RosettaAMRLD: A Reaction-Driven Approach for Structure-Based Drug Design from Combinatorial Libraries with Monte...

Area of Science:

• Computational chemistry
• Drug discovery
• Bioinformatics

Background:

• Structure-based *de novo* drug discovery is crucial for identifying novel therapeutics.
• Efficiently exploring vast chemical spaces and ensuring synthetic accessibility remain key challenges.

Purpose of the Study:

• To introduce Rosetta automated Monte Carlo reaction-based ligand design (RosettaAMRLD) for enhanced drug discovery.
• To demonstrate RosettaAMRLD's ability to generate novel, synthetically accessible ligands for various protein targets.

Main Methods:

• Integration of Monte Carlo Metropolis (MCM) algorithm with reaction-driven molecule proposal.
• Leveraging combinatorial ultralarge libraries for efficient chemical space sampling.
• Application to three drug-targeted protein classes.

Main Results:

• RosettaAMRLD generated novel, synthetically accessible ligands with active-like binding poses.
• Proposed ligands showed significantly improved docking scores compared to random sampling.
• Iterative refinement enhanced ligand quality, yielding molecules with superior *in silico* properties.

Conclusions:

• RosettaAMRLD effectively explores ultralarge chemical spaces for *de novo* ligand design.
• The method broadens applicability to novel targets by not requiring a known binder.
• RosettaAMRLD shows significant potential for early-stage drug discovery and development.