Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach | Paper Citation Record

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This paper cites Abd Elwakil, Ryota Suzuki, Alaa M.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Abd Elwakil, Ryota Suzuki, Alaa M

Reference 1

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Unresolved cited work

Reference 2

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This paper cites DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation

Reference 3

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This paper cites Deep convolutional generative adversarial network ( dcGAN ) models for screening and design of small molecules targeting cannabinoid receptors.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Deep convolutional generative adversarial network ( dcGAN ) models for screening and design of small molecules targeting cannabinoid receptors

Reference 4

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This paper cites Kusner, Marwin H.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Kusner, Marwin H

Reference 5

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This paper cites Carrasco, Suman Alishetty, Mohamad-Gabriel Alameh, Hooda Said, Lacey Wright, Mikell Paige, Ousamah Soliman, Drew Weissman, Thomas E.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Carrasco, Suman Alishetty, Mohamad-Gabriel Alameh, Hooda Said, Lacey Wright, Mikell Paige, Ousamah Soliman, Drew Weissman, Thomas E

Reference 6

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This paper cites Efficient selectivity and backup operators in monte-carlo tree search.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Efficient selectivity and backup operators in monte-carlo tree search

Reference 7

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Unresolved cited work

Reference 8

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This paper cites Machine Learning-guided Lipid Nanoparticle Design for mRNA Delivery.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Machine Learning-guided Lipid Nanoparticle Design for mRNA Delivery

Reference 9

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This paper cites Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions

Reference 10

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This paper cites Visual feature extraction by a multilayered network of analog threshold elements.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Visual feature extraction by a multilayered network of analog threshold elements

Reference 11

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This paper cites The Synthesizability of Molecules Proposed by Generative Models.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach The Synthesizability of Molecules Proposed by Generative Models

Reference 12

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This paper cites Grygorenko, Dmytro S.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Grygorenko, Dmytro S

Reference 13

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This paper cites Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D

Reference 14

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This paper cites A multidimensional approach to modulating ionizable lipids for high-performing and organ-selective mrna delivery.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach A multidimensional approach to modulating ionizable lipids for high-performing and organ-selective mrna delivery

Reference 15

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This paper cites Huayamares, Melissa P.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Huayamares, Melissa P

Reference 16

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This paper cites Irwin, Khanh G.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Irwin, Khanh G

Reference 17

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This paper cites Hierarchical Generation of Molecular Graphs using Structural Motifs.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Hierarchical Generation of Molecular Graphs using Structural Motifs

Reference 18

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Unresolved cited work

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This paper cites Thiessen, Evan E.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Thiessen, Evan E

Reference 20

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This paper cites Adam: A Method for Stochastic Optimization.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Adam: A Method for Stochastic Optimization

Reference 21

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Bandit based monte-carlo planning

Reference 22

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This paper cites mrna: A promising platform for cancer immunotherapy.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach mrna: A promising platform for cancer immunotherapy

Reference 23

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This paper cites The future of tissue-targeted lipid nanoparticle-mediated nucleic acid delivery.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach The future of tissue-targeted lipid nanoparticle-mediated nucleic acid delivery

Reference 24

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This paper cites Kusner, Brooks Paige, and José Miguel Hernández-Lobato.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Kusner, Brooks Paige, and José Miguel Hernández-Lobato

Reference 25

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This paper cites Combinatorial design of nanoparticles for pulmonary mRNA delivery and genome editing.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Combinatorial design of nanoparticles for pulmonary mRNA delivery and genome editing

Reference 26

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This paper cites Accelerating ionizable lipid discovery for mrna delivery using machine learning and combinatorial chemistry.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Accelerating ionizable lipid discovery for mrna delivery using machine learning and combinatorial chemistry

Reference 27

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This paper cites Johnson, Lukas Farbiak, and Daniel J.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Johnson, Lukas Farbiak, and Daniel J

Reference 28

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This paper cites Predicting molecular conformation via dynamic graph score matching.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Predicting molecular conformation via dynamic graph score matching

Reference 29

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Unresolved cited work

Reference 30

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Re-evaluating retrosynthesis algorithms with syntheseus

Reference 31

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Mitchell, Margaret M

Reference 32

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This paper cites Representations of lipid nanoparticles using large language models for transfection efficiency prediction.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Representations of lipid nanoparticles using large language models for transfection efficiency prediction

Reference 33

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This paper cites Mol G pka: A web server for small molecule pka prediction using a graph-convolutional neural network.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Mol G pka: A web server for small molecule pka prediction using a graph-convolutional neural network

Reference 34

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This paper cites Petrucci, William S.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Petrucci, William S

Reference 35

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits

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This paper cites mRNA -based therapeutics — developing a new class of drugs.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach mRNA -based therapeutics — developing a new class of drugs

Reference 37

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This paper cites Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity

Reference 38

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This paper cites Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Unresolved cited work

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Learning Gradient Fields for Molecular Conformation Generation

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Unresolved cited work

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Mastering the game of go without human knowledge

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This paper cites Graphvae: Towards generation of small graphs using variational autoencoders, 2018.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Graphvae: Towards generation of small graphs using variational autoencoders, 2018

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Stern and Scott E

Reference 45

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Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Alex Brown, Edward A

Reference 46

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This paper cites Reinforcement learning: An introduction.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Reinforcement learning: An introduction

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This paper cites Catacutan, Autumn Arnold, James Zou, and Jonathan M.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Catacutan, Autumn Arnold, James Zou, and Jonathan M

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This paper cites Regularization of neural networks using dropconnect.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Regularization of neural networks using dropconnect

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This paper cites Johnson, Melissa L.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Johnson, Melissa L

Reference 50

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This paper cites Visualizing lipid-formulated siRNA release from endosomes and target gene knockdown.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Visualizing lipid-formulated siRNA release from endosomes and target gene knockdown

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This paper cites Mark Saltzman, and Alexandra S.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Mark Saltzman, and Alexandra S

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This paper cites AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery

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This paper cites Analyzing learned molecular representations for property prediction.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Analyzing learned molecular representations for property prediction

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This paper cites Siegwart.

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach Siegwart

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