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   https://doi.org/10.1126/science.adj5957  

   Mitchell, Melanie (July 2023, Science)

   In 1967, Marvin Minksy, a founder of the field of artificial intelligence (AI), made a bold prediction: “Within a generation…the problem of creating ‘artificial intelligence’ will be substantially solved.” Assuming that a generation is about 30 years, Minsky was clearly overoptimistic. But now, nearly two generations later, how close are we to the original goal of human-level (or greater) intelligence in machines? 

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2. How We Code

   https://doi.org/https://doi.org/10.1007/978-3-030-67788-6_5  

   Shaffer, David W.; Ruis, Andrew R. (January 2021, Advances in Quantitative Ethnography: Second International Conference, ICQE 2020, Malibu, CA, USA, February 1-3, 2021, Proceedings)

   Ruis, Andrew R.; Lee, Seung B. (Ed.)

   Coding data—defining concepts and identifying where they occur in data—is a critical aspect of qualitative data analysis, and especially so in quantitative ethnography. Coding is a central process for creating meaning from data, and while much has been written about coding methods and theory, relatively little has been written about what constitutes best practices for fair and valid coding, what justifies those practices, and how to implement them. In this paper, our goal is not to address these issues comprehensively, but to provide guidelines for good coding practice and to highlight some of the issues and key questions that quantitative ethnographers and other researchers should consider when coding data. 

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3. How mammals prevailed

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4. Big data of tree species distributions: how big and how good?

   https://doi.org/DOI 10.1186/s40663-017-0120-0  

   Josep M. Serra-Diaz, Brian J. (January 2017, Forest Ecosystems)
5. Conformer Generation for Structure-Based Drug Design: How Many and How Good?

   https://doi.org/10.1021/acs.jcim.3c01245  

   McNutt, Andrew T.; Bisiriyu, Fatimah; Song, Sophia; Vyas, Ananya; Hutchison, Geoffrey R.; Koes, David Ryan (November 2023, Journal of Chemical Information and Modeling)

   Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is fundamental to structure-based drug design. Conformational ensembles are required for rigid-body matching algorithms, such as shape-based or pharmacophore approaches, and even methods that treat the ligand flexibly, such as docking, are dependent on the quality of the provided conformations due to not sampling all degrees of freedom (e.g., only sampling torsions). Here, we empirically elucidate some general principles about the size, diversity, and quality of the conformational ensembles needed to get the best performance in common structure-based drug discovery tasks. In many cases, our findings may parallel “common knowledge” well-known to practitioners of the field. Nonetheless, we feel that it is valuable to quantify these conformational effects while reproducing and expanding upon previous studies. Specifically, we investigate the performance of a state-of-the-art generative deep learning approach versus a more classical geometry-based approach, the effect of energy minimization as a postprocessing step, the effect of ensemble size (maximum number of conformers), and construction (filtering by root-mean-square deviation for diversity) and how these choices influence the ability to recapitulate bioactive conformations and perform pharmacophore screening and molecular docking. 

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