应用改进型遗传算法进行药物分子对接设计

文章建立了一种约束优化的演化模型,并构造出求解此模型的多种群空间收缩遗传算法,将信息熵概念引入进化过程,控制各种群寻优搜索时解空间的收缩。该算法用种群的多样性避免遗传进化的早熟现象,并以空间收缩尺度作为停机判椐,有效地控制了算法的收敛。利用基于小种群的多种群进化策略,在保证种群多样性的前提下,极大程度地减少了计算量,提高了计算效率。数值算例表明,熵的介入增强了随机搜索类进化算法的寻优目的性,使收敛过程平稳且迅速。算例表明此算法能有效的应用于药物分子对接设计。     

基于容错机制的精细药物分子对接网格

针对药物分子对接的搜索空间大、耗费时间长、对计算环境要求高等问题,给出一种将网格技术应用于药物分子对接的方案。基于遗传算法多种群竞争机制的对接演化模型GasDock,以信息熵控制空间的收缩,增强进化的目的性,提高对接效率。提出局部类树型结构及结点失效的容错机制,提高了精细药物分子对接任务的效率及容错性。测试结果证明了精细药物分子对接与网格技术相结合的合理性和有效性。     

计算机辅助药物分子对接并行演化设计

对分子对接理论作了简单介绍，建立了一个基于柔性配体分子与刚性受体分子对接的数学规划模型，将分子对接中的构象优化搜索转化为求解约束极小化问题的过程，并采用带有空间收缩的多种群并行遗传算法进行求解．在分布式存储的并行机曙光3000上模拟计算表明，该设计具有很高的并行加速比，在保证分子对接的准确性和有效性的前提下，大大提高了分子构象搜索的速度．     

基于演化设计的遗传算法在分子对接中的应用

将自然界的物种动态模型引入到遗传算法当中,反映出物种的真实进化状态,开发了基于演化设计的遗传算法。算法采用自适应策略克服了确定交叉和变异概率值的问题,利用小种群策略和最优保留策略保证了种群的多样性,改善了算法的寻优能力,进而提高了计算效率。运用该遗传算法求解分子对接优化模型,给出基于演化设计的分子对接程序。对接实例表明,算法能有效应用于分子对接问题中。     

一种基于免疫遗传算法的分子对接构象搜索策略

分子对接是计算机辅助药物分子优化设计中的一种重要方法,搜索算法和评分函数是当前分子对接研究的难点与热点.在借鉴当前分子对接构象搜索策略的基础上,提出了一个基于免疫遗传算法的分子对接药物设计方法AutoDockIGA.首先建立了基于最优化方法的分子对接数学模型AutoDockModel,并设计了基于免疫遗传算法的构象搜索策略,运用此方法对布克海文蛋白质数据库中(Brookhaven Protein Data Bank)的6种蛋白质一配体复合物进行了实验测试,并将实验结果与AutoDock3.0、模拟退火算法的对接时间和精度进行比较和分析,实例测试表明AutoDockIGA具有更高的寻优能力.     

基于寿命的变种群模糊遗传算法

针对简单遗传算法存在早收敛和在进化后期搜索效率较低的缺点，提出了一种种群数变化的模糊遗传算法．该算法对进化种群数进行宏观调控的同时，再用个体寿命限制个体的生存期，实现对种群数的微观调控．并采用模糊控制器控制交叉率，使其能够根据进化的实际情况自动调整．实验数据表明这种方法能够有效防止早收敛，大大改善遗传算法收敛性能．     

基于SAC模型的改进遗传算法求解TSP问题

遗传算法(GA)的全局搜索能力强,易于操作,但其收敛速度慢,易陷入局部最优值.针对以上问题,利用深度强化学习模型SAC对遗传算法进行改进,并将其应用至旅行商问题(TSP)的求解.改进算法将种群作为与智能体(agent)交互的环境,引入贪心算法对环境进行初始化,使用改进后的交叉与变异运算作为agent的动作空间,将种群的进化过程视为一个整体,以最大化种群进化过程的累计奖励为目标,结合当前种群个体适应度情况,采用基于SAC的策略梯度算法,生成控制种群进化的动作策略,合理运用遗传算法的全局和局部搜索能力,优化种群的进化过程,平衡种群收敛速度与遗传操作次数之间的关系.对TSPLIB实例的实验结果表明,改进的遗传算法可有效地避免陷入局部最优解,在提高种群收敛速度的同时,减少寻优过程的迭代次数.     

基于分布式遗传算法的移动Agent迁移策略

迁移策略是移动Agent（Mobile Agent，MA）的核心技术之一，MA的效率很大程度上取决于迁移策略的优化。本文提出了一种改进的分布式遗传算法（EDGA），用于对多约束条件下MA迁移策略最优问题进行求解。EDGA将分布式遗传算法和Cascade模型相结合，在迁移算子部分设计一个中心监控器，观察每个子种群的进化，并对迁移个体的选择以及相应子种群的大小做出调整，使进化能力好的子种群得到更大的空间来搜索最优值。实验结果表明：本文所提出的EDGA算法在求解速度和质量上取得了较大的改善。     

基于多种群进化小生境遗传算法的神经网络进化设计方法研究

针对神经网络进化设计问题中棋型解基因编码与棋型解空间的特点，提出了多种群进化小生境遗传算法。该神经网络进化楚棋方法设计简单、通用，棋型性能评价全面合理，全局搜索效率高，电力负荷预测支持系统的实际应用效果表明此方法是有效的，具有一定的应用推广价值。     

自适应分区多代理模型交互式遗传算法

为提高交互式遗传算法的性能.提出一种自适应分区多代理模型交互式遗传算法.该算法基于关键维分割进化初期的搜索空间,同时基于进化进程、逼近精度以及用户评价敏感度,自适应地分割进化中后期的搜索空间.在子空间上,采用多类代理模型学习用户对进化个体评价,并用于评价后续进化的部分或全部个体.将该算法应用于服装进化设计系统,实验结果表明,算法在种群多样性、减轻用户疲劳及用户对优化结果满意度等方面均具有优越性.     

