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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 its...
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...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Protein-protein Interfaces02:04

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...
Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though pharmacologically...

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Related Experiment Video

Updated: Jun 10, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

Using Computational Intelligence to Connect Data and Drug Design in Medicinal Chemistry: A Review.

Sujeet Maurya1, Amrita Singh1, Ajay Kumar1

  • 1Department of Pharmaceutical Science, School of Pharmaceutical Sciences, Chhatrapati Shahu Ji Maharaj University, Kanpur, 208024, Uttar Pradesh, India.

Current Drug Discovery Technologies
|June 9, 2026
PubMed
Summary

Computational intelligence (CI) and big data are revolutionizing drug discovery. These data-driven methods accelerate the identification and development of safer, more effective medicines, overcoming limitations of traditional approaches.

Keywords:
Big dataBioin-formaticsComputational intelligenceDe novo drug designDeep learningDrug discoveryExplainable AIGenerative modelsMachine learningMedicinal chemistry.Precision medicine; AI in pharmacologyQSARTarget identificationVirtual screening

Related Experiment Videos

Last Updated: Jun 10, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

Area of Science:

  • Medicinal Chemistry
  • Computational Biology
  • Artificial Intelligence in Drug Discovery

Background:

  • Traditional drug discovery is slow, expensive, and has high failure rates.
  • Big data and computational intelligence (CI) offer data-driven, algorithmic solutions.
  • Machine learning, deep learning, and hybrid models enhance decision-making in drug design.

Purpose of the Study:

  • To provide a comprehensive overview of CI techniques in drug discovery.
  • To explore the role of massive chemical and biological databases.
  • To highlight the integration of CI across key drug discovery stages.

Main Methods:

  • Review of CI techniques: supervised/unsupervised learning, neural networks, evolutionary algorithms, fuzzy logic.
  • Exploration of databases: PubChem, ChEMBL, DrugBank, Protein Data Bank.
  • Discussion of CI integration in target identification, hit discovery, lead optimization, and de novo design.

Main Results:

  • CI techniques are reshaping drug discovery pipelines.
  • Data quality, curation, and standardization are crucial for CI success.
  • Examples like AlphaFold, Atomwise, and In silico Medicine demonstrate CI's impact.

Conclusions:

  • Integrating CI with big data accelerates drug discovery and development.
  • This approach leads to the creation of safer and more effective drugs.
  • Robust data infrastructure is essential for maximizing CI benefits.