In silico estimation of DMSO solubility of organic compounds for bioscreening
In silico estimation of DMSO solubility of organic compounds for bioscreening
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DOI：
10.1177/1087057103260006
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发表时间：
2004-02-01
影响因子：
--
通讯作者：
Ivashchenko, AA
中科院分区：
文献类型：
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作者：
Balakin, KV;Ivanenkov, YA;Ivashchenko, AA
有机化合物在DMSO中的溶解度是处理大量化合物收集或进行生物筛选的商业和学术组织的重要问题。特别是，溶解度数据对于优化储存条件和选择与检测方案兼容的化合物进行生物筛选至关重要。溶解度在很大程度上由溶剂化能和晶体破坏能决定，这些分子现象应该在结构-溶解度相关性研究中进行评估。总结了我们对DMSO在有机物质中溶解度的物理化学决定因素的长期实验观察和理论研究。他们编制了一个关于化合物溶解度的专有数据的综合参考数据库(55,277种化合物具有良好的DMSO溶解性，10,223种化合物具有较差的DMSO溶解性)，计算特定的分子描述符(拓扑、电磁、电荷和亲油性参数)，并应用先进的机器学习方法来训练神经网络来解决溶解性问题。使用了有监督(前馈、反向传播神经网络)和无监督(Kohonen神经网络)两种学习方法。通过在独立的测试选择中成功地预测化合物的DMSO溶解度，验证了所得到的神经网络模型。
Solubility of organic compounds in DMSO is an important issue for commercial and academic organizations handling large compound collections or performing biological screening. In particular, solubility data are critical for the optimization of storage conditions and for the selection of compounds for bioscreening compatible with the assay protocol. Solubility is largely determined by the solvation energy and the crystal disruption energy, and these molecular phenomena should be assessed in structure-solubility correlation studies. The authors summarize our long-term experimental observations and theoretical studies of physicochemical determinants of DMSO solubility of organic substances. They compiled a comprehensive reference database of proprietary data on compound solubility (55,277 compounds with good DMSO solubility and 10,223 compounds with poor DMSO solubility), calculated specific molecular descriptors (topological, electromagnetic, charge, and lipophilicity parameters), and applied an advanced machine-learning approach for training neural networks to address the solubility. Both supervised (feed-forward, back-propagated neural networks) and unsupervised (Kohonen neural networks) learning methods were used. The resulting neural network models were validated by successfully predicting DMSO solubility of compounds in independent test selections.