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Updated: Jan 6, 2026
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
Published on: June 20, 2025
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Sampling High-Dimensional Conformational Free Energy Landscapes of Active Pharmaceutical Ingredients
Alexandre Ferreira1, Rui Guo2, Ivan Marziano2
1Thomas Young Centre and Department of Chemical Engineering, University College London, London Wc1e 7je, United Kingdom.
Journal of Chemical Theory and Computation
|December 4, 2025
Summary
This study introduces a gridless method to map molecular conformational free energy landscapes in high dimensions. The approach accurately predicts solvent-dependent molecular structures, aiding drug design and understanding molecular behavior.
Area of Science:
- Computational Chemistry
- Molecular Dynamics
- Biophysics
Background:
- Conventional methods for computing conformational free energy surfaces (FES) face dimensionality limitations.
- High-dimensional FES are crucial for understanding complex molecular behavior and interactions.
Purpose of the Study:
- To develop a gridless framework for computing high-dimensional FES.
- To enable accurate mapping of conformational ensembles for flexible molecules.
- To investigate solvent-dependent conformational preferences of drug molecules.
Main Methods:
- Combined concurrent well-tempered metadynamics with Density Peaks Advanced (DPA) clustering.
- Employed local density estimation and Zwanzig reweighting for per-configuration free energy assignment.
- Identified conformers as density peaks in torsional angle space for resolution-independent mapping.
Main Results:
- Successfully reproduced the FES of alanine dipeptide.
- Extended the method to analyze 4-, 7-, and 11-dimensional torsional angle spaces.
- Predicted solvent-induced conformational shifts in bicalutamide, aligning with experimental data.
Conclusions:
- The gridless framework offers a scalable solution for high-dimensional FES computation.
- This approach has direct relevance for studying molecular polymorphism, solvation effects, and drug design.
- The method provides direct, resolution-independent mapping of conformational ensembles.
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