An important practical problem plus an attractive technical formulation generate an exciting research discipline, with funding, journals, conferences, and the appearance of interesting incremental results. Usually this is all wasted because fundamental assumptions are false.
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Digging into this, I was probably briefly the world expert in understanding solvent effects in predicting intermolecular binding affinity, which was saying very little indeed. I only realized that I knew almost nothing, whereas most other people in the field somehow didn’t.
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Major opportunity for science culture reform: insist that the foundational assumptions of each field be made explicit and tested, instead of glossed over. Many-to-most fields will have to just start over when this happens. Some branches of psychology are doing this now—hooray!
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We should teach the skills for locating the hidden foundational assumptions of a field. Often these were explicit originally, then buried under various rhetorical devices that direct attention away. On the origins and obscuration of AI’s assumptions: https://www.gridspinoza.net/sites/default/files/2016-07/Toward%20a%20Critical%20Technical%20Practice%3A%20Lessons%20Learned%20in%20Trying%20to%20Reform%20AI%20Philip%20E.%20Agre.pdf …
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Same! But for me it was just about a year. 2003. Really threw myself at the water ligand van der waals thing
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similar boat…did "drug design" in 90s…left & looked into it again 20 years later…little new science/ideas (just faster computers)
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To clarify, that is the fundamental assumption of *computational* structure-based drug discovery. Drug discovery based on crystal structures is on significantly firmer ground -not a panacea, but many examples of crystal structures leading to new drugs.
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I spent 5 years automating fault tree generation before I understood that the technique made fundamental (and incorrect) assumptions about what was relevant and irrelevant in accident causation.
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