67220cf005e00.pdf
DOI:
Mavjud emas
Karina Martinez-Mayorga, Abraham Madariaga-Mazon, José L. Medina-Franco Gerald MaggioraThe impact of chemoinformatics on drug discovery in the pharmaceutical industry Expert Opinion on Drug Discovery, 15:3, 293-306, https://doi.org/10.1080/17460441.2020.1696307
Lo Y-C, Rensi SE, Torng W, et al. Machine learning in chemoinformatics and drug discovery. Drug Discov Today. 2018;23 (8):1538–1546
Kristina Edfeldt, Aled M. Edwards, Ola Engkvist, Judith Günther, Matthew Hartley et all A data science roadmap for open science organizations engaged in early-stage drug discovery Nature Communications | (2024) 15:5640 https://doi.org/10.1038/s41467-024-49777-x
Carter, A. J. et al. Target 2035: probing the human proteome. Drug Discov. Today 24, 2111–2115 (2019).
For chemists, the AI revolution has yet to happen. Nature 617,438 (2023).
Mammoliti, A. et al. Orchestrating and sharing large multimodal data for transparent and reproducible research. Nat. Commun. 12,5797 (2021).
Guinney, J. & Saez-Rodriguez, J. Alternative models for sharing confidential biomedical data. Nat. Biotechnol. 36, 391–392 (2018)
Goodfellow, I. J., Shlens, J. & Szegedy, C. Explaining and Harnessing Adversarial Examples. Preprint at (2014) https://doi.org/10.48550/ARXIV.1412.6572
A. Rollins, Alan C. Cheng, Essam Metwally MolPROP: Molecular Property prediction with multimodal language and graph fusion Journal of Cheminformatics (2024) 16:56 https://doi.org/10.1186/s13321-024-00846-9
Ahmad W, Simon E, Chithrananda S, Grand G, Ramsundar B (2022) Chem-BERTa-2: towards chemical foundation models. arXiv. https://doi.org/10.48550/arXiv.2209.01712
Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. arXiv. https://arxiv. org/ abs/ 1609. 02907
sBrody S, Alon U, Yahav E (2022) How attentive are graph attention networks? arXiv. https://arxiv.org/abs/2105.14491
Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, Leswing K, Pande V (2018) MoleculeNet: a benchmark for molecular machine learning. Chem Sci 9(2):513–530. https://doi.org/10.1039/C7SC02664A
Falkner S, Klein A, Hutter F (2018) BOHB: robust and efficient hyperparameter optimization at scale. arXiv. https://doi.org/10.48550/arXiv. 1807.01774
Yang K, Swanson K, Jin W, Coley C, Eiden P, Gao H, Guzman-Perez A, Hopper T, Kelley B, Mathea M, Palmer A, Settels V, Jaakkola T, Jensen K, Barzilay R (2019) Analyzing learned molecular representations for property prediction.J Chem Inform Model 59(8):3370–3388. https://doi.org/10.1021/acs.jcim.9b002 37
Wang Y, Wang J, Cao Z, Barati Farimani A (2022) Molecular contrastive learning of representations via graph neural networks. Nat Mach Intell 4(3):279–287. https://doi.org/10.1038/s42256-022-00447-x
David Weininger. 1988. SMILES, a chemical language and information system. 1.Introduction to methodology and encoding rules. Journal of chemical information and computer sciences 28, 1 (1988), 31–36.