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De novo drug design framework based on mathematical programming method and deep learning model

AICHE JOURNAL 2022年第9期68卷 e17748-e17748页

作者： Zhao, Yujing Liu, Qilei Wu, Xinyuan Zhang, Lei Du, Jian Meng, Qingwei Dalian Univ Technol Sch Chem Engn Inst Chem Proc Syst Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Pharmaceut Sci & Technol State Key Lab Fine Chem Dalian 116024 Peoples R China Dalian Univ Technol Ningbo Inst Ningbo Peoples R China

Small-molecule drugs are of significant importance to human health. The use of efficient model-based de novo drug design method is an option worth considering for expediting the discovery of drugs with satisfactory properties. In this article, a deep learning model is first developed for identifications of protein-ligand complexes with high binding affinity, where the Mol2vec descriptor, the convolutional neural network, and the gate augmentation-based Attention mechanism are used for the model construction. Then, an optimization-based de novo drug design framework is established by integrating the deep learning model into a Mixed-Integer NonLinear programming (MINLP) model for drug candidate design. The optimal solution of the MINLP model is further verified by the physics-based methods of molecular docking and molecular dynamics simulation. Finally, two case studies involving the design of anticoagulant and antitumor drug candidates are presented to highlight the wide applicability and effectiveness of the MINLP-based de novo drug design framework.

关键词： binding affinity chemical product design deep learning drug design mathematical programming method

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A mathematical programming method FOR THE TOPOLOGY OPTIMIZATION OF A TRUSS-LIKE CONTINUUM

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JOURNAL OF THEORETICAL AND APPLIED MECHANICS 2019年第3期57卷 751-763页

作者： Cui, Hao Zhou, Kemin Huaqiao Univ Coll Civil Engn Xiamen Peoples R China

A mathematical programming method to optimize the distribution field of a truss-like material is presented. The densities and angles of members are optimized in two separate procedures in each iteration. An explicit sub-problem in a variable separation form is established at every iteration procedure. At each sub-problem, the stress constraint function is expanded into a trigonometric series of the member angles. According to the extreme condition, the optimal orientations of members are determined. The member densities are optimized using the method of moving asymptotes (MMA). Two examples demonstrate that the optimal truss-like structures are very close to analytic solutions.

关键词： topology optimization mathematical programming method truss-like continuum

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A mathematical programming method for the Multiple Attribute Decision Making with Interval Intuitionistic Fuzzy Values

A Mathematical Programming Method for the Multiple Attribute...

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IEEE International Conference on Fuzzy Systems

作者： Chu, Junfeng Liu, Xinwang Southeast Univ Sch Econ & Management Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781479920723

In this paper, we investigate the multiple attribute decision making problems where the decision-making information and attribute weight vector are both given by the interval-valued intuitionistic fuzzy number (IVIFN). We introduce a mathematical model to obtain the comprehensive value of each alternative by the form of IVIFN. Then we utilize the TOPSIS method to rank all the alternatives. Finally, an illustrative example is used to illustrate applicability of the proposed method.

关键词： multiple attribute decision making interval-valued intuitionistic fuzzy number mathematical programming method

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A mathematical programming method for the Multiple Attribute Decision Making with Interval Intuitionistic Fuzzy Values

A Mathematical Programming Method for the Multiple Attribute...

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IEEE International Conference on Fuzzy Systems

作者： Junfeng Chu Xinwang Liu School of Economics and Management Southeast University

ISBN: (纸本)9781479920747

关键词： Multiple attribute decision making Interval-valued intuitionistic fuzzy number mathematical programming method

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Accelerating Factor Xa inhibitor discovery with a de novo drug design pipeline

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Chinese Journal of Chemical Engineering 2024年第8期72卷 85-94页

作者： Yujing Zhao Qilei Liu Jian Du Qingwei Meng Liang Sun Lei Zhang State Key Laboratory of Fine Chemicals Frontiers Science Center for Smart Materials Oriented Chemical EngineeringInstitute of Chemical Process Systems EngineeringSchool of Chemical EngineeringDalian University of TechnologyDalian 116024China Ningbo Institute of Dalian University of Technology Ningbo 315016China Shenzhen Shuli Tech Co. LtdShenzhen 518126China Department of Physics City University of Hong KongTat Chee AvenueKowloonHong Kong SARChina

