ZBH - Center for Bioinformatics, Universität Hamburg, Hamburg, Germany.
J Comput Chem. 2021 Jun 5;42(15):1095-1100. doi: 10.1002/jcc.26522.
Numerical optimization is a common technique in various areas of computational chemistry, molecular modeling and drug design. It is a key element of 3D techniques, for example, the optimization of protein-ligand poses and small-molecule conformers. Here, often the BFGS algorithm or variants thereof are used. However, the BFGS algorithm tends to make unreasonable large changes to the optimized system under certain circumstances. This behavior has been known for a long time and different solutions have been suggested. Recently, we have analyzed the optimization behavior of our novel JAMDA scoring function in detail and proposed the limited step length (LSL)-BFGS algorithm as a new solution to the problem of excessively large steps during optimization. The LSL-BFGS algorithm allows to control the step sizes during optimization. Its unique feature is the inclusion of arbitrary domain knowledge into the selection of the step sizes. Here, we introduce the open-source LSLOpt C++ library that implements this LSL-BFGS algorithm and demonstrate its usage.
数值优化是计算化学、分子建模和药物设计等各个领域中常用的技术。它是 3D 技术的关键要素，例如，蛋白质配体构象和小分子构象的优化。在这里，通常使用 BFGS 算法或其变体。然而，在某些情况下，BFGS 算法往往会对优化系统进行不合理的大幅改变。这种行为已经存在很长时间了，并且已经提出了不同的解决方案。最近，我们详细分析了我们新的 JAMDA 评分函数的优化行为，并提出了有限步长（LSL）-BFGS 算法作为优化过程中过大步长问题的新解决方案。LSL-BFGS 算法允许在优化过程中控制步长。它的独特之处在于将任意领域知识纳入到步长的选择中。在这里，我们介绍了实现这个 LSL-BFGS 算法的开源 LSLOpt C++库，并演示了它的用法。
