Walters W Patrick, Murcko Mark A
Vertex Pharmaceuticals, 130 Waverly Street, 02139, Cambridge, MA 02139, USA.
Adv Drug Deliv Rev. 2002 Mar 31;54(3):255-71. doi: 10.1016/s0169-409x(02)00003-0.
Recent developments in combinatorial chemistry and high-throughput screening have dramatically increased the scale on which drug discovery programs are carried out. Along with these advances has come a need for automated methods of determining which compounds from a library should be synthesized and screened. These methods range from simple counting schemes to sophisticated machine learning techniques such as neural networks. While many of these methods have performed well in validation studies, the field is still in its formative stage. This paper reviews a number of computational techniques for identifying drug-like molecules and examines challenges facing the field.
组合化学和高通量筛选领域的最新进展极大地提高了药物研发项目的开展规模。随着这些进展而来的是对确定文库中哪些化合物应进行合成和筛选的自动化方法的需求。这些方法从简单的计数方案到复杂的机器学习技术（如神经网络）不等。虽然其中许多方法在验证研究中表现良好，但该领域仍处于形成阶段。本文回顾了一些用于识别类药物分子的计算技术，并探讨了该领域面临的挑战。
