upvote

points

by jostmey8 hours ago |
comments
upvoteby pebbly_bread7 hours ago|
next
[-]
If you can increase the fidelity of your research model (in-silico, murine, etc) to real life humans then there are fewer false leads.
reply
upvoteby moregrist7 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.

reply
upvoteby busyant6 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).

reply
upvoteby i0006 hours ago|
prev|
next
[-]
That is not true. For many diseases we lack good hypotheses about the underlying causal mechanisms which resulted in decades and billions being pumped into few questionable targets (e.g. amyloid and alzheimers). In other diseases such as many cancers we often lack "actionable" targets, resulting in hundreds me-too companies all chasing the same target with only subtle variations in approach (inefficient capital alocation).

So AI could help a lot in discovery, much more than the engineering like uses in antibody/drug optimization.

reply
upvoteby 5 hours ago|
prev|
next
[-]
deleted
reply
upvoteby qurren7 hours ago|
prev|
next
[-]
The real problem is generating leads that are more likely to yield positive results than throwing darts at hypotheses.

That's what top-tier scientists are good at.

reply
upvoteby throw3108227 hours ago|
prev|
next
[-]
"The problem was never generating leads" is an absurd statement without any reference to the quality of those leads. Ideally you want a system able to generate one more lead, the correct one.
reply
upvoteby sagentra7 hours ago|
prev|
[-]
[dead]
reply