Monte Carlo Tree Search with macro-actions and heuristic route planning for the Multiobjective Physical Travelling Salesman Problem

2013 ◽  
Author(s):  
Edward J. Powley ◽  
Daniel Whitehouse ◽  
Peter I. Cowling
Keyword(s):  
Monte Carlo ◽  
Travelling Salesman Problem ◽  
Route Planning ◽  
Tree Search ◽  
Travelling Salesman ◽  
Monte Carlo Tree Search

scholarly journals Automatic Retrosynthetic Pathway Planning Using Template-free Models

2019 ◽  
Author(s):  
Kangjie Lin ◽  
Jianfeng Pei ◽  
Luhua Lai ◽  
Youjun Xu,
Keyword(s):  
Monte Carlo ◽  
Route Planning ◽  
The United States ◽  
Single Step ◽  
Drug Candidate ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Patent Literature ◽  
Acute Myeloid Leukemia Cells ◽  
Transformer Model

<div><div><div><p>We present an attention-based Transformer model for automatic retrosynthesis route planning. Our approach starts from <a></a><a>reactants prediction of single-step organic reactions for gi</a>ven products, <a>followed by Monte Carlo tree search-based automatic retrosynthetic pathway prediction</a>. Trained on two datasets from the United States patent literature, our models achieved a top-1 prediction accuracy of over 54.6% and 63.0% with more than 95% and 99.6% validity rate of SMILES, respectively, which is the best up to now to our knowledge. We also demonstrate the application potential of our model by successfully performing multi-step retrosynthetic route planning for four case products, i.e., antiseizure drug Rufinamide, a novel allosteric activator, an inhibitor of human acute-myeloid-leukemia cells and a complex intermediate of drug candidate. Further, by using heuristics Monte Carlo tree search, we achieved automatic retrosynthetic pathway searching and successfully reproduced published synthesis pathways. In summary, our model has achieved the state-of-the-art performance on single-step retrosynthetic prediction and provides a novel strategy for automatic retrosynthetic pathway planning. </p><div> <div><div><p><br></p></div></div><div><div> </div> </div> </div><br><p></p></div></div></div>

scholarly journals Automatic retrosynthetic route planning using template-free models

2020 ◽  
Vol 11 (12) ◽  
pp. 3355-3364 ◽  
Author(s):  
Kangjie Lin ◽  
Youjun Xu ◽  
Jianfeng Pei ◽  
Luhua Lai
Keyword(s):  
Monte Carlo ◽  
Route Planning ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Free Model ◽  
Template Free

Retrosynthetic pathway planning using a template-free model coupled with heuristic Monte Carlo tree search.

scholarly journals Automatic Retrosynthetic Pathway Planning Using Template-free Models

2019 ◽  
Author(s):  
Kangjie Lin ◽  
Jianfeng Pei ◽  
Luhua Lai ◽  
Youjun Xu,
Keyword(s):  
Monte Carlo ◽  
Route Planning ◽  
The United States ◽  
Single Step ◽  
Drug Candidate ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Patent Literature ◽  
Acute Myeloid Leukemia Cells ◽  
Transformer Model

<div><div><div><p>We present an attention-based Transformer model for automatic retrosynthesis route planning. Our approach starts from <a></a><a>reactants prediction of single-step organic reactions for gi</a>ven products, <a>followed by Monte Carlo tree search-based automatic retrosynthetic pathway prediction</a>. Trained on two datasets from the United States patent literature, our models achieved a top-1 prediction accuracy of over 54.6% and 63.0% with more than 95% and 99.6% validity rate of SMILES, respectively, which is the best up to now to our knowledge. We also demonstrate the application potential of our model by successfully performing multi-step retrosynthetic route planning for four case products, i.e., antiseizure drug Rufinamide, a novel allosteric activator, an inhibitor of human acute-myeloid-leukemia cells and a complex intermediate of drug candidate. Further, by using heuristics Monte Carlo tree search, we achieved automatic retrosynthetic pathway searching and successfully reproduced published synthesis pathways. In summary, our model has achieved the state-of-the-art performance on single-step retrosynthetic prediction and provides a novel strategy for automatic retrosynthetic pathway planning. </p><div> <div><div><p><br></p></div></div><div><div> </div> </div> </div><br><p></p></div></div></div>

scholarly journals Tourist Route Optimization in the Context of Covid-19 Pandemic

2021 ◽  
Vol 13 (10) ◽  
pp. 5492
Author(s):  
Cristina Maria Păcurar ◽  
Ruxandra-Gabriela Albu ◽  
Victor Dan Păcurar
Keyword(s):  
Travelling Salesman Problem ◽  
Route Planning ◽  
Route Optimization ◽  
Travelling Salesman ◽  
Social Distancing ◽  
Tourist Destinations ◽  
Innovative Method ◽  
The Social ◽  
Immediate Response

The paper presents an innovative method for tourist route planning inside a destination. The necessity of reorganizing the tourist routes within a destination comes as an immediate response to the Covid-19 crisis. The implementation of the method inside tourist destinations can bring an important advantage in transforming a destination into a safer one in times of Covid-19 and post-Covid-19. The existing trend of shortening the tourist stay length has been accelerated while the epidemic became a pandemic. Moreover, the wariness for future pandemics has brought into spotlight the issue of overcrowded attractions inside a destination at certain moments. The method presented in this paper proposes a backtracking algorithm, more precisely an adaptation of the travelling salesman problem. The method presented is aimed to facilitate the navigation inside a destination and to revive certain less-visited sightseeing spots inside a destination while facilitating conformation with the social distancing measures imposed for Covid-19 control.

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