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I worked a bit in computational chemistry (small molecule drug discovery) and currently do some early-stage biotech angel investing. Chemistry is very much on
by lchengify 3y ago
I worked a bit in computational chemistry (small molecule drug discovery) and currently do some early-stage biotech angel investing.
Chemistry is very much on the edge of what is possible with ML / AI because it requires training on hard data, first-principles QM/physics simulation, and finally actual new science that has to be tested whenever the edge of the data is reached.
Modern computational chemistry marries these techniques, effectively operating a search tree from least to most expensive. Picture a huge multi-dimensional game of minesweeper where the board is the entire chemical space for the problem. And to boot, every step is a huge pain for it's own reasons:
- Data is limited, given the (obvious) huge possibilities of chemical space
- Structure data such as the PDB is still one-off captures (x ray crystallography and cryo em), and often don't even capture the molecule pose as it would appear in biology.
- Data is heavily siloed. Data is a big reason biotechs buy other biotechs.
- All your math and chemistry models may say that a Sulfur will do what you think when it's somewhere, but legitimate, publishable new science happens a lot in the practice of discovery. Like a lot.
For those of you interested, I would check out the work pymol, Schrodinger, Chemical Computing Group, and others put out when they have to problem solve for a specific use case. You'd be surprised how much of it mirrors traditional software development when using AI (P/M fit, knowing your user, operational costs, etc). It's just that getting to the actual product is 10x more expensive, and sometimes you stumble on something genuinely undiscovered.
- adastra22 3y agoI'm working on a startup in a related field and would love to get your input/advice. Your HN profile doesn't have any contact method listed. How can I best reach out to you?
- jerven 3y ago> and often don't even capture the molecule pose as it would appear in biology. Mapping between ligands in PDB and cognate ligands as being annotated in UniProt is improving :) my UniProt curator colleagues are working hard on this. Though a lot was made possible by re-annotating all cognate ligands with ChEBI.
- COGlory 3y agoI just wanted to say that I use UniProt nearly every day and it's an absolutely invaluable resource. Didn't expect to find anyone from UniProt here.
- lchengify 3y agoThank you for your work :). It's been a long time since I used the PDB directly but I remember being frustrated about how essential and sparse it could be. I'm curious about your toolchain. Is it just a community going through and manually annotating, or do you have something that helps pick out obvious things that can be fixed using something computationally predictive? If you have links on the UniProt website I can also just read those. Thanks!
- COGlory 3y ago>Structure data such as the PDB is still one-off captures (x ray crystallography and cryo em), and often don't even capture the molecule pose as it would appear in biology. Cryo-EM structures frequently capture dozens or more discrete states of a complex.
- dekhn 3y agoIt may do so but there is still a very open question about whether the structure models represent biologically relevant information.
- COGlory 3y agoNot really. Unlike crystallography, the molecules are well solvated and flash-frozen. Thermodynamically, the range of possible conformations, once folded, is only so big, and structures can often be validated with some other technique. There's really not been many, if any structures proven to be irrelevant.
- lchengify 3y agoFull disclosure: I am not an active researcher and haven't been in a wet lab in a while. Not disagreeing with your statement. IMO Cryo-EM is a huge step above crystallography and captures way more biologically accurate structures on top of being way easier to do. To get a bit more nuanced: I think there's still a significant gap between the captured structure and biological action, and often people believe that the structure is the be-all and end-all to these conclusions. A simple example would be the location of water molecules and how the hydrogen bonds interact with protein active sites. In many cases it's not hard to impute, but it can be tricky and often requires outside techniques. I think the long term solution would be to directly capture structure level precision in motion, similar to looking at a slide from a mouse model or something. AFAIK we're not there yet, even though we can get pretty close by stitching together captured structures with predictive chemistry models.
- COGlory 3y ago