199 related articles for article (PubMed ID: 33431037)
1. DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach.
Khemchandani Y; O'Hagan S; Samanta S; Swainston N; Roberts TJ; Bollegala D; Kell DB
J Cheminform; 2020 Sep; 12(1):53. PubMed ID: 33431037
[TBL] [Abstract][Full Text] [Related]
2. Molecule generation toward target protein (SARS-CoV-2) using reinforcement learning-based graph neural network via knowledge graph.
Ranjan A; Kumar H; Kumari D; Anand A; Misra R
Netw Model Anal Health Inform Bioinform; 2023; 12(1):13. PubMed ID: 36627927
[TBL] [Abstract][Full Text] [Related]
3.
Atance SR; Diez JV; Engkvist O; Olsson S; Mercado R
J Chem Inf Model; 2022 Oct; 62(20):4863-4872. PubMed ID: 36219571
[TBL] [Abstract][Full Text] [Related]
4. Network-principled deep generative models for designing drug combinations as graph sets.
Karimi M; Hasanzadeh A; Shen Y
Bioinformatics; 2020 Jul; 36(Suppl_1):i445-i454. PubMed ID: 32657357
[TBL] [Abstract][Full Text] [Related]
5. MoleGuLAR: Molecule Generation Using Reinforcement Learning with Alternating Rewards.
Goel M; Raghunathan S; Laghuvarapu S; Priyakumar UD
J Chem Inf Model; 2021 Dec; 61(12):5815-5826. PubMed ID: 34866384
[TBL] [Abstract][Full Text] [Related]
6. FSM-DDTR: End-to-end feedback strategy for multi-objective De Novo drug design using transformers.
Monteiro NRC; Pereira TO; Machado ACD; Oliveira JL; Abbasi M; Arrais JP
Comput Biol Med; 2023 Sep; 164():107285. PubMed ID: 37557054
[TBL] [Abstract][Full Text] [Related]
7. De novo drug design based on Stack-RNN with multi-objective reward-weighted sum and reinforcement learning.
Hu P; Zou J; Yu J; Shi S
J Mol Model; 2023 Mar; 29(4):121. PubMed ID: 36991180
[TBL] [Abstract][Full Text] [Related]
8. DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology.
Liu X; Ye K; van Vlijmen HWT; Emmerich MTM; IJzerman AP; van Westen GJP
J Cheminform; 2021 Nov; 13(1):85. PubMed ID: 34772471
[TBL] [Abstract][Full Text] [Related]
9. GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning.
Zhang H; Huang J; Xie J; Huang W; Yang Y; Xu M; Lei J; Chen H
J Chem Inf Model; 2024 Feb; 64(3):666-676. PubMed ID: 38241022
[TBL] [Abstract][Full Text] [Related]
10. MedGAN: optimized generative adversarial network with graph convolutional networks for novel molecule design.
Macedo B; Ribeiro Vaz I; Taveira Gomes T
Sci Rep; 2024 Jan; 14(1):1212. PubMed ID: 38216614
[TBL] [Abstract][Full Text] [Related]
11. Multi-objective de novo drug design with conditional graph generative model.
Li Y; Zhang L; Liu Z
J Cheminform; 2018 Jul; 10(1):33. PubMed ID: 30043127
[TBL] [Abstract][Full Text] [Related]
12. De novo drug design by iterative multiobjective deep reinforcement learning with graph-based molecular quality assessment.
Fang Y; Pan X; Shen HB
Bioinformatics; 2023 Apr; 39(4):. PubMed ID: 36961341
[TBL] [Abstract][Full Text] [Related]
13. Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning.
Xu X; Zhou J; Zhu C; Zhan Q; Li Z; Zhang R; Wang Y; Liao X; Gao X
F1000Res; 2023; 12():757. PubMed ID: 38434657
[TBL] [Abstract][Full Text] [Related]
14.
Staker J; Marshall K; Leswing K; Robertson T; Halls MD; Goldberg A; Morisato T; Maeshima H; Ando T; Arai H; Sasago M; Fujii E; Matsuzawa NN
J Phys Chem A; 2022 Sep; 126(34):5837-5852. PubMed ID: 35984470
[TBL] [Abstract][Full Text] [Related]
15. Strategies for Design of Molecular Structures with a Desired Pharmacophore Using Deep Reinforcement Learning.
Yoshimori A; Kawasaki E; Kanai C; Tasaka T
Chem Pharm Bull (Tokyo); 2020; 68(3):227-233. PubMed ID: 32115529
[TBL] [Abstract][Full Text] [Related]
16. De novo generation of dual-target ligands using adversarial training and reinforcement learning.
Lu F; Li M; Min X; Li C; Zeng X
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34410338
[TBL] [Abstract][Full Text] [Related]
17. Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds.
Korshunova M; Huang N; Capuzzi S; Radchenko DS; Savych O; Moroz YS; Wells CI; Willson TM; Tropsha A; Isayev O
Commun Chem; 2022 Oct; 5(1):129. PubMed ID: 36697952
[TBL] [Abstract][Full Text] [Related]
18. Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation.
Xiong Y; Wang Y; Wang Y; Li C; Yusong P; Wu J; Wang Y; Gu L; Butch CJ
J Comput Aided Mol Des; 2023 Nov; 37(11):507-517. PubMed ID: 37550462
[TBL] [Abstract][Full Text] [Related]
19. Deep reinforcement learning for de novo drug design.
Popova M; Isayev O; Tropsha A
Sci Adv; 2018 Jul; 4(7):eaap7885. PubMed ID: 30050984
[TBL] [Abstract][Full Text] [Related]
20. Multi-Agent Decision-Making Modes in Uncertain Interactive Traffic Scenarios via Graph Convolution-Based Deep Reinforcement Learning.
Gao X; Li X; Liu Q; Li Z; Yang F; Luan T
Sensors (Basel); 2022 Jun; 22(12):. PubMed ID: 35746364
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]