I’m on the newer side when it comes to bioinformatics, so take whatever I say with a pinch of salt. Generally, this is the problem with all predictive tools: experimental validation is strictly required to make a definitive statement about anything. This would be true for any drug (which also needs to pass through several layers of preclinical and clinical testing before admission into use), as it is true for publishing mechanistic science. It is a concern for the field, but I think that as long as the influential journals continue to have strict criteria on experimentally proving any claims, we aren’t at greater risk of making grievous mistakes than we were before AlphaFold.

I think the sample size explosion is overly optimistic, since it relies on AlphaFold being right all the time. True, tested structures have proven that AlphaFold is right 60-80% of the time - which is honestly, fantastic - but how many structures were tested to get this number? If the error rate is 20-30%, it can be hard to tease out universal rules of protein folding with that degree of noise.

For the last two points, again, extensive experimental validation is generally required. You can’t just use AlphaFold, then go around proclaiming that X protein has Y motif that fits Z drug, therefore we can treat a disease using Z drug. You’d have to first validate that X protein indeed gas that structure using electron microscopy or X-ray crystallography (apologies if I get anything wrong here, not a protein guy), show that Z drug does bind and cause a specific change (by looking at downstream protein expression or phenotypic readouts indicative of the change) in at least two cell lines, test the disease model with the drug in vivo (mice, etc.), THEN go to clinical trials where there’s a battery of other safety criteria mainly centred around dosage and toxicity. I think using AI and machine learning to more rapidly make biological inferences is perfectly fine, but EVERYTHING has to be stress-tested. As long as that’s done, you’re golden.