Small-molecule drugs are essential for maintaining human health. The objective of this study is to identify a molecule that can inhibit the Factor Xa protein and be easily procured. An optimization-based de novo drug design framework, Drug CAMD, that integrates a deep learning model with a mixed-integer nonlinear programming model is used for designing drug candidates. Within this framework, a virtual chemical library is specifically tailored to inhibit Factor Xa. To further filter and narrow down the lead compounds from the designed compounds, comprehensive approaches involving molecular docking,binding pose metadynamics(BPMD), binding free energy calculations, and enzyme activity inhibition analysis are utilized. To maximize efficiency in terms of time and resources, molecules for in vitro activity testing are initially selected from commercially available portions of customized virtual chemical libraries. In vitro studies assessing inhibitor activities have confirmed that the compound EN300-331859shows potential Factor Xa inhibition, with an IC_(50)value of 34.57 μmol·L^(-1). Through in silico molecular docking and BPMD, the most plausible binding pose for the EN300-331859-Factor Xa complex are identified. The estimated binding free energy values correlate well with the results obtained from biological assays. Consequently, EN300-331859 is identified as a novel and effective sub-micromolar inhibitor of Factor Xa.

关键词： Chemical product design mathematical programming method Deep learning Binding affinity Factor Xa inhibitor

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mathematical programming method Based on Chaos Anti-Control for the Solution of Forward Displacement of Parallel Robot Mechanisms

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2013年第1期10卷

作者： Luo, Youxin Liu, Qiyuan Che, Xiaoyi Hunan Univ Arts & Sci Coll Mech Engn Changde Peoples R China

The pose of the moving platform in parallel robots is possible thanks to the strong coupling, but it consequently is very difficult to obtain its forward displacement. Different methods establishing forward displacement can obtain different numbers of variables and different solving speeds with nonlinear equations. The nonlinear equations with nine variables for forward displacement in the general 6-6 type parallel mechanism were created using the rotation transformation matrix R, translation vector P and the constraint conditions of the rod length. Given the problems of there being only one solution and sometimes no convergence when solving nonlinear equations with the Newton method and the quasi-Newton method, the Euler equation for free rotation in a rigid body was applied to a chaotic system by using chaos anti-control and chaotic sequences were produced. Combining the characteristics of the chaotic sequence with the mathematical programming method, a new mathematical programming method was put forward, which was based on chaos anti-control with the aim of solving all real solutions of nonlinear equations for forward displacement in the general 6-6 type parallel mechanism. The numerical example shows that the new method has some positive characteristics such as that it runs in the initial value range, it has fast convergence, it can find all the possible real solutions that be found out and it proves the correctness and validity of this method when compared with other methods.

关键词： Chaos Anti-Control Parallel Mechanism mathematical programming method Nonlinear Equations

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Topology optimization of truss-like structures with bidirectional non-orthogonal materials based on mathematical programming

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OPTIMIZATION AND ENGINEERING 2024年第2期25卷 605-+页

作者： Shi, Shunyi Zhou, Kemin Huaqiao Univ Coll Civil Engn Xiamen 361021 Peoples R China

Topology optimization of structures composed of truss-like members has been shown to produce results that are very close to the theoretical solution. However, solving complex optimization problems based on the traditional orthogonal truss-like material model remains a challenge. This article proposes a unified framework for solving topology optimization problems based on a non-orthogonal truss-like material model. The framework first establishes a new non-orthogonal truss-like material model that considers the stiffness singularity problem. Several strategies for dynamically changing shear stiffness are studied comparatively. Then, a globally convergent moving asymptote method is employed in three numerical examples, including minimum compliance problems under single and multiple load cases, as well as a stress-constrained problem for an L-shaped design domain. Finally, optimal truss-like structures are obtained with the help of a simple post-processing method. Numerical examples demonstrate that the optimization results for the multiple load cases are better than those obtained using traditional methods for minimum compliance problems. The framework can efficiently solve different types of optimization problems in a unified form, which confirms the effectiveness and advantages of the proposed method.

