Brown R D, Hassan M, Waldman M
Molecular Simulations, Inc., 9685 Scranton Road, San Diego, CA 92121, USA.
J Mol Graph Model. 2000 Aug-Oct;18(4-5):427-37, 537. doi: 10.1016/s1093-3263(00)00072-3.
Most computational techniques for the design of combinatorial libraries have concentrated solely on maximizing the diversity of the selected subset or its similarity to a known target. However, such libraries can produce high-throughput screening hits with properties that make them unsuitable to take forward into medicinal chemistry. This article describes software that allows the design of library subsets to simultaneously optimize a library's diversity or similarity to a target, properties (such as drug likeness) of the library members, properties (such as cost) of the reagents required to make them, and efficiency of synthesis in arrays or mixtures. Example are given showing that libraries can be designed to contain drug-like molecules with only a small trade-off in terms of the maximum possible diversity, and that the cost of the library, in terms of the reagents required to make it, can be contained. Other examples show that libraries can be designed to minimize the deconvolution problem or to maximize the number of molecules predicted to be active while also being designed for efficiency of synthesis.
大多数用于组合文库设计的计算技术都仅仅专注于最大化所选子集的多样性或其与已知靶点的相似性。然而，这样的文库可能会产生高通量筛选命中物，但其性质使其不适于推进到药物化学领域。本文介绍了一种软件，该软件可用于设计文库子集，以同时优化文库与靶点的多样性或相似性、文库成员的性质（如类药性质）、合成它们所需试剂的性质（如成本）以及阵列或混合物中的合成效率。给出的示例表明，可以设计文库来包含类药分子，而在最大可能的多样性方面仅有很小的权衡，并且就制备文库所需的试剂而言，文库的成本是可控的。其他示例表明，可以设计文库来最小化去卷积问题，或者在设计合成效率的同时最大化预测有活性的分子数量。
