DNA-encoded library (DEL) screening and quantitative structure-activity
relationship (QSAR) modeling are two techniques used in drug discovery to find
small molecules that bind a protein target. Applying QSAR modeling to DEL data
can facilitate the selection of compounds for off-DNA synthesis and evaluation.
Such a combined approach has been shown recently by training binary classifiers
to learn DEL enrichments of aggregated "disynthons" to accommodate the sparse
and noisy nature of DEL data. However, a binary classifier cannot distinguish
between different levels of enrichment, and information is potentially lost
during disynthon aggregation. Here, we demonstrate a regression approach to
learning DEL enrichments of individual molecules using a custom negative
log-likelihood loss function that effectively denoises DEL data and introduces
opportunities for visualization of learned structure-activity relationships
(SAR). Our approach explicitly models the Poisson statistics of the sequencing
process used in the DEL experimental workflow under a frequentist view. We
illustrate this approach on a dataset of 108k compounds screened against CAIX,
and a dataset of 5.7M compounds screened against sEH and SIRT2. Due to the
treatment of uncertainty in the data through the negative log-likelihood loss
function, the models can ignore low-confidence outliers. While our approach
does not demonstrate a benefit for extrapolation to novel structures, we expect
our denoising and visualization pipeline to be useful in identifying SAR trends
and enriched pharmacophores in DEL data. Further, this approach to
uncertainty-aware regression is applicable to other sparse or noisy datasets
where the nature of stochasticity is known or can be modeled; in particular,
the Poisson enrichment ratio metric we use can apply to other settings that
compare sequencing count data between two experimental conditions.

本研究中使用回归方法和 Poisson 统计学建模 DEL 数据，计算药物的定量结构 - 活性关系并可视化学习的结果。

使用具有不确定性感知的概率损失函数对 DNA 编码库计数数据进行机器学习

Machine learning on DNA-encoded library count data using an  uncertainty-aware probabilistic loss function

In this paper, we propose an end-to-end trainable regression approach for
human pose estimation from still images. We use the proposed Soft-argmax
function to convert feature maps directly to joint coordinates, resulting in a
fully differentiable framework. Our method is able to learn heat maps
representations indirectly, without additional steps of artificial ground truth
generation. Consequently, contextual information can be included to the pose
predictions in a seamless way. We evaluated our method on two very challenging
datasets, the Leeds Sports Poses (LSP) and the MPII Human Pose datasets,
reaching the best performance among all the existing regression methods and
comparable results to the state-of-the-art detection based approaches.

本研究提出了一种端到端训练的回归方法，使用 Soft-argmax 函数将特征映射直接转换为关节坐标，避免了人工生成的基准图像，使得上下文信息可以无缝地包含在姿势预测中。实验证明该方法在人类姿势估计方面具有较高性能，可应用于多种复杂数据集。