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Updated: Jun 23, 2026
16:14
Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs
Jose L Medina-Franco1, Karina Martínez-Mayorga, Andreas Bender
1Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, USA. jmedina@tpims.org
Journal of Chemical Information and Modeling
|May 13, 2009
Summary
This study introduces a consensus approach to map chemical activity landscapes, revealing how molecular features influence drug-target interactions. This method aids in optimizing drug discovery by identifying challenging compounds for virtual screening.
Area of Science:
- Computational chemistry
- Medicinal chemistry
- Drug discovery
Background:
- Activity landscape characterization is crucial for lead optimization and virtual screening.
- Understanding structure-activity relationships guides the design of potent and selective compounds.
Purpose of the Study:
- To present a general protocol for systematically exploring the activity landscape of a lead compound series.
- To introduce the concept of consensus activity cliffs by integrating multiple descriptor types.
Main Methods:
- Utilized 11 2D and 3D structural representations for activity landscape exploration.
- Employed orthogonal descriptors including MACCS keys, pharmacophores, fingerprints, ROCS, and TARIS.
- Analyzed a dataset of 48 bicyclic guanidines (BCGs) with kappa-opioid receptor binding affinity.
Main Results:
- Identified potential differences in binding modes for BCGs based on the presence or absence of a methoxybenzyl group.
- The most potent compound (37 nM IC50) was found in multiple consensus cliffs, posing a challenge for virtual screening.
- Highlighted the importance of screening dense combinatorial libraries in 'cliff-rich' activity landscape regions.
Conclusions:
- The developed protocol provides a robust method for activity landscape characterization.
- Consensus activity cliffs offer a more comprehensive understanding of structure-activity relationships.
- This approach can be applied to diverse datasets for developing predictive computational models.
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