关键词： Topology optimization Truss-like structure Bidirectional non-orthogonal mathematical programming method Moving asymptote method

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QPNet: Lane-changing trajectory planning combining quadratic programming and neural network under the convex optimization framework

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第11期16卷 1578-1599页

作者： Wang, Ying Wei, Chong Li, Shurong Beijing Jiaotong Univ Sch Traff & Transportat Key Lab Transport Ind Big Data Applicat Technol C Minist Transport Beijing 100044 Peoples R China

Lane-changing is a basic driving behaviour, which largely impacts on traffic safety and efficiency. With the development of technology, the automated lane-changing system has attracted extensive attention. Among it, the trajectory planning part is a challenging problem owing to the complexity and diversity of the driving situations. The planner requires the real-time capability to produce safe and comfortable trajectories for coping with the dynamically changing environment. Based on this, the paper proposes a lane-changing trajectory planning model in dynamic driving environments. The model constructs a neural network to predict the end position of the ego vehicle, and then adopts the mathematical programming method to solve the optimal lane-changing trajectory that guarantees the ride safety and comfort. With the assistance of the neural network, the lane-changing trajectory planning problem is converted into a quadratic programming (QP) model, thereby achieving rapid solution of the model. Moreover, to train the proposed algorithm, a novel approach for generating the scenario data is designed, which can generate rich and diverse traffic scenarios at a low cost. The simulation results show that the proposed model can plan a lane-changing trajectory quickly and effectively, and the ego vehicle can follow the planned trajectory safely and comfortably.

关键词： rich traffic scenarios traffic safety ride safety optimal lane-changing trajectory trajectory planning part road vehicles intelligent transportation systems driving situations trajectory control convex programming optimisation planned trajectory path planning convex optimization framework basic driving behaviour safe trajectories road traffic comfortable trajectories neural network quadratic programming model road safety road traffic control dynamic driving environments lane-changing trajectory planning problem automated lane-changing system quadratic programming dynamically changing environment diverse traffic scenarios lane-changing trajectory planning model ego vehicle mathematical programming method traffic engineering computing

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Spectral Reflectance Estimation from Camera Responses Using Local Optimal Dataset

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JOURNAL OF IMAGING 2023年第2期9卷 47-47页

作者： Tominaga, Shoji Sakai, Hideaki Norwegian Univ Sci & Technol Dept Comp Sci N-2815 Gjovik Norway Nagano Univ Dept Business & Informat Ueda 3860032 Japan Kyoto Univ Kyoto 6068501 Japan

A novel method is proposed to estimate surface-spectral reflectance from camera responses using a local optimal reflectance dataset. We adopt a multispectral imaging system that involves an RGB camera capturing multiple images under multiple light sources. A spectral reflectance database is utilized to locally determine the candidates to optimally estimate the spectral reflectance. The proposed estimation method comprises two stages: (1) selecting the local optimal reflectance dataset and (2) determining the best estimate using only the local optimal dataset. In (1), the camera responses are predicted for the respective reflectances in the database, and then the prediction errors are calculated to select the local optimal dataset. In (2), multiple methods are used;in particular, the Wiener and linear minimum mean square error estimators are used to calculate all statistics, based only on the local optimal dataset, and linear and quadratic programming methods are used to solve optimization problems with constraints. Experimental results using different mobile phone cameras show that the estimation accuracy has improved drastically. A much smaller local optimal dataset among spectral reflectance databases is enough to obtain the optimal estimates. The method has potential applications including fields of color science, image science and technology, computer vision, and graphics.

关键词： surface-spectral reflectance reflectance estimation multispectral imaging local optimal dataset statistical estimation method mathematical programming method

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A Hybrid Multi-Objective programming Framework for Modeling and Optimization of Supply Chain Problems

A Hybrid Multi-Objective Programming Framework for Modeling ...

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3rd International Conference on Innovative Network Systems and Applications (iNetSApp) held in conjunction with Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Sitek, Pawel Kielce Univ Technol Inst Management Control Syst Al 1000 Lecia PP 7 PL-25314 Kielce Poland

ISBN: (纸本)9788360810668

This paper presents a hybrid programming framework for solving multi-objective optimization problems in supply chain. The proposed approach consists of the integration and hybridization of two modeling and solving environments, i.e., constraint logic programming and mathematical programming, to obtain a programming framework that offers significant advantages over the classical approach derived from operational research. The strongest points of both components are combined in the hybrid framework, which by introducing transformation allows a significant reduction in size of a problem and the optimal solution is found a lot faster. This is particularly important in the multi-objective optimization where problems have to be solved over and over again to find a set of Pareto-optimal solutions. An over two thousand-fold reduction in size was obtained for the illustrative examples together with a few hundred-fold reduction in the speed of finding the solution in relation to the mathematical programming method. In addition, the proposed framework allows the introduction of logical constraints that are difficult or impossible to model in operational research environments.

关键词： Pareto optimisation mathematical programming supply chain management Pareto-optimal solutions constraint logic programming hybrid multiobjective programming framework mathematical programming method operational research environments supply chain problems Linear programming Logic programming mathematical model mathematical programming Supply chains

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