De novo generation of optically active small organic molecules using Monte Carlo tree search combined with recurrent neural network

2020 ◽  
Vol 42 (3) ◽  
pp. 136-143
Author(s):  
Motomichi Tashiro ◽  
Yutaka Imamura ◽  
Michio Katouda
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Recurrent Neural Network ◽  
Organic Molecules ◽  
De Novo ◽  
Optically Active ◽  
Tree Search ◽  
Small Organic Molecules ◽  
Monte Carlo Tree Search

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 De Novo Prediction of RNA 3D Structures with Deep Learning

2021 ◽  
Author(s):  
Julius Ramakers ◽  
Christopher Frederik Blum ◽  
Sabrina König ◽  
Stefan Harmeling ◽  
Markus Kollmann
Keyword(s):  
Monte Carlo ◽  
Deep Learning ◽  
Nucleic Acid ◽  
Predictive Power ◽  
De Novo ◽  
Learning Approach ◽  
Tree Search ◽  
Blind Test ◽  
Monte Carlo Tree Search ◽  
Score Model

We present a Deep Learning approach to predict 3D folding structures of RNAs from their nucleic acid sequence. Our approach combines an autoregressive Deep Generative Model, Monte Carlo Tree Search, and a Score Model to find and rank the most likely folding structures for a given RNA sequence. We confirm the predictive power of our approach by setting new benchmarks for some longer sequences in a simulated blind test of the RNA Puzzles prediction challenge.

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