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Self-driving labs are transforming drug discovery by enabling automated, AI-guided experimentation, but they face challenges in orchestrating complex workflows, integrating diverse instruments and AI models, and managing data efficiently.
Big data in laboratory medicine—fair quality for ai?
Blatter, T.U., Witte, H., Nakas, C.T., Leichtle, A.B., 2022 · 1923
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Artificial intelligence in virtual screening: Models versus experiments
Murugan, N.A., Priya, G.R., Sastry, G.N., Markidis, S., 2022 · 1923
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Drug discovery in pharmaceutical industry: productivity challenges and trends
Khanna, I., 2012 · 2012
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Virtual screening strategies in drug discovery: a critical review
Lavecchia, A., Di Giovanni, C., 2013 · 2013
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Interconnectivity of disparate nonclinical data silos for drug discovery and development
Kasturi, J., Brown, A.P., Brown, P., Madhavan, S., Prabakar, L., Wally, J.L., 2014 · 2014
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Opportunities and challenges in phenotypic drug discovery: an industry perspective
Moffat, J.G., Vincent, F., Lee, J.A., Eder, J., Prunotto, M., 2017 · 2017
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Machine learning in data lake for combining data silos, in: Data Mining and Big Data: Second International Conference, DMBD 2017, Fukuoka, Japan, July 27–August 1, 2017, Proceedings 2, Springer. pp. 294–306
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Bayesian optimization for accelerated drug discovery
Pyzer-Knapp, E.O., 2018 · 2018
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Leveraging big data to transform drug discovery
Glicksberg, B.S., Li, L., Chen, R., Dudley, J., Chen, B., 2019 · 2019
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Overcoming data silos through big data integration
Patel, J., 2019 · 2019
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Importance of scientific collaboration in contemporary drug discovery and development: a detailed network analysis
Cheng, F., Ma, Y., Uzzi, B., Loscalzo, J., 2020 · 2020
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Big data and artificial intelligence modeling for drug discovery
Zhu, H., 2020 · 2020
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Not only in silico drug discovery: Molecular modeling towards in silico drug delivery formulations
Casalini, T., 2021 · 2021
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Data silos are undermining drug development and failing rare disease patients
Denton, N., Molloy, M., Charleston, S., Lipset, C., Hirsch, J., Mulberg, A.E., Howard, P., Marsh, E.D., 2021 · 2021
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Molecular dynamics simulation in drug discovery: opportunities and challenges
Shukla, R., Tripathi, T., 2021 · 2021
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Assessing drug development risk using big data and machine learning
Vergetis, V., Skaltsas, D., Gorgoulis, V.G., Tsirigos, A., 2021 · 2021
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A metadata model to connect isolated data silos and activities of the cae domain, in: International Conference on Advanced Information Systems Engineering, Springer. pp. 213–228
Ziegler, J., Reimann, P., Keller, F., Mitschang, B., 2021 · 2021
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Molecular modeling in drug discovery
Adelusi, T.I., Oyedele, A.Q.K., Boyenle, I.D., Ogunlana, A.T., Adeyemi, R.O., Ukachi, C.D., Idris, M.O., Olaoba, O.T., Adedotun, I.O., Kolawole, O.E., et al., 2022 · 2022
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Artificial intelligence in drug discovery: applications and techniques
Deng, J., Yang, Z., Ojima, I., Samaras, D., Wang, F., 2022 · 2022
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Understanding the impact of binding free energy and kinetics calculations in modern drug discovery
Adediwura, V.A., Koirala, K., Do, H.N., Wang, J., Miao, Y., 2024 · 2024
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Epigenetics is all you need: A transformer to decode chromatin structural compartments from the epigenome
Dodero-Rojas, E., Contessoto, V.G., Fehlis, Y., Mayala, N., Onuchic, J.N., 2024 · 2024
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A data science roadmap for open science organizations engaged in early-stage drug discovery
Edfeldt, K., Edwards, A.M., Engkvist, O., Günther, J., Hartley, M., Hulcoop, D.G., Leach, A.R., Marsden, B.D., Menge, A., Misquitta, L., et al., 2024 · 2024
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Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation
Guo, S.B., Meng, Y., Lin, L., Zhou, Z.Z., Li, H.L., Tian, X.P., Huang, W.J., 2024 · 2024
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Bionemo framework: a modular, high-performance library for ai model development in drug discovery
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Ai-based protein structure prediction in drug discovery: impacts and challenges
Schauperl, M., Denny, R.A., 2022 · 2022
Cited alongside, same era.
