论文标题
学习具有深层生成模型的3D分子结构的连续表示
Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models
论文作者
论文摘要
药物发现中的机器学习一直集中在使用歧视模型的分子文库的虚拟筛选上。生成模型是一种完全不同的方法，可以学会在连续的潜在空间中代表和优化分子。这些方法越来越成功地作为微笑字符串和分子图产生两个维分子。在这项工作中，我们描述了使用原子密度网格和一种新型拟合算法将连续网格转换为离散分子结构的新型拟合算法的三维分子结构的深层生成模型。我们的模型共同表示可以通过插值探索的潜在空间中的药物样分子及其构象。我们还能够根据给定的输入化合物对各种分子进行采样，并增加产生有效的药物样分子的概率。
Machine learning in drug discovery has been focused on virtual screening of molecular libraries using discriminative models. Generative models are an entirely different approach that learn to represent and optimize molecules in a continuous latent space. These methods have been increasingly successful at generating two dimensional molecules as SMILES strings and molecular graphs. In this work, we describe deep generative models of three dimensional molecular structures using atomic density grids and a novel fitting algorithm for converting continuous grids to discrete molecular structures. Our models jointly represent drug-like molecules and their conformations in a latent space that can be explored through interpolation. We are also able to sample diverse sets of molecules based on a given input compound and increase the probability of creating valid, drug-like molecules.
