嵌合抗原受体 (CAR) T 细胞代表了一种革命性的免疫疗法，它允许通过源自单克隆抗体 (mAb) 的独特单链片段变量 (scFv) 来特异性识别肿瘤。因此，scFv 选择是 CAR 构建的基本步骤，以确保针对肿瘤抗原结合的 CAR 信号传导准确有效。然而，比较不同 scFv 衍生 CAR 的传统体外和体内生物学方法既昂贵又费力。为了在 CAR-T 细胞工程之前预测最佳的 scFv 结合，我们对不同的抗 CD30 mAb 克隆进行了人工智能 (AI) 引导的分子对接和分子动力学分析。虚拟计算 scFv 筛选在结合能力和抗肿瘤功效方面分别显示出与表面等离子共振 (SPR) 和功能性 CAR-T 细胞体外和体内测定相当的结果。所提出的快速、低成本的计算机分析有可能推动新型 CAR 结构的开发，对减少时间、成本和实验动物使用需求产生重大影响。

Chimeric antigen receptor (CAR) T cells represent a revolutionary immunotherapy that allows specific tumor recognition by a unique single-chain fragment variable (scFv) derived from monoclonal antibodies (mAbs). scFv selection is consequently a fundamental step for CAR construction, to ensure accurate and effective CAR signaling toward tumor antigen binding. However, conventional in vitro and in vivo biological approaches to compare different scFv-derived CARs are expensive and labor-intensive. With the aim to predict the finest scFv binding before CAR-T cell engineering, we performed artificial intelligence (AI)-guided molecular docking and steered molecular dynamics analysis of different anti-CD30 mAb clones. Virtual computational scFv screening showed comparable results to surface plasmon resonance (SPR) and functional CAR-T cell in vitro and in vivo assays, respectively, in terms of binding capacity and anti-tumor efficacy. The proposed fast and low-cost in silico analysis has the potential to advance the development of novel CAR constructs, with a substantial impact on reducing time, costs, and the need for laboratory animal use.