Ajay A, Walters W P, Murcko M A
Vertex Pharmaceuticals Inc., 130 Waverly Street, Cambridge, Massachusetts 02139, USA.
J Med Chem. 1998 Aug 27;41(18):3314-24. doi: 10.1021/jm970666c.
We have used a Bayesian neural network to distinguish between drugs and nondrugs. For this purpose, the CMC acts as a surrogate for drug-like molecules while the ACD is a surrogate for nondrug-like molecules. This task is performed by using two different set of 1D and 2D parameters. The 1D parameters contain information about the entire molecule like the molecular weight and the the 2D parameters contain information about specific functional groups within the molecule. Our best results predict correctly on over 90% of the compounds in the CMC while classifying about 10% of the molecules in the ACD as drug-like. Excellent generalization ability is shown by the models in that roughly 80% of the molecules in the MDDR are classified as drug-like. We propose to use the models to design combinatorial libraries. In a computer experiment on generating a drug-like library of size 100 from a set of 10 000 molecules we obtain at least a 3 or 4 order of magnitude improvement over random methods. The neighborhoods defined by our models are not similar to the ones generated by standard Tanimoto similarity calculations. Therefore, new and different information is being generated by our models, and so it can supplement standard diversity approaches to library design.
我们使用了贝叶斯神经网络来区分药物和非药物。为此，CMC作为类药物分子的替代物，而ACD作为非类药物分子的替代物。这项任务通过使用两组不同的一维和二维参数来完成。一维参数包含有关整个分子的信息，如分子量，二维参数包含有关分子内特定官能团的信息。我们的最佳结果在CMC中超过90%的化合物上预测正确，同时将ACD中约10%的分子分类为类药物。模型显示出出色的泛化能力，MDDR中约80%的分子被分类为类药物。我们建议使用这些模型来设计组合文库。在一个从10000个分子集合生成大小为100的类药物文库的计算机实验中，我们相对于随机方法至少获得了3到4个数量级的改进。我们的模型定义的邻域与标准Tanimoto相似性计算生成的邻域不同。因此，我们的模型正在生成新的、不同的信息，从而可以补充文库设计的标准多样性方法。
