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

由所有类似药物的分子组成的“化学空间”估计含有10的60次方种化合物，这个数字太大了，以至于无法合成每种化合物中的一种。在这个项目中，我们将应对两个挑战。首先，我们如何具体地发现化学空间中包含哪些分子，或者至少是与治疗相关的部分？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？我们的策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稳健和广泛适用的化学反应，以及过滤器来提高合成成功率。这将大大增加公众可以轻易获得的化学空间的规模。为了导航这个巨大的空间，我们将开发进化算法，帮助我们以有效的方式识别有希望的配体。此外，我们将开发一种基于深度学习的方法，以存储专家对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无法触及的分子数。该项目的两个部分将共同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.