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.

通过信息对齐方法，我们提出了一种学习细胞分子表示的信息瓶颈方法，整合分子和细胞反应数据构建上下文图，优化编码器的潜在表示以丢弃冗余结构信息，并通过对齐分子在上下文图中的不同特征空间来验证所提出的方法在药物疗效和分子性质预测方面的有效性。

细胞中的分子表示学习

Learning Molecular Representation in a Cell

The COVID-19 pandemic created a deluge of questionable and contradictory
scientific claims about drug efficacy -- an "infodemic" with lasting
consequences for science and society. In this work, we argue that NLP models
can help domain experts distill and understand the literature in this complex,
high-stakes area. Our task is to automatically identify contradictory claims
about COVID-19 drug efficacy. We frame this as a natural language inference
problem and offer a new NLI dataset created by domain experts. The NLI framing
allows us to create curricula combining existing datasets and our own. The
resulting models are useful investigative tools. We provide a case study of how
these models help a domain expert summarize and assess evidence concerning
remdisivir and hydroxychloroquine.

本文旨在探讨 NLP 模型如何帮助领域专家整理和理解复杂的 COVID-19 药物疗效相关文献。我们利用自己创建的 NLI 数据集结合已有数据集，使用自然语言推断技术来自动地识别关于 COVID-19 药物疗效的矛盾陈述，得出有价值的调查工具，并提供了一份 hydroxychloroquine 和 remdisivir 的案例研究。