Multi-objective Genetic Algorithm for De Novo Drug Design

2020 ◽  
Vol 16 ◽  
Author(s):  
R. Vasundhara Devi ◽  
S. Siva Sathya ◽  
S. Mohane Coumar
Keyword(s):  
Genetic Algorithm ◽  
Drug Design ◽  
De Novo ◽  
New Drugs ◽  
Weighted Sum ◽  
De Novo Drug Design ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Fragment Library ◽  
Novel Drug

Background: Genetic algorithm being a famous evolutionary algorithm, multiple objectives of drug design are solved using weighted sum approach. Objective: To design a computational tool for the de novo design of novel drug-like molecules to aid in the discovery of new drugs using Genetic algorithm and chemical fragment library and reference molecules. Method: Multi-objective optimization using genetic algorithm and weighted sum approach. Results: The drug-like molecules for the reference molecules such as Lidocaine, Furano-pyrimidine, Imatinib, Atorvastatin and Glipizide. Conclusion: The performance of the MOGADdrug tool is evaluated using 5 reference molecules and the designed molecules are compared with Zinc and Pubchem databases along with their docking investigations.

Analysis and Modeling of Supply Chain Management of Fresh Products Based on Genetic Algorithm in E-commerce Environment

2020 ◽  
Author(s):  
Yaoting Chen
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Weighted Sum ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Optimal Process ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Proposed Model ◽  
Fresh Products

Abstract BackgroundSupply chain provides the chance to enhance chain performances by decrease these uncertainties. It is a demand for some level of co-ordination of activities and processes within and between organization in the supply chain to decrease uncertainties and increase more cost for customers. Partner selection is an important issue in the supply chain management of fresh products in E-commerce environment. In this paper, we utilized a multi-objective genetic algorithm for evaluation supply chain of fresh products in E-commerce environment. ResultsThe proposed multi-objective genetic algorithm is to search the set of Pareto-optimal solutions for these conflicting objectives using by weighted sum approach. The proposed model suitable for fresh products in E-commerce environment to optimize supply chain are derived. The value of objective 1 (f1) performs approximately nonlinearly with the increasing the value of objective 2,3 and 4 (f2,f3 and f4). At the value of objective 1 of 3.2*105, f2, f3 and f4 is about 4.3*105, 86 and 5.6*104. When the value of objective 1 is increased to 7.6*105, the minimum f2, f3 and f4 is about 3.0*105, 38 and 2.56*104. It is noted that the value of objective 1 is increased from 6.4*105 to 7.6*105, the variation of f2, f3 and f4 is 11.7%, 17.4% and 3.4% respectively. It is pointed out that the variation of f2 and f3 with f1 and f4 is kept within obvious ranges. This practical result highlights the fact that the effects of the fact that effects of f2 and f3 are important factors affecting the performance supply chain network of fresh product in E-commerce environment.ConclusionsIn this paper, we utilized a multi-objective genetic algorithm for evaluation supply chain of fresh products in E-commerce environment. Four objectives for optimal process are included in the proposed model: (1) maximization of green appraisal score, (2) minimization of transportation time and total time comprised of product time, (3) maximization of average product quality, (4) minimization of transportation cost and total cost comprised of product cost. In order to evaluate optimal process, set of Pareto-optimal solutions is obtained based on the weighted sum method.

Genetic and hybrid algorithms for optimization of non-singular 3PRR planar parallel kinematics mechanism for machining application

Robotica ◽  
2018 ◽  
Vol 36 (6) ◽  
pp. 839-864 ◽  
Author(s):  
Abdur Rosyid ◽  
Bashar El-Khasawneh ◽  
Anas Alazzam
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Weighted Sum ◽  
Parallel Kinematics ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Cartesian Stiffness Matrix ◽  
Optimal Value ◽  
Single Objective

SUMMARYThis paper proposes a special non-symmetric topology of a 3PRR planar parallel kinematics mechanism, which naturally avoids singularity within the workspace and can be utilized for hybrid kinematics machine tools. Subsequently, single-objective and multi-objective optimizations are conducted to improve the performance. The workspace area and minimum eigenvalue, as well as the condition number of the homogenized Cartesian stiffness matrix across the workspace, have been chosen as the objectives in the optimization based on their relevance to the machining application. The single-objective optimization is conducted by using a single-objective genetic algorithm and a hybrid algorithm, whereas the multi-objective optimization is conducted by using a multi-objective genetic algorithm, a weighted sum single-objective genetic algorithm, and a weighted sum hybrid algorithm. It is shown that the single-objective optimization gives superior value in the optimized objective, while sacrificing the other objectives, whereas the multi-objective optimization compromises the improvement of all objectives by providing non-dominated values. In terms of the algorithms, it is shown that a hybrid algorithm can either verify or refine the optimal value obtained by a genetic algorithm.

Multi-objective biofilm algorithm (MOBifi) for de novo drug design with special focus to anti-diabetic drugs

2020 ◽  
Vol 96 ◽  
pp. 106655
Author(s):  
R. Vasundhara Devi ◽  
S. Siva Sathya ◽  
Mohane Selvaraj Coumar
Keyword(s):  
Drug Design ◽  
De Novo ◽  
Special Focus ◽  
De Novo Drug Design ◽  
Multi Objective

Lead Molecules as Novel Aromatase Inhibitors: In Silico De Novo Designing and Binding Affinity Studies

2020 ◽  
Vol 17 (5) ◽  
pp. 655-665 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Drug Design ◽  
Aromatase Inhibitors ◽  
De Novo ◽  
Scale Up ◽  
Geometry Optimization ◽  
Docking Study ◽  
Molecular Docking Study ◽  
De Novo Drug Design ◽  
Molegro Virtual Docker ◽  
Estrogen Production

Background:Aromatase inhibitors emerged as a pivotal moiety to selectively block estrogen production, prevention and treatment of tumour growth in breast cancer. De novo drug design is an alternative approach to blind virtual screening for successful designing of the novel molecule against various therapeutic targets.Objective:In the present study, we have explored the de novo approach to design novel aromatase inhibitors.Method:The e-LEA3D, a computational-aided drug design web server was used to design novel drug-like candidates against the target aromatase. For drug-likeness ADME parameters (molecular weight, H-bond acceptors, H-bond donors, LogP and number of rotatable bonds) of designed molecules were calculated in TSAR software package, geometry optimization and energy minimization was accomplished using Chem Office. Further, molecular docking study was performed in Molegro Virtual Docker (MVD).Results:Among 17 generated molecules using the de novo pathway, 13 molecules passed the Lipinski filter pertaining to their bioavailability characteristics. De novo designed molecules with drug-likeness were further docked into the mapped active site of aromatase to scale up their affinity and binding fitness with the target. Among de novo fabricated drug like candidates (1-13), two molecules (5, 6) exhibited higher affinity with aromatase in terms of MolDock score (-150.650, -172.680 Kcal/mol, respectively) while molecule 8 showed lowest target affinity (-85.588 Kcal/mol).Conclusion:The binding patterns of lead molecules (5, 6) could be used as a pharmacophore for medicinal chemists to explore these molecules for their aromatase inhibitory potential.

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