Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
IEEE/ACM Trans Comput Biol Bioinform. 2011 Jul-Aug;8(4):987-1003. doi: 10.1109/TCBB.2010.100.
Traditionally, Computer-Aided Molecular Design (CAMD) uses heuristic search and mathematical programming to tackle the molecular design problem. But these techniques do not handle large and nonlinear search space very well. To overcome these drawbacks, graph-based evolutionary algorithms (EAs) have been proposed to evolve molecular design by mimicking chemical reactions on the exchange of chemical bonds and components between molecules. For these EAs to perform their tasks, known molecular components, which can serve as building blocks for the molecules to be designed, and known chemical rules, which govern chemical combination between different components, have to be introduced before the evolutionary process can take place. To automate molecular design without these constraints, this paper proposes an EA called Evolutionary Algorithm for Molecular Design (EvoMD). EvoMD encodes molecular designs in graphs. It uses a novel crossover operator which does not require known chemistry rules known in advanced and it uses a set of novel mutation operators. EvoMD uses atomics-based and fragment-based approaches to handle different size of molecule, and the value of the fitness function it uses is made to depend on the property descriptors of the design encoded in a molecular graph. It has been tested with different data sets and has been shown to be very promising.
传统上，计算机辅助分子设计（CAMD）使用启发式搜索和数学规划来解决分子设计问题。但是，这些技术并不能很好地处理大型非线性搜索空间。为了克服这些缺点，已经提出了基于图的进化算法（EA），通过模拟分子之间化学键和成分的交换来进化分子设计。为了使这些 EA 执行其任务，在进化过程发生之前，必须引入已知的分子成分，这些成分可以作为要设计的分子的构建块，以及已知的化学规则，这些规则控制不同成分之间的化学组合。为了在没有这些约束的情况下自动化分子设计，本文提出了一种称为分子设计进化算法（EvoMD）的 EA。EvoMD 将分子设计编码为图。它使用一种新颖的交叉算子，不需要事先知道化学规则，并且使用一组新颖的突变算子。EvoMD 使用基于原子和基于片段的方法来处理不同大小的分子，并且它使用的适应度函数的值取决于编码在分子图中的设计的属性描述符。它已经在不同的数据集上进行了测试，并且表现非常有前途。
