4 ms·

When I see a paper like this one of my first thoughts is "What will Derek Lowe say about this?" He is a chemist in the drug industry and excellent writer. On hi

When I see a paper like this one of my first thoughts is "What will Derek Lowe say about this?" He is a chemist in the drug industry and excellent writer. On his blog he regularly tears apart overhyped claims for how software searches for targets and automated synthesis of chemicals are going to find perfect cures for everything at the press of a button. His take on this paper is actually positive, though with some important caveats: http://blogs.sciencemag.org/pipeline/archives/2019/02/11/virtual-screening-as-big-as-it-currently-gets http://blogs.sciencemag.org/pipeline/archives/2019/02/11/vir...

higginsc 8y ago
I have been out of this space for a few years (transitioned to data science from drug discovery), but from my time doing in silico and in vitro work, a major issue with docking was rank ordering. His comments are right on the mark IMO. Especially this paragraph: >Another point is that high-middle-low effort on the D4 case. The binding assay results compared to the docking scores are shown at right. You can see that the number of potent compounds (better than 50% displacement, below that dashed line) decreases as the scores get worse; the lowest bin doesn’t have any at all. But at the same time, there are a few false-negative outliers with binding activity at pretty low scores, and at the other end of the scale, the top three bins look basically undistinguishable. So the broad strokes are there, but the details are of course smeared out a bit. These methods can filter millions of compounds down to hundreds, but as an academic lab, it's still a herculean effort to synthesize hundreds of compounds. And out of those hundreds, you might get a couple that are active. This study is a combination hard work, yes, but also a lot of money and luck. That being said, good for the team, and good for science. I have nothing but respect for Shoichet and Roth. Didn't ever cross paths with Irwin.
- cowsandmilk 8y ago
  > I have nothing but respect for Shoichet and Roth. Didn't ever cross paths with Irwin. This makes me laugh since Shoichet was childhood friends with Irwin and they've worked together on almost everything together since 2000 when Irwin went to Northwestern to join his lab.
  - higginsc 8y ago
    That is pretty funny. The more you know. I was just a grad student and met Shoichet at conferences.
  - deleted 8y ago
    [deleted]
cowsandmilk 8y ago
I can tell you one key sentence from his post is "reports what I believe is the largest publicly disclosed effort of this type", where the key words are "publicly disclosed". From talking to pharma companies, they all have done virtual screening with larger libraries than what is reported in this paper. I'm not familiar with details of Novartis's efforts, but based on talks with other companies, I would not be surprised if Derek is familiar with virtual screens at Novartis that have been 1-2 orders of magnitude larger. Many of the questions in virtual screening of large libraries are not answered in the paper as the authors did not look at them. As mentioned in the comments, they are using Enamine RealDB which has been around for a while, and Enamine has an even larger virtual library called RealSpace.
semi-extrinsic 8y ago
One other very interesting takeaway that Derek points out is that humans eyeballing the candidate molecule-protein interaction are somehow better at selecting good candidates than the computer: """ There’s also a human-versus-machine comparison in evaluating the hits. The authors took the top 1,000 compounds and selected 124 of them by eyeballing them for what looked like good interactions in the docking pose (not looking at the scoring), and took 114 molecules on the basis of docking scores alone. The hit rates for the two sets were almost identical (about 24%), but the human-selected ones were disproportionately potent – and indeed, in the two campaigns, the human-selected compounds were quite over-represented in the lists of potent compounds. """

higginsc 8y ago

I have been out of this space for a few years (transitioned to data science from drug discovery), but from my time doing in silico and in vitro work, a major issue with docking was rank ordering. His comments are right on the mark IMO. Especially this paragraph: >Another point is that high-middle-low effort on the D4 case. The binding assay results compared to the docking scores are shown at right. You can see that the number of potent compounds (better than 50% displacement, below that dashed line) decreases as the scores get worse; the lowest bin doesn’t have any at all. But at the same time, there are a few false-negative outliers with binding activity at pretty low scores, and at the other end of the scale, the top three bins look basically undistinguishable. So the broad strokes are there, but the details are of course smeared out a bit. These methods can filter millions of compounds down to hundreds, but as an academic lab, it's still a herculean effort to synthesize hundreds of compounds. And out of those hundreds, you might get a couple that are active. This study is a combination hard work, yes, but also a lot of money and luck. That being said, good for the team, and good for science. I have nothing but respect for Shoichet and Roth. Didn't ever cross paths with Irwin.

cowsandmilk 8y ago
> I have nothing but respect for Shoichet and Roth. Didn't ever cross paths with Irwin. This makes me laugh since Shoichet was childhood friends with Irwin and they've worked together on almost everything together since 2000 when Irwin went to Northwestern to join his lab.
- higginsc 8y ago
  That is pretty funny. The more you know. I was just a grad student and met Shoichet at conferences.
- deleted 8y ago
  [deleted]

cowsandmilk 8y ago

I can tell you one key sentence from his post is "reports what I believe is the largest publicly disclosed effort of this type", where the key words are "publicly disclosed". From talking to pharma companies, they all have done virtual screening with larger libraries than what is reported in this paper. I'm not familiar with details of Novartis's efforts, but based on talks with other companies, I would not be surprised if Derek is familiar with virtual screens at Novartis that have been 1-2 orders of magnitude larger. Many of the questions in virtual screening of large libraries are not answered in the paper as the authors did not look at them. As mentioned in the comments, they are using Enamine RealDB which has been around for a while, and Enamine has an even larger virtual library called RealSpace.

semi-extrinsic 8y ago

One other very interesting takeaway that Derek points out is that humans eyeballing the candidate molecule-protein interaction are somehow better at selecting good candidates than the computer: """ There’s also a human-versus-machine comparison in evaluating the hits. The authors took the top 1,000 compounds and selected 124 of them by eyeballing them for what looked like good interactions in the docking pose (not looking at the scoring), and took 114 molecules on the basis of docking scores alone. The hit rates for the two sets were almost identical (about 24%), but the human-selected ones were disproportionately potent – and indeed, in the two campaigns, the human-selected compounds were quite over-represented in the lists of potent compounds. """