TLDR? "The Bitter Lesson of AI-driven drug discovery is that methods that primarily focus on raising the quantity of hypotheses available to be pursued are bound to not be transformative for drug discovery"
BinaryIgor · 6h ago
And this nugget:
"Unfortunately, working on hypothesis generation remains extremely attractive to AI in drug discovery researchers: (1) day-to-day generating more and more ideas subjectively feels like progress is being made - after all, you’ve created an enormous and ever-growing database of almost-medicines - and (2) science has trained researchers to value invention over doing the hard work to validate ideas, leading to a world in which everyone wants to be the idea-person and comparatively almost no-one wants to be doing the hard and unrewarding work to take these ideas all the way. And so, AI-driven drug discovery researchers still largely pursue hypothesis-generation machines and consequently, as a field, we unfortunately continue to make the same mistakes."
"Unfortunately, working on hypothesis generation remains extremely attractive to AI in drug discovery researchers: (1) day-to-day generating more and more ideas subjectively feels like progress is being made - after all, you’ve created an enormous and ever-growing database of almost-medicines - and (2) science has trained researchers to value invention over doing the hard work to validate ideas, leading to a world in which everyone wants to be the idea-person and comparatively almost no-one wants to be doing the hard and unrewarding work to take these ideas all the way. And so, AI-driven drug discovery researchers still largely pursue hypothesis-generation machines and consequently, as a field, we unfortunately continue to make the same mistakes."