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

Search research articles

Related Experiment Videos

Can we separate active from inactive conformations?

David J Diller¹, Kenneth M Merz

¹Department of Molecular Modeling, Pharmacopeia Inc, Princeton, NJ 08543-5350, USA. ddiller@pharmacop.com

Journal of Computer-Aided Molecular Design

|August 22, 2002

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Quantum-Centric Alchemical Free Energy Calculations.

Journal of chemical theory and computation·2026

Same author

An N-terminal amphipathic helix governs activity and conformational dynamics of Nramp metal transporters.

The Journal of biological chemistry·2026

Same author

An N-terminal amphipathic helix governs activity and conformational dynamics of Nramp metal transporters.

bioRxiv : the preprint server for biology·2026

Same author

Molecular Quantum Computations on a Protein.

Journal of chemical theory and computation·2026

Same author

Advancing Reproducibility and Open Data in Theoretical and Computational Chemistry.

Journal of chemical theory and computation·2026

Same author

Zinc-induced exposure of an LxL motif drives clathrin-mediated endocytosis of the zinc transceptor ZIP4.

The Journal of biological chemistry·2026

Same journal

Integrating evolutionary and compositional features with ML and DL for robust and interpretable druggable protein prediction.

Journal of computer-aided molecular design·2026

Same journal

QUAD: a composite risk framework integrating uncertainty, applicability domain, and model disagreement for reliable QSAR predictions.

Journal of computer-aided molecular design·2026

Same journal

Comparative quantum-chemical investigation of 2-chloro-N-(4-methoxyphenyl)acetamide and 2-(4-methoxyphenylamino)-2-oxoethyl meth/acrylate: DFT, TD-DFT, and non-covalent interaction analyses.

Journal of computer-aided molecular design·2026

Same journal

Discovery of a novel CaMKⅡα inhibitor by machine learning, molecular docking and molecular dynamics simulation.

Journal of computer-aided molecular design·2026

Same journal

Explainable QSAR models of 5-HT1A receptor ligands using conceptual DFT descriptors and no-code machine learning tools.

Journal of computer-aided molecular design·2026

Same journal

Computational investigation of antioxidant activities and mechanisms of catechol analogues through a DFT study.

Journal of computer-aided molecular design·2026

See all related articles

Researchers identified molecular descriptors that distinguish active drug conformations from random ones. These descriptors, including solvent accessible surface area, can improve molecular modeling by filtering unlikely binding poses.

Area of Science:

Computational chemistry
Structural biology
Drug discovery

Background:

Molecular modeling techniques like docking and pharmacophore modeling require accurate small molecule conformations.
Identifying the specific active conformation of a small molecule bound to a protein target is crucial for these methods.
Distinguishing active from inactive conformations is a significant challenge in computational drug design.

Purpose of the Study:

To identify three-dimensional (3D) descriptors capable of differentiating between active and random low-energy conformations of small molecules.
To evaluate the utility of these descriptors in improving the efficiency and accuracy of molecular modeling workflows.

Main Methods:

Utilized 65 protein-ligand complexes from the Protein Data Bank.

Related Experiment Videos

Compared the active conformation of each ligand with randomly generated low-energy conformations.

Analyzed descriptors such as solvent accessible surface area, number of internal interactions, and radius of gyration.

Main Results:

Active conformations were found to be less compact than random conformations.
Key descriptors indicating reduced compactness include higher solvent accessible surface area, fewer internal interactions, and a larger radius of gyration for active conformations.
These descriptors demonstrated effectiveness in separating active from random conformations.

Conclusions:

Selected 3D descriptors can effectively distinguish active small molecule conformations from random ones.
These descriptors can be employed as weights to bias conformational searches towards more biologically relevant poses.
Alternatively, they can serve as filters to eliminate improbable conformations, thereby enhancing molecular modeling accuracy and efficiency in drug discovery.