Machine Learning-based techniques are emerging as state-of-the-art methods in chemoinformatics to selectively, effectively, and speedily identify biologically-relevant molecules from large databases. So far, a multitude of such techniques have been proposed, but unfortunately due to their sparse availability, and the dependency on high-end computational literacy, their wider adaptation faces challenges, at least in the context of G-Protein Coupled Receptors (GPCRs)-associated chemosensory research. Here we report Machine-OlF-Action (MOA), a user-friendly, open-source computational framework, that utilizes user-supplied SMILES (simplified molecular-input line-entry system) of the chemicals, along with their activation status, to synthesize classification models. MOA integrates a number of popular chemical databases collectively harboring ∼103 million chemical moieties. MOA also facilitates customized screening of user-supplied chemical datasets. A key feature of MOA is its ability to embed molecules based on the similarity of their local neighborhood, by utilizing a state of the art model interpretability framework LIME. We demonstrate the utility of MOA in identifying previously unreported agonists for human and mouse olfactory receptors OR1A1 and MOR174-9 by leveraging the chemical features of their known agonists and non-agonists. In summary, here we develop an ML-powered software playground for performing supervisory learning tasks involving chemical compounds.

中文翻译：

---

Machine-OlF-Action：用于开发和解释用于化学感应研究的机器学习模型的统一框架

基于机器学习的技术正在成为化学信息学中最先进的方法，可以选择性、有效且快速地从大型数据库中识别生物学相关分子。到目前为止，已经提出了许多这样的技术，但不幸的是，由于它们的可用性稀少，以及对高端计算素养的依赖，它们更广泛的适应性面临挑战，至少在 G 蛋白偶联受体 (GPCR) 的背景下是这样。 -相关的化学感应研究。这里我们报告Machine-OlF-Action(MOA)，一个用户友好的开源计算框架，它利用用户提供的化学品的 SMILES（简化的分子输入行输入系统）及其激活状态来合成分类模型。MOA 整合了许多流行的化学数据库，共同拥有约 1.03 亿个化学部分。MOA 还有助于对用户提供的化学数据集进行自定义筛选。MOA 的一个关键特征是它能够通过利用最先进的模型可解释性框架 LIME，根据分子的局部邻域的相似性嵌入分子。我们通过利用 MOA 已知激动剂和非激动剂的化学特征，证明了 MOA 在识别以前未报道的人类和小鼠嗅觉受体 OR1A1 和 MOR174-9 激动剂方面的效用。总之，