在细胞中学习分子表示

Learning Molecular Representation in a Cell

June 17, 2024

作者: Gang Liu, Srijit Seal, John Arevalo, Zhenwen Liang, Anne E. Carpenter, Meng Jiang, Shantanu Singh

cs.AI

摘要

预测药物在体内的疗效和安全性需要关于生物反应（例如细胞形态和基因表达）对小分子干扰的信息。然而，当前的分子表示学习方法无法全面展示这些干扰下的细胞状态，并且难以消除噪音，从而阻碍模型的泛化能力。我们引入信息对齐（InfoAlign）方法，通过信息瓶颈方法在细胞中学习分子表示。我们将分子和细胞反应数据作为节点整合到上下文图中，根据化学、生物和计算标准使用加权边将它们连接起来。对于训练批次中的每个分子，InfoAlign通过最小化目标优化编码器的潜在表示，以丢弃冗余的结构信息。一个充分性目标解码表示，使其与上下文图中分子邻域的不同特征空间对齐。我们证明了所提出的对齐充分性目标比现有基于编码器的对比方法更紧密。在实证上，我们验证了来自InfoAlign的表示在两个下游任务中的有效性：针对四个数据集中高达19种基线方法的分子性质预测，以及零样本分子形态匹配。

English

Predicting drug efficacy and safety in vivo requires information on biological responses (e.g., cell morphology and gene expression) to small molecule perturbations. However, current molecular representation learning methods do not provide a comprehensive view of cell states under these perturbations and struggle to remove noise, hindering model generalization. We introduce the Information Alignment (InfoAlign) approach to learn molecular representations through the information bottleneck method in cells. We integrate molecules and cellular response data as nodes into a context graph, connecting them with weighted edges based on chemical, biological, and computational criteria. For each molecule in a training batch, InfoAlign optimizes the encoder's latent representation with a minimality objective to discard redundant structural information. A sufficiency objective decodes the representation to align with different feature spaces from the molecule's neighborhood in the context graph. We demonstrate that the proposed sufficiency objective for alignment is tighter than existing encoder-based contrastive methods. Empirically, we validate representations from InfoAlign in two downstream tasks: molecular property prediction against up to 19 baseline methods across four datasets, plus zero-shot molecule-morphology matching.