Data-driven generation of molecules with desired properties, also known as
inverse molecular design (IMD), has attracted significant attention in recent
years. Despite the significant progress in the accuracy and diversity of
solutions, existing IMD methods lag behind in terms of trustworthiness. The
root issue is that the design process of these methods is increasingly more
implicit and indirect, and this process is also isolated from the native
forward process (NFP), the ground-truth function that models the molecular
dynamics. Following this insight, we propose TrustMol, an IMD method built to
be trustworthy. For this purpose, TrustMol relies on a set of technical
novelties including a new variational autoencoder network. Moreover, we propose
a latent-property pairs acquisition method to effectively navigate the
complexities of molecular latent optimization, a process that seems intuitive
yet challenging due to the high-frequency and discontinuous nature of molecule
space. TrustMol also integrates uncertainty-awareness into molecular latent
optimization. These lead to improvements in both explainability and reliability
of the IMD process. We validate the trustworthiness of TrustMol through a wide
range of experiments.

数据驱动的分子设计，逆向分子设计方法，可信性，变分自动编码器网络，不确定性意识

TrustMol: 通过与分子动力学对齐的可信逆向分子设计

TrustMol: Trustworthy Inverse Molecular Design via Alignment with  Molecular Dynamics

To facilitate a wide-spread acceptance of AI systems guiding decision making
in real-world applications, trustworthiness of deployed models is key. That is,
it is crucial for predictive models to be uncertainty-aware and yield
well-calibrated (and thus trustworthy) predictions for both in-domain samples
as well as under domain shift. Recent efforts to account for predictive
uncertainty include post-processing steps for trained neural networks, Bayesian
neural networks as well as alternative non-Bayesian approaches such as ensemble
approaches and evidential deep learning. Here, we propose an efficient yet
general modelling approach for obtaining well-calibrated, trustworthy
probabilities for samples obtained after a domain shift. We introduce a new
training strategy combining an entropy-encouraging loss term with an
adversarial calibration loss term and demonstrate that this results in
well-calibrated and technically trustworthy predictions for a wide range of
domain drifts. We comprehensively evaluate previously proposed approaches on
different data modalities, a large range of data sets including sequence data,
network architectures and perturbation strategies. We observe that our
modelling approach substantially outperforms existing state-of-the-art
approaches, yielding well-calibrated predictions under domain drift.

为了让实际应用的人工智能系统更加被广泛认知，模型的可信度至关重要。文章提出了一种新的训练策略，结合熵鼓励损失项和对抗校准损失项，以提高样本在领域世界偏移下的校准性和适应性。文章在不同的数据模式、数据集和网络架构上进行了全面的评估，结果显示该方法明显高于现有的最先进方法，应用于领域漂移下预测方面具有技术上的可信度。

用快速对抗校准实现深度神经网络中值得信赖的预测

Towards Trustworthy Predictions from Deep Neural Networks with Fast  Adversarial Calibration

This paper argues the need for research to realize uncertainty-aware
artificial intelligence and machine learning (AI\&ML) systems for decision
support by describing a number of motivating scenarios. Furthermore, the paper
defines uncertainty-awareness and lays out the challenges along with surveying
some promising research directions. A theoretical demonstration illustrates how
two emerging uncertainty-aware ML and AI technologies could be integrated and
be of value for a route planning operation.

本文阐述了实现基于不确定性意识的人工智能和机器学习系统在决策支持中的必要性及其面临的挑战，并介绍了当前令人看好的研究方向。通过理论模拟，展示了两种新兴的不确定性意识机器学习和人工智能技术的集成对路径规划操作的价值。