Importance sampling of chemical compound space: Thermodynamic properties from high-throughput coarse-grained simulations

计算机辅助合理设计小分子以优化生物分子过程的功能是一项具有挑战性的奋进，有许多希望。在这种情况下，计算机模拟的使用仍然受到计算资源的影响，限制了单个项目中化合物的典型数量为10-100。基本概念是探索化学空间，以及分子结构如何影响所需的功能。一个更系统的方法来解决这个问题必然需要更多的分子。该项目旨在开发高通量生物分子模拟领域，以评估大量化合物在特定环境下的自由能。通过两种互补技术的结合，这一目标成为可能。首先，粗粒度建模简化了每种化合物的计算投入，以及化学空间的大小。粗粒化的作用，以减少相空间和化学空间的手段之间的平行突出，提出了一个有前途的路线，更系统地探索各种分子结构。此外，重要性采样方案的引入有助于小分子的定向进化以优化给定的功能。该新方法应用于两种生物分子场景：膜中的小分子插入（即，麻醉剂）和蛋白质-配体结合。

The computer-assisted rational design of small molecules to optimize the function of a biomolecular process is a challenging endeavor that holds many promises. In this context, the use of computer simulations is still impinged by computational resources, limiting the typical number of compounds within a single project to 10-100. The underlying concept is the exploration of chemical space, and how molecular structures impact the desired function. A more systematic approach to the problem necessarily requires much larger numbers of molecules. This project aims at developing the realm of high-throughput biomolecular simulations to evaluate free energies of a large number of compounds subject to a certain environment. The goal is made possible by the combination of two complementary techniques. First, coarse-grained modeling alleviates the computational investment of each compound, as well as the size of chemical space. The parallel between coarse-graining's role as the means to reduce both phase space and chemical space is highlighted, suggesting a promising route to more systematically explore the variety of molecular structures. Further, the introduction of an importance-sampling scheme helps the directed evolution of small molecules to optimize a given function. The novel methodology is applied to two biomolecular scenarios: small-molecule insertion in the membrane (i.e., anaesthetics) and protein-ligand binding.