Protein–protein interactions (PPIs) mediate nearly every cellular process and represent attractive targets for modulating disease states but are challenging to target with small molecules. Despite this, several PPI inhibitors (iPPIs) have entered clinical trials, and a growing number of PPIs have become validated drug targets. However, high-throughput screening efforts still endure low hit rates mainly because of the use of unsuitable screening libraries. Here, we describe the collective effort of a French consortium to build, select, and store in plates a unique chemical library dedicated to the inhibition of PPIs. Using two independent predictive models and two updated databases of experimentally confirmed PPI inhibitors developed by members of the consortium, we built models based on different training sets, molecular descriptors, and machine learning methods. Independent statistical models were used to select putative PPI inhibitors from large commercial compound collections showing great complementarity. Medicinal chemistry filters were applied to remove undesirable structures from this set (such as PAINS, frequent hitters, and toxic compounds) and to improve drug likeness. The remaining compounds were subjected to a clustering procedure to reduce the final size of the library while maintaining its chemical diversity. In practice, the library showed a 46-fold activity rate enhancement when compared to a non-iPPI-enriched diversity library in high-throughput screening against the CD47-SIRPα PPI. The Fr-PPIChem library is plated in 384-well plates and will be distributed on demand to the scientific community as a powerful tool for discovering new chemical probes and early hits for the development of potential therapeutic drugs.

中文翻译：

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Fr-PPIChem：致力于蛋白质-蛋白质相互作用的学术化合物库。

蛋白质-蛋白质相互作用 (PPI) 介导几乎所有细胞过程，是调节疾病状态的有吸引力的靶点，但用小分子靶向具有挑战性。尽管如此，多种质子泵抑制剂（iPPI）已进入临床试验，并且越来越多的质子泵抑制剂已成为经过验证的药物靶点。然而，高通量筛选工作仍然存在低命中率，这主要是由于使用了不合适的筛选文库。在这里，我们描述了一个法国财团的集体努力，他们建立、选择并在平板中存储了一个致力于抑制 PPI 的独特化学库。我们使用两个独立的预测模型和两个由联盟成员开发的经实验证实的 PPI 抑制剂的更新数据库，基于不同的训练集、分子描述符和机器学习方法构建了模型。使用独立的统计模型从显示出巨大互补性的大型商业化合物集合中选择假定的 PPI 抑制剂。应用药物化学过滤器从该组中去除不需要的结构（例如疼痛、频繁击球和有毒化合物）并提高药物相似度。剩余的化合物经过聚类程序以减小文库的最终大小，同时保持其化学多样性。实际上，在针对 CD47-SIRPα PPI 的高通量筛选中，与非 iPPI 富集的多样性文库相比，该文库的活性率提高了 46 倍。Fr-PPIChem 文库置于 384 孔板中，将根据需要分发给科学界，作为发现新化学探针和开发潜在治疗药物的早期命中的强大工具。