoyang Han3

  • 1Faculty of Computing, Harbin Institute of Technology, Harbin, China.

British Journal of Pharmacology
|July 10, 2026
PubMed
Summary

Virtual cells integrate multi-omics and AI to predict cellular responses to drug perturbations. These computational models enhance drug discovery by improving target prioritization and predicting therapeutic outcomes.

Keywords:
artificial intelligencedrug discoverymulti‐omicsvirtual cell

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Last Updated: Jul 12, 2026

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Published on: May 19, 2018

Area of Science:

  • Computational biology
  • Pharmacology
  • Genomics

Background:

  • Cells are central to disease and drug response, but predicting their behavior in different contexts is challenging.
  • Understanding cellular responses to interventions requires integrating diverse biological data.

Purpose of the Study:

  • To review the foundations and applications of virtual cell systems in drug discovery.
  • To explore how computational frameworks model cellular states and predict perturbation responses.

Main Methods:

  • Leveraging single-cell and spatial multi-omics, perturbation profiling, and artificial intelligence.
  • Developing integrative computational frameworks representing cellular states, context, and responses.
  • Utilizing representation learning, multimodal integration, and mechanistic inference.

Main Results:

  • Virtual cells offer unified models for cellular systems and perturbation responses.
  • These frameworks support target prioritization and drug response prediction.
  • Advancements enable better design of combination therapies.

Conclusions:

  • Virtual cells are powerful predictive systems for therapeutic discovery.
  • Further development is needed to address challenges and expand future directions.
  • These models are crucial for advancing precision medicine.