Adaptive molecular docking method based on information entropy genetic algorithm

Almost all the molecule docking models, using by widespread docking software, are approximate. Approximation will make the scoring function inaccurate under some circumstances. This study proposed a new molecule docking scoring method: based on force-field scoring function, it use information entropy genetic algorithm to solve the docking problem. Empirical-based and knowledge-based scoring function are also considered in this method. Instead of simple combination with fixed weights, coefficients of each factor are adaptive in the process of searching optimum solution. Genetic algorithm with the multi-population evolution and entropy-based searching technique with narrowing down space is used to solve the optimization model for molecular docking problem. To evaluate this method, we carried out a numerical experiment with 134 protein–ligand complexes of the publicly available GOLD test set. The results show that this study improved the docking accuracy over the individual force-field scoring greatly. Comparing with other popular docking software, it has the best average Root-Mean-Square Deviation (RMSD). The average computing time of this study is also good among them.     

Bio-inspired optimization for the molecular docking problem: State of the art,recent results and perspectives

Molecular docking is a Bioinformatics method based on predicting the position and orientation of a small molecule or ligand when it is bound to a target macromolecule. This method can be modeled as an optimization problem where one or more objectives can be defined, typically around an energy scoring function. This paper reviews developments in the field of single- and multi-objective meta-heuristics for efficiently addressing molecular docking optimization problems. We comprehensively analyze both problem formulations and applied techniques from Evolutionary Computation and Swarm Intelligence, jointly referred to as Bio-inspired Optimization. Our prospective analysis is supported by an experimental study dealing with a molecular docking problem driven by three conflicting objectives, which is tackled by using different multi-objective heuristics. We conclude that genetic algorithms are the most widely used techniques by far, with a noted increasing prevalence of particle swarm optimization in the last years, being these last techniques particularly adequate when dealing with multi-objective formulations of molecular docking problems. We end this experimental survey by outlining future research paths that should be under target in this vibrant area.     

蛋白质-配体分子对接中构象搜索方法

分子对接是研究蛋白质-配体分子间相互作用与识别的有效方法。分子间的相互作用过程中形成的近天然构象是结合自由能极低的构象,快速且准确搜索能量极低的构象对于蛋白质-配体分子对接至关重要。本文回顾了蛋白质-配体分子对接中主要的构象搜索算法,包括快速穷举搜索、启发式搜索和其他搜索方法,并列举了采用不同搜索算法的代表性分子对接软件。其次,介绍了蛋白质-配体分子对接的国际评估实验、常用的测试标准库和评价的重要指标。最后,分析并指出了当前蛋白质-配体对接构象搜索方法所存在的主要问题,并对未来的工作进行了展望。     

Application of numerical optimization methods to molecular docking on graphics processing units

This paper is devoted to analyzing numerical optimization methods for solving the problem of molecular docking. Some additional requirements for optimization methods that take into account certain architectural features of graphics processing units (GPUs) have been formulated. A promising optimization method for use on graphics processors has been selected, its implementation is described, and its efficiency and accuracy have been estimated.     

基于粒子群优化算法的支持向量机参数选择及其应用

参数选择是支持向量机(SVM)研究领域的重要问题,它的本质是一个优化搜索过程,考虑到进化算法在求解优化问题上的有效性,提出了以最小化k-fold交叉验证误差为目标．粒子群优化(PSO)算法为寻优技巧的SVM参数调整方法．通过仿真例子验证该方法的有效性后,用其建立了聚丙烯腈生产过程中数均分子量的软测量模型,结果表明该方法有效．     

eHiTS: a new fast, exhaustive flexible ligand docking system

The flexible ligand docking problem is divided into two subproblems: pose/conformation search and scoring function. For successful virtual screening the search algorithm must be fast and able to find the optimal binding pose and conformation of the ligand. Statistical analysis of experimental data of bound ligand conformations is presented with conclusions about the sampling requirements for docking algorithms. eHiTS is an exhaustive flexible-docking method that systematically covers the part of the conformational and positional search space that avoids severe steric clashes, producing highly accurate docking poses at a speed practical for virtual high-throughput screening. The customizable scoring function of eHiTS combines novel terms (based on local surface point contact evaluation) with traditional empirical and statistical approaches. Validation results of eHiTS are presented and compared to three other docking software on a set of 91 PDB structures that are common to the validation sets published for the other programs.     

An Immune Algorithm for Protein Structure Prediction on Lattice Models

We present an immune algorithm (IA) inspired by the clonal selection principle, which has been designed for the protein structure prediction problem (PSP). The proposed IA employs two special mutation operators, hypermutation and hypermacromutation to allow effective searching, and an aging mechanism which is a new immune inspired operator that is devised to enforce diversity in the population during evolution. When cast as an optimization problem, the PSP can be seen as discovering a protein conformation with minimal energy. The proposed IA was tested on well-known PSP lattice models, the HP model in two-dimensional and three-dimensional square lattices', and the functional model protein, which is a more realistic biological model. Our experimental results demonstrate that the proposed IA is very competitive with the existing state-of-art algorithms for the PSP on lattice models