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

The Rosetta automated
Monte Carlo reaction-based ligand design
(RosettaAMRLD) integrates a Monte Carlo Metropolis (MCM) algorithm
and reaction-driven molecule proposal to enhance structure-based de novo drug discovery. By leveraging combinatorial ultralarge
libraries, RosettaAMRLD ensures synthetic accessibility, optimizing
protein–ligand interactions while efficiently sampling accessible
chemical space. Importantly, RosettaAMRLD can be initiated without
a known binder, broadening its applicability to novel pharmaceutical
targets. We applied RosettaAMRLD to three protein classes typically
targeted by drugs, demonstrating its ability to generate novel, synthetically
accessible ligands with active-like binding poses. Benchmark results
show that RosettaAMRLD can propose diverse ligands with significantly
improved docking scores compared to random sampling, and multiround
iteration further enhances output quality, resulting in molecules
with in silico properties exceeding those of known
actives. The method’s capability to explore ultralarge chemical
spaces and generate novel drug-like molecules highlights its potential
in early stage drug discovery.