A comprehensive survey of prospective structure-based virtual screening for early drug discovery in the past fifteen years
Zhu, H., Zhang, Y., Li, W., Huang, N., 2022 · 2022
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The rise of self-driving labs in chemical and materials sciences
Abolhasani, M., Kumacheva, E., 2023 · 2023
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A fair-decide framework for pharmaceutical r&d: Fair data cost–benefit assessment
Alharbi, E., Skeva, R., Juty, N., Jay, C., Goble, C., 2023 · 2023
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The role of ai in drug discovery: challenges, opportunities, and strategies
Blanco-Gonzalez, A., Cabezon, A., Seco-Gonzalez, A., Conde-Torres, D., Antelo-Riveiro, P., Pineiro, A., Garcia-Fandino, R., 2023 · 2023
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Bayesian optimization in drug discovery, in: High Performance Computing for Drug Discovery and Biomedicine. Springer, pp. 101–136
Colliandre, L., Muller, C., 2023 · 2023
Cited alongside, same era.
Research acceleration in self-driving labs: Technological roadmap toward accelerated materials and molecular discovery
Delgado-Licona, F., Abolhasani, M., 2023 · 2023
Cited alongside, same era.
John, P.S., Lin, D., Binder, P., Greaves, M., Shah, V., John, J.S., Lange, A., Hsu, P., Illango, R., Ramanathan, A., et al., 2024 · 2024
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Artificial intelligence in drug discovery and development
Mak, K.K., Wong, Y.H., Pichika, M.R., 2024 · 2024
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Augmenting dmta using predictive ai modelling at astrazeneca
Marco, G., Evertsson, E., Riley, D.J., Tyrchan, C., Rathi, P.C., 2024 · 2024
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Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects
Obaido, G., Mienye, I.D., Egbelowo, O.F., Emmanuel, I.D., Ogunleye, A., Ogbuokiri, B., Mienye, P., Aruleba, K., 2024 · 2024
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Gtc 2024: Nvidia highlights ai ‘revolution’in drug discovery, genomics
Philippidis, A., 2024 · 2024
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Self-driving laboratories to autonomously navigate the protein fitness landscape
Rapp, J.T., Bremer, B.J., Romero, P.A., 2024 · 2024
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Machine learning in drug discovery: A critical review of applications and challenges
Udegbe, F.C., Ebulue, O.R., Ebulue, C.C., Ekesiobi, C.S., 2024 · 2024
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An artificial intelligence accelerated virtual screening platform for drug discovery
Zhou, G., Rusnac, D.V., Park, H., Canzani, D., Nguyen, H.M., Stewart, L., Bush, M.F., Nguyen, P.T., Wulff, H., Yarov-Yarovoy, V., et al., 2024 · 2024
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Unlocking disease insights to facilitate drug development: Pharmaceutical industry–academia collaborations in inflammation and immunology
Peeva, E., Guttman-Yassky, E., Yamaguchi, Y., Berman, B., Oemar, B., Ramakrishna, J., Fasano, A., Evans-Molina, C., Chu, M., Ungar, B., et al., 2025 · 2025
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Selection of data sets for fairification in drug discovery and development: Which, why, and how?
Alharbi, E., Gadiya, Y., Henderson, D., Zaliani, A., Delfin-Rossaro, A., Cambon-Thomsen, A., Kohler, M., Witt, G., Welter, D., Juty, N., et al., 2022 · 2085
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