Current ligand-based machine learning methods in virtual screening rely heavily on molecular fingerprinting for preprocessing, i.e., explicit description of ligands’ structural and physicochemical properties in a vectorized form. Of particular importance to current methods are the extent to which molecular fingerprints describe a particular ligand and what metric sufficiently captures similarity among ligands. In this work, we propose and evaluate methods that do not require explicit feature vectorization through fingerprinting, but, instead, provide implicit descriptors based only on other known assays. Our methods are based upon well known collaborative filtering algorithms used in recommendation systems. Our implicit descriptor method does not require any fingerprint similarity search, which makes the method free of the bias arising from the empirical nature of the fingerprint models. We show that implicit methods significantly outperform traditional machine learning methods, and the main strengths of implicit methods are their resilience to target-ligand sparsity and high potential for spotting promiscuous ligands.

中文翻译：

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通过协同过滤基于隐式描述符配体的虚拟筛选。

虚拟筛选中当前基于配体的机器学习方法在很大程度上依赖于分子指纹识别的预处理，即以矢量化形式对配体的结构和理化性质进行明确描述。对于当前方法特别重要的是分子指纹描述特定配体的程度以及何种度量足以捕获配体之间的相似性。在这项工作中，我们提出并评估了不需要通过指纹进行显式特征矢量化的方法，而是仅基于其他已知测定方法提供隐式描述符。我们的方法基于推荐系统中使用的众所周知的协作过滤算法。我们的隐式描述符方法不需要任何指纹相似性搜索，从而使该方法摆脱了指纹模型的经验性质所产生的偏差。我们表明隐式方法明显优于传统的机器学习方法，并且隐式方法的主要优点是它们对目标配体稀疏性的适应能力强，并且具有发现混杂配体的高潜力。