Supercomputing-Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120, Almería, Spain.
Structural Bioinformatics and High Performance Computing Research Group (BIO-HPC), HiTech Innovation Hub, Universidad Católica San Antonio De Murcia (UCAM), Campus de los Jerónimos, 30107, Murcia, Spain.
Sci Rep. 2022 Jul 27;12(1):12769. doi: 10.1038/s41598-022-16913-w.
Virtual screening methods focus on searching molecules with similar properties to a given compound. Molecule databases are made up of large numbers of compounds and are constantly increasing. Therefore, fast and efficient methodologies and tools have to be designed to explore them quickly. In this context, ligand-based virtual screening methods are a well-known and helpful tool. These methods focus on searching for the most similar molecules in a database to a reference one. In this work, we propose a new tool called 2L-GO-Pharm, which requires less computational effort than OptiPharm, an efficient and robust piece of software recently proposed in the literature. The new-implemented tool maintains or improves the quality of the solutions found by OptiPharm, and achieves it by considerably reducing the number of evaluations needed. Some of the strengths that help 2L-GO-Pharm enhance searchability are the reduction of the search space dimension and the introduction of some circular limits for the angular variables. Furthermore, to ensure a trade-off between exploration and exploitation of the search space, it implements a two-layer strategy and a guided search procedure combined with a convergence test on the rotation axis. The performance of 2L-GO-Pharm has been tested by considering two different descriptors, i.e. shape similarity and electrostatic potential. The results show that it saves up to 87.5 million evaluations per query molecule.
虚拟筛选方法侧重于搜索具有与给定化合物相似性质的分子。分子数据库由大量化合物组成，并且还在不断增加。因此，必须设计快速有效的方法和工具来快速探索它们。在这种情况下，基于配体的虚拟筛选方法是一种众所周知且很有帮助的工具。这些方法侧重于在数据库中搜索与参考化合物最相似的分子。在这项工作中，我们提出了一种名为 2L-GO-Pharm 的新工具，与最近文献中提出的高效稳健软件 OptiPharm 相比，它需要的计算工作量更少。新实现的工具保持或提高了 OptiPharm 找到的解决方案的质量，通过大大减少所需的评估数量来实现这一点。有助于 2L-GO-Pharm 提高可搜索性的一些优势包括降低搜索空间维度和为角度变量引入一些圆形限制。此外，为了在搜索空间的探索和利用之间取得平衡，它实现了两层策略和引导搜索过程，以及对旋转轴的收敛测试。通过考虑两种不同的描述符，即形状相似性和静电势，对 2L-GO-Pharm 的性能进行了测试。结果表明，它为每个查询分子节省了多达 8750 万次评估。
