共无定形混合物的智能机械化学设计,Crystal Growth & Design

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共无定形混合物的智能机械化学设计
Crystal Growth & Design ( IF 3.2 ) Pub Date : 2022-04-04 , DOI: 10.1021/acs.cgd.1c01442
Jan R. Gröls ₁ , Bernardo Castro-Dominguez ₁

Affiliation

机械化学是一种利用机械力促进化学反应的绿色制备方法。该技术已显示出其作为多种溶剂型工艺（例如，共晶体、金属络合物或聚合物的合成）的有效替代方案的潜力；然而，预测其反应性仍然是一个挑战。在这项研究中，开发了一个机器学习模型来深入了解这一过程并预测共无定形混合物的形成。当结晶活性药物成分的分子排列被破坏并通过二级结构的协同存在而保持在“随机”状态时，就会产生共无定形混合物。共无定形混合物可以设计为多组分药物，并且通常表现出增强的溶解度和生物利用度。在这项工作中，我们生成了一个包含 418 个内部非晶化实验的数据库，这些实验对于当前的文献来说是新颖的，以及用于预测目的和推断基本见解的知情数据分析（即梯度提升和神经网络）。通过使用 2066 个化学描述符来训练梯度提升模型，实现了 >73% 的预测准确度。该模型进一步用于预测和合成六种新型共无定形混合物。我们期望这个新的数据库和预测模型将有助于设计新的药物和推进可持续的无溶剂工艺。实现了> 73％的预测准确度。该模型进一步用于预测和合成六种新型共无定形混合物。我们期望这个新的数据库和预测模型将有助于设计新的药物和推进可持续的无溶剂工艺。实现了> 73％的预测准确度。该模型进一步用于预测和合成六种新型共无定形混合物。我们期望这个新的数据库和预测模型将有助于设计新的药物和推进可持续的无溶剂工艺。

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Intelligent Mechanochemical Design of Co-Amorphous Mixtures

Mechanochemistry is a green preparation method that uses mechanical forces to prompt chemical reactions. This technique has shown its potential as an efficient alternative for several solvent-based processes (e.g., synthesis of co-crystals, metal complexes, or polymers); however, predicting its reactivity remains a challenge. In this study, a machine learning model was developed to gain insights into this process and predict the formation of co-amorphous mixtures. Co-amorphous mixtures are produced when the molecular arrangement of a crystalline active pharmaceutical ingredient is disrupted and maintained at “random” by the synergistic presence of a secondary structure. Co-amorphous mixtures can be designed as multicomponent drugs and often display an enhanced solubility and bioavailability. In this work, we generated a database of 418 in-house amorphization experiments, novel to current literature, and informed data analysis (i.e., gradient boosting and neural networks) for predictive purposes and to extrapolate fundamental insights. By using 2066 chemical descriptors to train a gradient boost model, a predictive accuracy of >73% was achieved. This model was further used to predict and synthesize six novel co-amorphous mixtures. We expect that this novel database and the predictive model will aid at designing novel pharmaceuticals and advancing sustainable solvent-free processes.

更新日期：2022-04-04

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