If you can increase the fidelity of your research model (in-silico, murine, etc) to real life humans then there are fewer false leads.

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moregrist 2 hours ago | parent [-]

Drug design is hard because it’s a system problem not a molecule-protein interaction problem.

At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds.

I’m not sure how wide-spread that is any more; it used a lot of reagents. And it ran into the real bottleneck: lead generation tells you little to nothing about the efficacy, toxicity, and side effects of a drug or biologic in actual humans.

In silicon will never do a better job at lead generation than combinatorial chemistry. It could help with the real bottleneck. But… there just isn’t a lot of high quality training data on this.

They’ve been pushing ML approaches to drug design since at least the early 2010s. The approach has evolved with the trends in ML. Overall, I think the results have been very mixed; much like the traditional pharma pipeline.

	▲	busyant 2 hours ago \| parent [-]
		> At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds. I don't think this was ever the gold standard. I mean, some companies did employ these approaches, but they were generally viewed as low-quality, brute-force approaches (at least at the handful of companies I was associated with).