scholarly journals AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Samuel Genheden ◽  
Amol Thakkar ◽  
Veronika Chadimová ◽  
Jean-Louis Reymond ◽  
Ola Engkvist ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Open Source ◽  
Open Source Software ◽  
Automatic Testing ◽  
Testing System ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Complete Search ◽  
Automatic Testing System

AbstractWe present the open-source AiZynthFinder software that can be readily used in retrosynthetic planning. The algorithm is based on a Monte Carlo tree search that recursively breaks down a molecule to purchasable precursors. The tree search is guided by an artificial neural network policy that suggests possible precursors by utilizing a library of known reaction templates. The software is fast and can typically find a solution in less than 10 s and perform a complete search in less than 1 min. Moreover, the development of the code was guided by a range of software engineering principles such as automatic testing, system design and continuous integration leading to robust software with high maintainability. Finally, the software is well documented to make it suitable for beginners. The software is available at http://www.github.com/MolecularAI/aizynthfinder.

scholarly journals AiZynthFinder: A Fast Robust and Flexible Open-Source Software for Retrosynthetic Planning

2020 ◽  
Author(s):  
Samuel Genheden ◽  
Amol Thakkar ◽  
Veronika Chadimova ◽  
Jean-Louis Reymond ◽  
Ola Engkvist ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Object Oriented ◽  
Automatic Testing ◽  
Testing System ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Object Oriented Design ◽  
Complete Search ◽  
Automatic Testing System

We present the open-source AiZynthFinder software that can be readily used in retrosynthetic planning. The algorithm is based on a Monte Carlo tree search that recursively breaks down a molecule to purchasable precursors. The tree search is guided by an artificial neural network policy that suggests possible precursors by utilizing a library of known reaction templates. The software is fast and can typically find a solution in less than 10 seconds and perform a complete search in less than 1 minute. Moreover, the writing of the code was guided by a range of software engineering principles such as automatic testing, system design and continuous integration leading to robust software. The object-oriented design makes the software very flexible and can straightforwardly be extended to support a range of new features. Finally, the software is clearly documented and should be easy to get started with. The software is available at http://www.github.com/MolecularAI/aizynthfinder.

scholarly journals AiZynthFinder: A Fast Robust and Flexible Open-Source Software for Retrosynthetic Planning

2020 ◽  
Author(s):  
Samuel Genheden ◽  
Amol Thakkar ◽  
Veronika Chadimova ◽  
Jean-Louis Reymond ◽  
Ola Engkvist ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Object Oriented ◽  
Automatic Testing ◽  
Testing System ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Object Oriented Design ◽  
Complete Search ◽  
Automatic Testing System

We present the open-source AiZynthFinder software that can be readily used in retrosynthetic planning. The algorithm is based on a Monte Carlo tree search that recursively breaks down a molecule to purchasable precursors. The tree search is guided by an artificial neural network policy that suggests possible precursors by utilizing a library of known reaction templates. The software is fast and can typically find a solution in less than 10 seconds and perform a complete search in less than 1 minute. Moreover, the writing of the code was guided by a range of software engineering principles such as automatic testing, system design and continuous integration leading to robust software. The object-oriented design makes the software very flexible and can straightforwardly be extended to support a range of new features. Finally, the software is clearly documented and should be easy to get started with. The software is available at http://www.github.com/MolecularAI/aizynthfinder.

Automatic testing system for electricity meters

1975 ◽  
Vol 18 (11) ◽  
pp. 1701-1702
Author(s):  
S. R. Élkin ◽  
V. V. Ivanov ◽  
S. Sh. Simaev ◽  
B. Sh. Barbakadze ◽  
B. D. Pkhakadze ◽  
...  
Keyword(s):  
Automatic Testing ◽  
Testing System ◽  
Automatic Testing System

A Novel Automatic Testing System Close-loop Model Based on Event Feedback

2013 ◽  
Vol 12 (14) ◽  
pp. 2943-2949
Author(s):  
Shuai Wang ◽  
Yindong Ji ◽  
Wei Dong ◽  
Xinya Sun ◽  
Yafang Liu
Keyword(s):  
Automatic Testing ◽  
Testing System ◽  
Loop Model ◽  
Model Based ◽  
Automatic Testing System

scholarly journals Deep Neural Network and Monte Carlo Tree Search applied to Fluid-Structure Topology Optimization

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Audrey Gaymann ◽  
Francesco Montomoli
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Topology Optimization ◽  
Deep Neural Network ◽  
Level Set Methods ◽  
Test Case ◽  
Tree Search ◽  
Fluid Structure ◽  
Monte Carlo Tree Search ◽  
Structure Topology

Abstract This paper shows the application of Deep Neural Network algorithms for Fluid-Structure Topology Optimization. The strategy offered is a new concept which can be added to the current process used to study Topology Optimization with Cellular Automata, Adjoint and Level-Set methods. The design space is described by a computational grid where every cell can be in two states: fluid or solid. The system does not require human intervention and learns through an algorithm based on Deep Neural Network and Monte Carlo Tree Search. In this work the objective function for the optimization is an incompressible fluid solver but the overall optimization process is independent from the solver. The test case used is a standard duct with back facing step where the optimizer aims at minimizing the pressure losses between inlet and outlet. The results obtained with the proposed approach are compared to the solution via a classical adjoint topology optimization code.

scholarly journals Clearing Kidney Exchanges via Graph Neural Network Guided Tree Search (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13989-13990
Author(s):  
Zeyu Zhao ◽  
John P. Dickerson
Keyword(s):  
Neural Network ◽  
Independent Set ◽  
Integer Program ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Tree Search ◽  
Scalable Algorithms ◽  
Monte Carlo Tree Search ◽  
Kidney Exchange ◽  
Stage Renal Disease

Kidney exchange is an organized barter market that allows patients with end-stage renal disease to trade willing donors—and thus kidneys—with other patient-donor pairs. The central clearing problem is to find an arrangement of swaps that maximizes the number of transplants. It is known to be NP-hard in almost all cases. Most existing approaches have modeled this problem as a mixed integer program (MIP), using classical branch-and-price-based tree search techniques to optimize. In this paper, we frame the clearing problem as a Maximum Weighted Independent Set (MWIS) problem, and use a Graph Neural Network guided Monte Carlo Tree Search to find a solution. Our initial results show that this approach outperforms baseline (non-optimal but scalable) algorithms. We believe that a learning-based optimization algorithm can improve upon existing approaches to the kidney exchange clearing problem.

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