论文标题
简单的机器学习预测有机分子几何形状的基本电论描述符
A basic electro-topological descriptor for the prediction of organic molecule geometries by simple machine learning
论文作者
论文摘要
本文提出了一种机器学习（ML）方法，以从其化学组成中预测稳定的分子几何形状。该方法可用于产生分子构象，该分子构象可以用作通过大分子的量子机械计算在昂贵结构优化过程中节省时间的初始几何形状。通过预测从先前优化的小分子的数据库训练后，可以通过预测分子中每个原子周围的局部排列来找到构象。它通过将数据库中的每个分子分为不同类型的最小构建块来起作用。然后，对算法进行训练，以使用电论指纹作为描述符来预测每种类型的构建基块的键长和角度。然后，通过连接预测的块来生成构象。我们的模型能够从化学公式和连通性的基本知识中为优化的分子几何形状提供有希望的结果。 RMSD低于$ 0.05 $Å的测试块内的原子间距离的方法趋势
This paper proposes a machine learning (ML) method to predict stable molecular geometries from their chemical composition. The method is useful for generating molecular conformations which may serve as initial geometries for saving time during expensive structure optimizations by quantum mechanical calculations of large molecules. Conformations are found by predicting the local arrangement around each atom in the molecule after trained from a database of previously optimized small molecules. It works by dividing each molecule in the database into minimal building blocks of different type. The algorithm is then trained to predict bond lengths and angles for each type of building block using an electro-topological fingerprint as descriptor. A conformation is then generated by joining the predicted blocks. Our model is able to give promising results for optimized molecular geometries from the basic knowledge of the chemical formula and connectivity. The method trends to reproduce interatomic distances within test blocks with RMSD under $0.05$ Å
