Drug discovery has entered a new period of vigorous development with advanced technologies such as DNA-encoded library (DEL) and artificial intelligence (AI). The previous DEL-AI combination has been successfully applied in the drug discovery of classical kinase and receptor targets mainly based on the known scaffold. So far, there is no report of the DEL-AI combination on inhibitors targeting protein-protein interaction, including those undruggable targets with few or unknown active scaffolds. Here, we applied DEL technology on the T cell immunoglobulin and ITIM domain (TIGIT) target, resulting in the unique hit compound 1 (IC50 = 20.7 μM). Based on the screening data from DEL and hit derivatives a1-a34, a machine learning (ML) modeling process was established to address the challenge of poor sample distribution uniformity, which is also frequently encountered in DEL screening on new targets. In the end, the established ML model achieved a satisfactory hit rate of about 75% for derivatives in a high-scored area.
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机译：随着 DNA 编码文库 （DEL） 和人工智能 （AI） 等先进技术的出现，药物发现进入了蓬勃发展的新时期。既往的 DEL-AI 联合疗法主要基于已知的支架，已成功应用于经典激酶和受体靶点的药物发现。到目前为止，还没有关于 DEL-AI 联合治疗靶向蛋白质-蛋白质相互作用的抑制剂的报道，包括那些活性支架很少或未知的不可成药靶点。在这里，我们将 DEL 技术应用于 T 细胞免疫球蛋白和 ITIM 结构域 （TIGIT） 靶标，产生了独特的命中化合物 1 （IC50 = 20.7 μM）。基于来自 DEL 和 hit 衍生物 a1-a34 的筛选数据，建立了机器学习 （ML） 建模流程，以解决样本分布均匀性差的挑战，这也是在新靶标的 DEL 筛选中经常遇到的挑战。最终，建立的 ML 模型在高分领域的衍生品中取得了令人满意的约 75% 的命中率。
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