Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

靶向纳米颗粒药物载体的开发通常需要复杂的合成方案，涉及超分子自组装和化学修饰。这些过程通常难以预测、执行和控制。我们在此描述了一种靶向药物递送系统，该系统根据前体分子（即药物本身）的分子结构，准确且定量地预测自组装成纳米颗粒。这些药物借助硫酸化吲哚菁组装成颗粒，载药量高达 90%。我们设计了定量结构纳米颗粒组装预测（QSNAP）模型来识别和验证电拓扑分子描述符作为纳米组装和纳米颗粒尺寸的高度预测指标。由此产生的纳米粒子选择性地将激酶抑制剂靶向表达caveolin-1的人类结肠癌和本土肝癌模型，从而产生显着的治疗效果，同时避免健康皮肤中的pERK抑制。这一发现使得基于药物有效负载选择的定量模型的纳米药物的计算设计成为可能。