Determining whether a molecule can be synthesized is crucial for many aspects
of chemistry and drug discovery, allowing prioritization of experimental work
and ranking molecules in de novo design tasks. Existing scoring approaches to
assess synthetic feasibility struggle to extrapolate to out-of-distribution
chemical spaces or fail to discriminate based on minor differences such as
chirality that might be obvious to trained chemists. This work aims to address
these limitations by introducing the Focused Synthesizability score (FSscore),
which learns to rank structures based on binary preferences using a graph
attention network. First, a baseline trained on an extensive set of
reactant-product pairs is established that subsequently is fine-tuned with
expert human feedback on a chemical space of interest. Fine-tuning on focused
datasets improves performance on these chemical scopes over the pre-trained
model exhibiting moderate performance and generalizability. This enables
distinguishing hard- from easy-to-synthesize molecules and improving the
synthetic accessibility of generative model outputs. On very complex scopes
with limited labels achieving satisfactory gains remains challenging. The
FSscore showcases how human expert feedback can be utilized to optimize the
assessment of synthetic feasibility for a variety of applications.

该研究通过引入基于二元偏好的图注意力网络，建立了一个以合成可行性为评估标准的评分方法 FSscore，并通过专家人工反馈对感兴趣的化学空间进行了微调。FSscore 展示了如何利用人工专家反馈来优化合成可行性评估，适用于多种应用领域。

FSscore：一种基于机器学习的合成可行性评分，利用人工专家的专长

FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging  Human Expertise

We address how to robustly interpret natural language refinements (or
critiques) in recommender systems. In particular, in human-human recommendation
settings people frequently use soft attributes to express preferences about
items, including concepts like the originality of a movie plot, the noisiness
of a venue, or the complexity of a recipe. While binary tagging is extensively
studied in the context of recommender systems, soft attributes often involve
subjective and contextual aspects, which cannot be captured reliably in this
way, nor be represented as objective binary truth in a knowledge base. This
also adds important considerations when measuring soft attribute ranking. We
propose a more natural representation as personalized relative statements,
rather than as absolute item properties. We present novel data collection
techniques and evaluation approaches, and a new public dataset. We also propose
a set of scoring approaches, from unsupervised to weakly supervised to fully
supervised, as a step towards interpreting and acting upon soft attribute based
critiques.

讨论了如何稳定地解释推荐系统中的自然语言细化 (或批评)，提出了一种基于个性化相对语句的更自然的表述方式，并提供一组评分方法，从无监督到弱监督到全监督，在解释和处理软属性的基础上提供了方案。