发表机构
The Ohio State University（俄亥俄州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对药物发现难题，提出基于条件扩散的SBDD框架conDitar-dev，含msPRL、conDitar和paOPT三个模块。在新基准上表现出色，在ADMET属性上性能提升显著，应用于两个靶点验证了其生成可开发分子的能力，还发现了新抑制剂及药物重新定位机会。
AI 中文摘要
药物发现和开发既耗时又资源密集，这促使人们采用计算方法，如用于从头药物设计的扩散模型。许多此类模型遵循基于结构的药物设计（SBDD）范式，生成适合目标结合口袋的分子。然而，现有的基于扩散的SBDD方法通常将口袋和配体表示学习结合在一起，仅在原子水平上对相互作用进行建模，并且将结合亲和力置于其他可开发性属性之上。在此，我们引入了conDitar-dev，这是一个基于条件扩散的SBDD框架，用于生成具有强结合亲和力和良好ADMET属性的配体。它由三个模块组成：msPRL，一个预训练的多尺度口袋表示学习模块；conDitar，一个由msPRL表示引导的口袋条件扩散模型；以及paOPT，一种用于优化配体可开发性的生成时方法。在一个新策划的人类疾病靶点基准上，conDitar优于现有的SBDD基线，平均结合分数达到-8.85 kcal/mol。在五个ADMET属性方面，conDitar-dev比conDitar性能提高了73%。为了进一步验证conDitar-dev生成可开发分子的能力，我们将其应用于两个经过验证的可成药靶点：程序性死亡配体1（PD-L1）和集落刺激因子1受体（CSF1R）蛋白。通过实验合成并对排名靠前的生成设计分子及其类似物进行了生物学测试。conDitar-dev直接为PD-L1生成的两个分子的SPR衍生$K_D$值分别为3.49和3.75 μM。基于conDitar-dev设计分子的命中扩展鉴定出了IC$_{50}$值低至200 nM的选择性CSF1R抑制剂，同时还发现了药物重新定位的机会。
英文摘要
Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design. Many such models follow the structure-based drug design (SBDD) paradigm, generating molecules to fit a target binding pocket. However, existing diffusion-based SBDD methods typically couple pocket and ligand representation learning, model interactions only at the atom level, and prioritize binding affinity over other developability properties. Here, we introduce conDitar-dev, a conditional diffusion-based SBDD framework for generating ligands with strong binding affinities and favorable ADMET properties. It consists of three modules: msPRL, a pretrained multi-scale pocket representation learning module; conDitar, a pocket-conditioned diffusion model guided by msPRL representations; and paOPT, a generation-time method for optimizing ligand developability. On a newly curated benchmark of human disease targets, conDitar outperforms state-of-the-art SBDD baselines, achieving an average binding score of -8.85 kcal/mol. Across five ADMET properties, conDitar-dev improves performance by up to 73% over conDitar. To further validate the abilities of conDitar-dev to generate developable molecules, we have applied it to two validated druggable targets: programmed death-ligand 1 (PD-L1) and colony-stimulating factor 1 receptor (CSF1R) proteins. Top-ranked generatively designed molecules and their analogs have been experimentally synthesized and biologically tested. Two molecules generated directly by conDitar-dev for PD-L1 exhibited SPR-derived $K_D$ values of 3.49 and 3.75 $μ$M, respectively. Hit expansion based on conDitar-dev-designed molecules identified selective CSF1R inhibitors with IC$_{50}$ values as low as 200 nM, while also uncovering opportunities for drug repositioning.
