多靶点药物能同时调节多靶点、调节疾病网络的多个环节,在获得较高疗效的同时可降低单靶点引起的毒副作用,是治疗复杂性疾病的理想药物,因此已成为药物研发的主要方向。而天然产物凭借其结构的多样性,较高的多靶点活性和较小的毒副作用...多靶点药物能同时调节多靶点、调节疾病网络的多个环节,在获得较高疗效的同时可降低单靶点引起的毒副作用,是治疗复杂性疾病的理想药物,因此已成为药物研发的主要方向。而天然产物凭借其结构的多样性,较高的多靶点活性和较小的毒副作用等优势,是多靶点药物开发的重要来源。计算机辅助药物设计(computer-aided drug design,CADD)是常用的多靶点药物研发方法,其主要包括虚拟筛选和药效团设计。该文对其进行了系统梳理,探讨了各方法用于天然产物多靶点药物研发的前景与优势。展开更多
Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical propertie...Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical properties of rockfill from laboratory tests. Parameters inversion based on in situ monitoring data has been proven to be an efficient method for identifying the exact parameters of the rockfill. In this paper, we propose a modified genetic algorithm to solve the high-dimension multimodal and nonlinear optimal parameters inversion problem. A novel crossover operator based on the sum of differences in gene fragments(So DX) is proposed, inspired by the cloning of superior genes in genetic engineering. The crossover points are selected according to the difference in the gene fragments, defining the adaptive length. The crossover operator increases the speed and accuracy of algorithm convergence by reducing the inbreeding and enhancing the global search capability of the genetic algorithm. This algorithm is compared with two existing crossover operators. The modified genetic algorithm is then used in combination with radial basis function neural networks(RBFNN) to perform the parameters back analysis of a high central earth core rockfill dam. The settlements simulated using the identified parameters show good agreement with the monitoring data, illustrating that the back analysis is reasonable and accurate. The proposed genetic algorithm has considerable superiority for nonlinear multimodal parameter identification problems.展开更多
文摘多靶点药物能同时调节多靶点、调节疾病网络的多个环节,在获得较高疗效的同时可降低单靶点引起的毒副作用,是治疗复杂性疾病的理想药物,因此已成为药物研发的主要方向。而天然产物凭借其结构的多样性,较高的多靶点活性和较小的毒副作用等优势,是多靶点药物开发的重要来源。计算机辅助药物设计(computer-aided drug design,CADD)是常用的多靶点药物研发方法,其主要包括虚拟筛选和药效团设计。该文对其进行了系统梳理,探讨了各方法用于天然产物多靶点药物研发的前景与优势。
基金supported by the National Natural Science Foundation of China(Grant Nos.51379161&51509190)China Postdoctoral Science Foundation(Grant No.2015M572195)the Fundamental Research Funds for the Central Universities
文摘Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical properties of rockfill from laboratory tests. Parameters inversion based on in situ monitoring data has been proven to be an efficient method for identifying the exact parameters of the rockfill. In this paper, we propose a modified genetic algorithm to solve the high-dimension multimodal and nonlinear optimal parameters inversion problem. A novel crossover operator based on the sum of differences in gene fragments(So DX) is proposed, inspired by the cloning of superior genes in genetic engineering. The crossover points are selected according to the difference in the gene fragments, defining the adaptive length. The crossover operator increases the speed and accuracy of algorithm convergence by reducing the inbreeding and enhancing the global search capability of the genetic algorithm. This algorithm is compared with two existing crossover operators. The modified genetic algorithm is then used in combination with radial basis function neural networks(RBFNN) to perform the parameters back analysis of a high central earth core rockfill dam. The settlements simulated using the identified parameters show good agreement with the monitoring data, illustrating that the back analysis is reasonable and accurate. The proposed genetic algorithm has considerable superiority for nonlinear multimodal parameter identification problems.
