Division of Chemistry and Structural Biology, Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld 4072, Australia.
Curr Top Med Chem. 2012;12(14):1500-13. doi: 10.2174/156802612802652466.
Most drug discovery programs today originate by selection of 'hit' molecules resulting from assays against large compound screening libraries. The chemical space in which these hits reside has implications for its biological activity in vivo and likelihood of progression to a drug candidate. We have created a database of commercially available screening compounds and natural products in order to analyse the drug- and lead-likeness of commercial screening compounds and compare them with i) orally administered drugs, ii) non-orally administered drugs, and iii) compounds with significant biological activity but unspecified or not yet determined route of administration from the public databases DrugBank and ChEMBL. The data set contained 15.5 million entries from 102 vendors, which resulted in just over 8 million unique chemical structures. We review these data for current drug/lead-likeness, then utilise substructure-based filters for promiscuity and unwanted groups, and finally compare chemical properties for structures within the different sub-sets. While the majority of the commercial compounds satisfy various drug-likeness rules, they show a larger molecular weight and higher hydrophobicity compared to orally available drugs, with generally higher aromaticity and lower solubility. This 'right shift' of chemical properties has also been found in the majority of the compounds with significant biological activity in ChEMBL, reflecting a common trend in current drug discovery, towards larger, more hydrophobic compounds and fewer drug-like compounds. In particular, successful drugs were found to possess much lower median logD values than those found for compound collections. In addition, commercial compounds show a quite narrow distribution in molecular weight, with a median absolute deviation of only 78 Da around a median of 387 Da. For high-throughput screening a highly stringent combination of several lead-likeness and substructure filters against unwanted groups could be applied, resulting in 2 million lead-like structures. For fragment based screening approaches the rule of three (Ro3) would select around 400,000 structures.
当今大多数药物发现计划都是通过筛选针对大型化合物筛选文库的“命中”分子来启动的。这些命中分子所在的化学空间与其在体内的生物活性以及成为候选药物的可能性有关。我们创建了一个商业可用筛选化合物和天然产物数据库，以便分析商业筛选化合物的药物和先导化合物特性，并将其与以下各项进行比较：i）口服药物，ii）非口服药物，以及 iii）具有显著生物活性但给药途径未指定或尚未确定的化合物，这些化合物来自公共数据库 DrugBank 和 ChEMBL。该数据集包含来自 102 家供应商的 1550 万个条目，产生了超过 800 万个独特的化学结构。我们审查了这些数据的当前药物/先导化合物特性，然后利用基于子结构的筛选器来筛选混杂性和不需要的基团，最后比较不同子集内结构的化学性质。虽然大多数商业化合物符合各种药物相似性规则，但与可口服药物相比，它们的分子量更大，疏水性更高，通常具有更高的芳香性和更低的溶解度。这种化学性质的“右移”也在 ChEMBL 中具有显著生物活性的大多数化合物中发现，反映了当前药物发现的一个共同趋势，即更大、更疏水的化合物和更少的类药化合物。特别是，发现成功的药物的中位 logD 值远低于化合物集合的中位 logD 值。此外，商业化合物的分子量分布相当狭窄，中位数为 387Da，中位数绝对偏差仅为 78Da。对于高通量筛选，可以针对不需要的基团应用几个先导化合物相似性和子结构筛选器的高度严格组合，从而得到 200 万个类似先导化合物的结构。对于基于片段的筛选方法，Ro3 规则将选择大约 40 万个结构。
