Tang Yidan, Moretti Rocco, Meiler Jens
Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37240, United States.
Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240, United States.
J Chem Inf Model. 2025 Jun 23;65(12):5945-5959. doi: 10.1021/acs.jcim.5c00497. Epub 2025 Jun 11.
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 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 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.
罗塞塔基于反应的自动蒙特卡洛配体设计（RosettaAMRLD）整合了蒙特卡洛梅特罗波利斯（MCM）算法和反应驱动的分子提议，以加强基于结构的药物发现。通过利用组合超大型文库，RosettaAMRLD确保了合成可及性，优化了蛋白质-配体相互作用，同时有效地对可及化学空间进行采样。重要的是，RosettaAMRLD可以在没有已知结合剂的情况下启动，拓宽了其对新型药物靶点的适用性。我们将RosettaAMRLD应用于通常被药物靶向的三类蛋白质，证明了它能够生成具有类似活性结合构象的新型、合成可及的配体。基准测试结果表明，与随机采样相比，RosettaAMRLD可以提出具有显著提高的对接分数的多样配体，多轮迭代进一步提高了输出质量，产生了性质超过已知活性物质的分子。该方法探索超大型化学空间并生成新型类药物分子的能力突出了其在早期药物发现中的潜力。
