Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Structure-Activity Relationships and Drug Design

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

EC-Dock: A Fast Equivariant Consistency Model for Molecular Docking and Virtual Screening.

Journal of chemical information and modeling·2026

Same author

Stimuli-Responsive Triplet Emission and X-Ray Scintillation via Reversible Structural Switching in Pyromellitic Diimide Cocrystals.

Angewandte Chemie (International ed. in English)·2026

Same author

A Return to the Logic-First Spirit of Corey's Retrosynthetic Analysis, Now Implemented in Modern Data-Driven CASP.

ACS central science·2026

Same author

Integrating Machine Learning Interatomic Potentials with MMPBSA for Accurate Protein-Ligand Binding Free Energy Calculations.

The journal of physical chemistry. B·2026

Same author

Improving protein-ligand complex generation with force field guidance.

Journal of cheminformatics·2026

Same author

High-speed infrared thermistor bolometer based on the Gires-Tournois resonator architecture.

Optics letters·2026

Same journal

PFASGroups: An Open-Source Framework for Automated Identification, Structural Classification, and Prioritization of Per- and Polyfluoroalkyl Substances.

Journal of chemical information and modeling·2026

Same journal

DeepKbhb: Context-Aware Prediction of Human Lysine β-Hydroxybutyrylation Sites.

Journal of chemical information and modeling·2026

Same journal

HyperDC: A Non-Uniform Hypergraph Framework for Dual- and Higher-Order Drug Combination Recommendation Across Diverse Complex Diseases.

Journal of chemical information and modeling·2026

Same journal

MolPy: A Large Language Model-Friendly Toolkit for Reactive Topology Editing in Polymer Simulations.

Journal of chemical information and modeling·2026

Same journal

Molecular Mechanisms of KIT Receptor Dimerization and Oncogenic Activation Revealed by Multiscale Simulations.

Journal of chemical information and modeling·2026

Same journal

Structural and Thermodynamic Discrimination between Agonists and Antagonists of Retinoic Acid Receptor γ and the Vitamin D Receptor.

Journal of chemical information and modeling·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

ProSAR: a new methodology for combinatorial library design.

Hongming Chen¹, Ulf Börjesson, Ola Engkvist

¹DECS GCS Computational Chemistry, AstraZeneca R&D Mölndal, Pepparedsleden 1, SE-43183 Mölndal, Sweden. hongming.chen@astrazeneca.com

Journal of Chemical Information and Modeling

|May 13, 2009

Summary

This summary is machine-generated.

This study introduces ProSAR, a novel method for reagent selection in chemical library design. ProSAR optimizes pharmacophore diversity to create libraries that effectively support structure-activity relationship studies.

More Related Videos

Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

Published on: December 6, 2017

Primer-Free Aptamer Selection Using A Random DNA Library

Primer-Free Aptamer Selection Using A Random DNA Library

Published on: July 26, 2010

Related Experiment Videos

Last Updated: Jun 23, 2026

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

Published on: December 6, 2017

Primer-Free Aptamer Selection Using A Random DNA Library

Primer-Free Aptamer Selection Using A Random DNA Library

Published on: July 26, 2010

Area of Science:

Medicinal Chemistry
Computational Chemistry
Drug Discovery

Background:

Combinatorial chemistry enables rapid synthesis of diverse compound libraries.
Effective library design requires strategic reagent selection to maximize coverage of relevant chemical space.
Structure-activity relationship (SAR) studies are crucial for optimizing drug candidates.

Purpose of the Study:

To develop an automated method for reagent selection in combinatorial library design.
To enhance the utility of designed libraries for SAR studies.
To incorporate product property profiles into the library design process.

Main Methods:

Utilized topological (2D) pharmacophore fingerprints for reagent characterization.
Employed Shannon entropy optimization to select reagents maximizing pharmacophore diversity.
Integrated product property profiles (e.g., solubility, hERG risk) using a genetic algorithm.
Compared the ProSAR methodology against a diversity-based strategy minimizing Tanimoto similarity.

Main Results:

Designed libraries demonstrated high coverage of pharmacophores present in known active compounds.
The ProSAR method successfully optimized both pharmacophore diversity and product property profiles simultaneously.
Comparison showed ProSAR outperformed a diversity-based strategy in library design.

Conclusions:

The ProSAR methodology provides an effective strategy for designing combinatorial libraries.
This approach facilitates the generation of libraries well-suited for SAR exploration and lead optimization.
Simultaneous optimization of chemical diversity and desirable product properties is achievable.