Consensus queries in ligand-based virtual screening experiments.
Consensus queries in ligand-based virtual screening experiments.
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DOI：
10.1186/s13321-017-0248-5
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发表时间：
2017-11-28
影响因子：
8.6
通讯作者：
Meiler J
中科院分区：
文献类型：
--
作者：
Berenger F;Vu O;Meiler J
在基于配基的虚拟筛选实验中，一个已知的活性配基被用于相似性搜索，以寻找相同蛋白质靶标的假定活性化合物。当有几个已知的活性分子时，使用所有这些分子进行筛选比使用单一配体进行筛选更有效。可以通过在合并所获得的相似性分数之前对不同配体进行连续筛选，或者通过组合那些配体的分子描述符(即，化学指纹)来创建共识查询。我们报告了几种共识方法的辨别能力和速度，只在两个由实验验证的分子组成的数据集上。这两个数据集共包含19个蛋白质靶点，3776个已知活性分子和~32万×106个非活性分子。研究了三种化学指纹：MACCS 166位，ECFP 4 2048位和MOLPRINT2D的展开版本。对四种不同的共识政策和五种共识规模进行了基准比较。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获得的最高分数来对候选分子进行排名。当使用的活性物质数量很少时，可以通过一致的指纹来接近相同的筛选性能。然而，如果对化学空间的计算探索受到速度(即吞吐量)的限制，那么共识指纹就可以超过这个共识分数。
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.
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DOI：
10.1021/ci034207y
发表时间：
2004-01-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Bender, A;Mussa, HY;Reiling, S
通讯作者：
Reiling, S
影响因子：
7.3
作者：
Godden, JW;Stahura, FL;Bajorath, J
通讯作者：
Bajorath, J
影响因子：
7.3
作者：
Mysinger, Michael M.;Carchia, Michael;Irwin, John. J.;Shoichet, Brian K.
通讯作者：
Shoichet, Brian K.
影响因子：
8.6
作者：
Kothiwale S;Mendenhall JL;Meiler J
通讯作者：
Meiler J
影响因子：
8.6
作者：
O'Boyle NM;Banck M;James CA;Morley C;Vandermeersch T;Hutchison GR
通讯作者：
Hutchison GR