化学空间探索:遗传算法如何在大海捞针中找到针

E. Hénault, M. Rasmussen, Jan H. Jensen
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引用次数: 23

摘要

我们解释了为什么搜索算法可以通过只考虑极小的子集(通常为103−6个分子),在巨大的化学空间中找到具有特定性质的分子(约1060个分子)。通过一个非常简单的例子,我们表明搜索算法可以遵循到目标的潜在路径的数量同样巨大。因此,随机找到在这些路径之一上的分子的概率相当高,并且从这里搜索算法可以沿着路径到达目标分子。路径被定义为与目标分子具有一些非零可量化相似性(分数)并且与目标分子越来越相似的一系列分子。从化学空间中的任何点到目标对应的最小路径长度约为100步,其中一步是原子或键类型的变化。因此,一个完美的搜索算法应该能够通过筛选100s数量级的分子来定位化学空间中的特定分子,前提是分数逐渐变化。我们表明,遗传搜索算法的实际数量在100到数百万之间,这取决于目标性质及其对分子变化的依赖性、分子表示和搜索问题的解决方案数量。
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Chemical space exploration: how genetic algorithms find the needle in the haystack
We explain why search algorithms can find molecules with particular properties in an enormous chemical space (ca 1060 molecules) by considering only a tiny subset (typically 103−6 molecules). Using a very simple example, we show that the number of potential paths that the search algorithms can follow to the target is equally vast. Thus, the probability of randomly finding a molecule that is on one of these paths is quite high and from here a search algorithm can follow the path to the target molecule. A path is defined as a series of molecules that have some non-zero quantifiable similarity (score) with the target molecule and that are increasingly similar to the target molecule. The minimum path length from any point in chemical space to the target corresponds is on the order of 100 steps, where a step is the change of and atom- or bond-type. Thus, a perfect search algorithm should be able to locate a particular molecule in chemical space by screening on the order of 100s of molecules, provided the score changes incrementally. We show that the actual number for a genetic search algorithm is between 100 and several millions, and depending on the target property and its dependence on molecular changes, the molecular representation, and the number of solutions to the search problem.
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