We explore the possibility of a deep learning (DL) platform that steers drug design to target proteins by inducing binding-competent conformations. We deal with the fact that target proteins are usually not fixed targets but structurally adapt to the ligand in ways that need to be predicted to enable pharmaceutical discovery. In contrast with protein folding predictors, the proposed DL system integrates signals for structural disorder to predict conformations in floppy regions of the target protein that rely on associations with the purposely designed drug to maintain their structural integrity. This is tantamount to solve the drug-induced folding problem within an AI-empowered drug discovery platform. Preliminary testing of the proposed DL platform reveals that it is possible to infer the induced folding ensemble from which a therapeutically targetable conformation gets selected by DL-instructed drug design.

中文翻译：

---

人工智能通过解决诱导的折叠问题来指导药物靶向蛋白质。

我们探索了深度学习（DL）平台的可能性，该平台可通过诱导结合型构象引导药物设计靶向蛋白质。我们处理的事实是，靶蛋白通常不是固定的靶标，而是在结构上以需要预测的方式适应配体，以实现药物发现。与蛋白质折叠预测因子相比，拟议的DL系统整合了结构异常信号，以预测目标蛋白的软区域中的构象，该构象依赖于与专门设计的药物的关联来维持其结构完整性。这无异于在具有AI功能的药物发现平台中解决药物引起的折叠问题。