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The Development of a Weighted Index to Optimise Compound Libraries for High Throughput Screening

机译：加权指数的发展，优化高吞吐量筛选复合库

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Abstract 1880 known drugs were collected and analysed for their mainstream molecular descriptors: MW, log P, HA, HD, RB and PSA. The statistical distributions were fitted to Gaussian functions for each of the descriptors. This gave a mathematical tool to calculate a weighted score, or an Index, for each descriptor. Known Drug Indexes (KDIs) were derived either by summation or multiplication of the Indexes, giving one number for each molecule calculated. The KDI summation and multiplication methods give a theoretical maxima of 6 and 1 respectively. According to both methods, methysergide (5.89/0.90), amsacrine (5.89/0.89) and fluorometholone (5.88/0.88) have the scores of the most well‐balanced pharmaceuticals. The KDIs are advantageous tools in identifying the most well‐balanced screening compounds based on the properties of known drugs; the screening collection can be optimised to only include quality compounds, which in turn produce tractable hit and lead compounds from the screening campaign.
机译：摘要收集了1880年已知的药物并分析其主流分子描述符：MW，Log P，HA，HD，RB和PSA。统计分布适用于每个描述符的高斯函数。这给出了一个数学工具来计算每个描述符的加权分数或索引。通过对指标的总和或倍增来衍生已知的药物指数（KDIS），给出每个分子的一个数字。 KDI求和和乘法方法分别提供了6和1的理论最大值。根据两种方法，甲藻土（5.89 / 0.90），氨基碱（5.89 / 0.89）和氟甲甲酮（5.88 / 0.88）具有最平衡的药物评分。 KDIS是有利的工具，用于鉴定基于已知药物的性质的最良好平衡的筛选化合物;筛选收集可以优化，仅包括质量化合物，其又产生从筛选活动中产生易击球和铅化合物。

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