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Molecular de-novo design through deep reinforcement learning., , , and . J. Cheminformatics, 9 (1): 48:1-48:14 (2017)Randomized SMILES strings improve the quality of molecular generative models., , , , , , , and . J. Cheminformatics, 11 (1): 71:1-71:13 (2019)Accelerated rational PROTAC design via deep learning and molecular simulations., , , , , , , and . Nat. Mac. Intell., 4 (9): 739-748 (September 2022)REINVENT 2.0: An AI Tool for De Novo Drug Design., , , , , , , and . J. Chem. Inf. Model., 60 (12): 5918-5922 (2020)Fatigue Driving Recognition Based on Deep Learning., , and . CAIBDA, page 1-4. VDE / IEEE, (2022)ASIC implementation of an OFDM baseband transceiver for HINOC., , , and . ASICON, page 167-170. IEEE, (2011)On Evaluating Molecular-Docking Methods for Pose Prediction and Enrichment Factors. J. Chem. Inf. Model. 46, 401-415 (2006)., , , , and . Journal of Chemical Information and Modeling, 48 (1): 246 (2008)De Novo Molecule Design Through the Molecular Generative Model Conditioned by 3D Information of Protein Binding Sites., , and . J. Chem. Inf. Model., 61 (7): 3240-3254 (2021)De Novo Molecule Design Using Molecular Generative Models Constrained by Ligand-Protein Interactions., and . J. Chem. Inf. Model., 62 (14): 3291-3306 (2022)3D based generative PROTAC linker design with reinforcement learning., , and . Briefings Bioinform., (September 2023)