Novel In Silico Approach to Drug Discovery via Computational Intelligence
Novel In Silico Approach to Drug Discovery via Computational Intelligence
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DOI：
10.1021/ci9000647
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发表时间：
2009-04-01
影响因子：
5.6
通讯作者：
Fogel, Gary B.
中科院分区：
文献类型：
--
作者：
Hecht, David;Fogel, Gary B.
介绍了一种计算智能药物发现平台，作为一种创新技术，旨在加速广义蛋白质靶向药物发现的高通量药物筛选。该技术收集了与蛋白质靶点结合的新型小分子化合物以及预测的结合模式和分子相互作用的细节。该方法在二氢叶酸还原酶（DHFR）上进行了测试，用于新型抗疟药物的发现;然而，所开发的方法可以广泛应用于早期药物发现和开发。为此，定义了初始片段文库，并生成了自动化片段组装算法。这些与计算智能筛选工具相结合，用于相对于DHFR抑制的化合物的预筛选。整个方法相对于已知DHFR抑制剂的空间进行测定，并考虑到化学可行性，从而在未来的研究中进行实验验证。
A computational intelligence drug discovery platform is introduced as an innovative technology designed to accelerate high-throughput drug screening for generalized protein-targeted drug discovery. This technology results in collections of novel small molecule compounds that bind to protein targets as well as details on predicted binding modes and molecular interactions. The approach was tested on dihydrofolate reductase (DHFR) for novel antimalarial drug discovery; however, the methods developed can be applied broadly in early stage drug discovery and development. For this purpose, an initial fragment library was defined, and an automated fragment assembly algorithm was generated. These were combined with a computational intelligence screening tool for prescreening of compounds relative to DHFR inhibition. The entire method was assayed relative to spaces of known DHFR inhibitors and with chemical feasibility in mind, leading to experimental validation in future studies.