De novo generation of optically active small organic molecules using Monte Carlo tree search combined with recurrent neural network
De novo generation of optically active small organic molecules using Monte Carlo tree search combined with recurrent neural network
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DOI：
10.1002/jcc.26441
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发表时间：
2020-10
影响因子：
3
通讯作者：
M. Tashiro;Y. Imamura;Michio Katouda
中科院分区：
文献类型：
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作者：
M. Tashiro;Y. Imamura;Michio Katouda
光学活性有机小分子是利用Tsuda及其合作者开发的ChemTS蟒库进行计算设计的，该库采用蒙特卡罗树搜索(MCTS)和递归神经网络模型相结合的方法。对每个生成的分子进行几何优化和激发态计算，然后计算激发能和不对称因子来计算MCTS过程中的分数函数。使用这个程序，可以生成现有数据库中没有包含的分子。根据分数函数的选择，可以产生具有高不对称因子或高跃迁偶极强度的分子。在具有100,000次试验的单一轨迹中，在最初的15,000-20,000次试验之后，经常会产生相互相似的高得分分子。这表明，从几个轨迹中采样高分分子，每个轨迹都有适度的试验次数，比从一个轨迹中进行大量试验要好。
Optically active small organic molecules are computationally designed using the ChemTS python library developed by Tsuda and collaborators, which utilizes a combined Monte Carlo tree search (MCTS) and recurrent neural network model. Geometry optimization and excited‐state calculations are performed for each generated molecule, following which the excitation energy and dissymmetry factors are computed to evaluate the score function in the MCTS process. Using this procedure, molecules not contained in existing databases are generated. Molecules having either high dissymmetry factors or high transition dipole strengths can be generated depending on the choice of the score function. In a single trajectory with 100,000 trials, mutually similar high‐scoring molecules are generated frequently after the initial 15,000–20,000 trials. This indicates that it is better to sample high‐scoring molecules from several trajectories having a modest number of trials each than from a single trajectory having a large number of trials.