Accelerating fragment-based drug discovery using grand canonical nonequilibrium candidate Monte Carlo

Area of Science:

• Computational Chemistry
• Drug Discovery
• Molecular Modeling

Background:

• Fragment-based drug discovery (FBDD) is crucial for early-stage drug development.
• Computational tools aid FBDD in library design, virtual screening, and binding site identification.
• Molecular dynamics (MD) simulations are popular but face sampling challenges.

Purpose of the Study:

• To develop a novel computational method to overcome sampling limitations in FBDD.
• To introduce Grand Canonical Nonequilibrium Candidate Monte Carlo (GCNCMC) for fragment-based simulations.
• To demonstrate GCNCMC's efficacy in identifying binding sites and predicting binding affinities.

Main Methods:

• Development of Grand Canonical Nonequilibrium Candidate Monte Carlo (GCNCMC).
• GCNCMC attempts fragment insertion/deletion within a region of interest.
• Moves are accepted/rejected based on rigorous thermodynamic property tests.

Main Results:

• Fragment-based GCNCMC efficiently identifies occluded fragment binding sites.
• The method accurately samples multiple binding modes.
• Binding affinities are calculated without restraints, multiple mode handling, or symmetry corrections.

Conclusions:

• GCNCMC is an effective computational tool for fragment-based drug discovery.
• It overcomes key sampling limitations inherent in traditional molecular dynamics simulations.
• GCNCMC enables accurate prediction of fragment binding affinities and identification of binding sites.