Machine-Learning-guided chemical space exploration: automatic creation and navigation of ultra-large open-source molecular libraries

由所有类药物分子形成的“化学空间”估计包含10的60次方的化合物，这个数字太大了，以至于无法合成每一种化合物。在这个项目中，我们将解决两个挑战。首先，我们如何具体地发现化学空间或至少是其中与治疗相关的部分包含哪些分子？其次，我们如何用基于蛋白质结构的计算机方法搜索如此大的空间？我们的战略是基于我们的虚拟合成化合物数据库SCUBIDOO，我们将开发算法来识别新的强大和广泛适用的化学反应以及过滤器，以提高合成成功率。这将大大增加公众可利用的、容易进入的化学空间的规模。为了导航这个巨大的空间，我们将开发进化算法，这将有助于我们以有效的方式识别有前途的配体。此外，我们将开发一种基于深度学习的方法，以存储专家关于每个潜在配体在蛋白质结合口袋中的适合度的意见。通过这种方式，我们将能够保存知识，并将其应用于单个人类无法达到的分子数量。该项目的两个分支将为快速和全面的化学空间探索打开大门。

The “chemical space” formed by all drug-like molecules contains an estimated 10 to the power of 60 compounds, a number too large to ever synthesise one of each. In this project we will tackle two challenges. First, how can we discover concretely which molecules are contained in chemical space or at least a therapeutically relevant portion thereof? Second, how can we search such large spaces with protein-structure-based in silico methods? Our strategy is based on our database of virtually synthesised compounds, SCUBIDOO, and we will develop algorithms to identify novel robust and broadly applicable chemical reactions as well as filters to increase synthesis success rates. This will substantially increase the size of publicly available easily accessible chemical space. For navigating this huge space, we will develop evolutionary algorithms that will help us identify promising ligands in an efficient way. Moreover, we will develop a deep-learning based method in order to store the opinion of an expert about the fit of each potential ligand in a protein binding pocket. In this way, we will be able to preserve knowledge and also apply it to molecule numbers that are out of reach for a single human being. Both arms of the project together will open the door for fast and comprehensive chemical space exploration.