Computational Biology Unit (CBU) and Department of Chemistry, University of Bergen, N-5020 Bergen, Norway.
Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, 20146 Hamburg, Germany.
Int J Mol Sci. 2021 Jul 21;22(15):7773. doi: 10.3390/ijms22157773.
Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the "fitness" of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle ("BonMOLière").
实验筛选针对大分子靶标的大量化合物是识别新生物活性的关键策略。然而，大规模筛选需要大量的实验资源，并且耗时且具有挑战性。因此，具有高几率在任意感兴趣的蛋白质上产生真实命中的中小规模化合物库对于与早期药物发现相关的领域（特别是生化和细胞研究）将具有巨大价值。在这里，我们提出了一种计算方法，该方法结合了药物相似性、预测的生物活性、生物空间覆盖度和靶标新颖性，以生成具有最大几率产生广泛蛋白质的真实命中的优化化合物库。该计算方法使用一组既定规则评估药物相似性，使用经过验证的基于相似性的方法预测生物活性，并优化小化合物集的组成，以实现最大的靶标覆盖率和新颖性。我们发现，与随机选择化合物库相比，我们的方法生成了明显改进的化合物集。作为化合物库的“适应性”进行量化，计算出的改进幅度从+60%（对于 15000 个化合物的库）到+184%（对于 1000 个化合物的库）不等。在这项工作中准备的最佳优化化合物库可作为数据集捆绑包（“BonMOLière”）下载。
