In ligand-based virtual screening experiments, a known active ligand is used in similarity searches to find putative active compounds for the same protein target. When there are several known active molecules, screening using all of them is more powerful than screening using a single ligand. A consensus query can be created by either screening serially with different ligands before merging the obtained similarity scores, or by combining the molecular descriptors (i.e. chemical fingerprints) of those ligands. We report on the discriminative power and speed of several consensus methods, on two datasets only made of experimentally verified molecules. The two datasets contain a total of 19 protein targets, 3776 known active and ~ 2 × 106 inactive molecules. Three chemical fingerprints are investigated: MACCS 166 bits, ECFP4 2048 bits and an unfolded version of MOLPRINT2D. Four different consensus policies and five consensus sizes were benchmarked. The best consensus method is to rank candidate molecules using the maximum score obtained by each candidate molecule versus all known actives. When the number of actives used is small, the same screening performance can be approached by a consensus fingerprint. However, if the computational exploration of the chemical space is limited by speed (i.e. throughput), a consensus fingerprint allows to outperform this consensus of scores.

中文翻译：

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基于配体的虚拟筛选实验中的共识性查询。

在基于配体的虚拟筛选实验中，在相似性搜索中使用了已知的活性配体，以找到用于同一蛋白质靶标的推定活性化合物。当存在几个已知的活性分子时，使用所有分子进行的筛选比使用单个配体进行的筛选更有效。可以通过在合并获得的相似性分数之前用不同的配体进行连续筛选，或通过组合这些配体的分子描述符（即化学指纹）来创建共识查询。我们在仅由实验验证的分子组成的两个数据集上报告了几种共识方法的判别力和速度。这两个数据集总共包含19个蛋白质靶标，3776个已知活性分子和〜2×106个非活性分子。研究了三种化学指纹：MACCS 166位，ECFP4 2048位和MOLPRINT2D的展开版本。对四种不同的共识政策和五个共识规模进行了基准测试。最好的共识方法是使用每个候选分子相对于所有已知活性物质的最大得分对候选分子进行排名。当使用的活性物质数量较少时，共识指纹可以达到相同的筛选性能。但是，如果化学空间的计算探索受到速度（即吞吐量）的限制，则共识指纹可以胜过分数的共识。共识指纹可以达到相同的筛选性能。但是，如果化学空间的计算探索受到速度（即吞吐量）的限制，则共识指纹可以胜过分数的共识。共识指纹可以达到相同的筛选性能。但是，如果化学空间的计算探索受到速度（即吞吐量）的限制，则共识指纹可以胜过分数的共识。