DNA-Encoded Library (DEL) has proven to be a powerful tool that utilizes combinatorially constructed small molecules to facilitate highly-efficient screening assays. These selection experiments, involving multiple stages of washing, elution, and identification of potent binders via unique DNA barcodes, often generate complex data. This complexity can potentially mask the underlying signals, necessitating the application of computational tools such as machine learning to uncover valuable insights. We introduce a compositional deep probabilistic model of DEL data, DEL-Compose, which decomposes molecular representations into their mono-synthon, di-synthon, and tri-synthon building blocks and capitalizes on the inherent hierarchical structure of these molecules by modeling latent reactions between embedded synthons. Additionally, we investigate methods to improve the observation models for DEL count data such as integrating covariate factors to more effectively account for data noise. Across two popular public benchmark datasets (CA-IX and HRP), our model demonstrates strong performance compared to count baselines, enriches the correct pharmacophores, and offers valuable insights via its intrinsic interpretable structure, thereby providing a robust tool for the analysis of DEL data.

翻译：DNA编码文库(DEL)已被证明是一种强大工具，它利用组合化学构建的小分子实现高效筛选分析。这些涉及多轮洗涤、洗脱及通过独特DNA条形码鉴定强效结合物的选择实验，往往会产生复杂数据。这种复杂性可能掩盖潜在信号，亟需应用机器学习等计算工具来挖掘有价值的信息。我们提出了一种针对DEL数据的组合深度概率模型——DEL-Compose，该模型将分子表示分解为单合成子、双合成子和三合成子结构单元，并通过建模嵌入合成子间的潜在反应，充分利用这些分子固有的层次结构。此外，我们研究了改进DEL计数数据观测模型的方法，例如整合协变量因子以更有效地消除数据噪声。在两个常用公共基准数据集（CA-IX和HRP）上，我们的模型相较于计数基线方法展现出卓越性能，能够富集正确的药效团，并通过其内在的可解释结构提供有价值的见解，从而为DEL数据分析提供了稳健工具。