Obtaining accurate and valid information for drug molecules is a crucial and
challenging task. However, chemical knowledge and information have been
accumulated over the past 100 years from various regions, laboratories, and
experimental purposes. Little has been explored in terms of the
out-of-distribution (OOD) problem with noise and inconsistency, which may lead
to weak robustness and unsatisfied performance. This study proposes a novel
benchmark ADMEOOD, a systematic OOD dataset curator and benchmark specifically
designed for drug property prediction. ADMEOOD obtained 27 ADME (Absorption,
Distribution, Metabolism, Excretion) drug properties from Chembl and relevant
literature. Additionally, it includes two kinds of OOD data shifts: Noise Shift
and Concept Conflict Drift (CCD). Noise Shift responds to the noise level by
categorizing the environment into different confidence levels. On the other
hand, CCD describes the data which has inconsistent label among the original
data. Finally, it tested on a variety of domain generalization models, and the
experimental results demonstrate the effectiveness of the proposed partition
method in ADMEOOD: ADMEOOD demonstrates a significant difference performance
between in-distribution and out-of-distribution data. Moreover, ERM (Empirical
Risk Minimization) and other models exhibit distinct trends in performance
across different domains and measurement types.

该研究提出了一种针对药物属性预测的系统的 OOD 数据集整理器和基准 ADMEOOD，其中包括噪声转移和概念冲突漂移两种 OOD 数据转移方式，并经过多个领域泛化模型测试，实验结果表明 ADMEOOD 在分布内和分布外数据上有明显不同的性能表现，ERM 和其他模型在不同领域和测量类型上展现出不同的趋势。

ADMEOOD: 药物性质预测的超出分布基准

ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction

Logic Synthesis (LS) plays a vital role in chip design -- a cornerstone of
the semiconductor industry. A key task in LS is to transform circuits --
modeled by directed acyclic graphs (DAGs) -- into simplified circuits with
equivalent functionalities. To tackle this task, many LS operators apply
transformations to subgraphs -- rooted at each node on an input DAG --
sequentially. However, we found that a large number of transformations are
ineffective, which makes applying these operators highly time-consuming. In
particular, we notice that the runtime of the Resub and Mfs2 operators often
dominates the overall runtime of LS optimization processes. To address this
challenge, we propose a novel data-driven LS operator paradigm, namely PruneX,
to reduce ineffective transformations. The major challenge of developing PruneX
is to learn models that well generalize to unseen circuits, i.e., the
out-of-distribution (OOD) generalization problem. Thus, the major technical
contribution of PruneX is the novel circuit domain generalization framework,
which learns domain-invariant representations based on the
transformation-invariant domain-knowledge. To the best of our knowledge, PruneX
is the first approach to tackle the OOD problem in LS operators. We integrate
PruneX with the aforementioned Resub and Mfs2 operators. Experiments
demonstrate that PruneX significantly improves their efficiency while keeping
comparable optimization performance on industrial and very large-scale
circuits, achieving up to $3.1\times$ faster runtime.

提出了一种新的数据驱动型逻辑综合运算符模型，名为 PruneX，用于减少无效转换并解决逻辑综合中的 OOD 问题。通过集成 PruneX 与现有的 Resub 和 Mfs2 运算符，实验证明 PruneX 在工业和大规模电路上显著提高效率，同时保持可比较的优化性能，加速运行时间高达 3.1 倍。