It's focused on structural analysis right now, but the goal is to allow for biologists, crystallographers, chemists, etc to quickly analyze large samples of structural data for patterns and find where those patterns break down.
Our goal is to make it a platform to analyze the output of various papers, tools, and structures to build a single unified biological model of your druggable target. For example, what if your alphafold output disagrees with pre-existing literature? If Diffdock says your candidate can bind to a pocket on a protein that hasn't been validated yet, what's the implications of that on the underlying biological mechanism?
Biology is extremely complicated, so scientists create simplified lenses of the world to make sense of things. Biologists are looking at different things than crystallographers, crystallographers are looking at different things than computational chemists, etc, etc.
Finding disagreements in these simplified lenses early can save a lot of money before things move to lab experiments.
I really don't think the analysis here is that credible. People aren't leaving existing SaaS for AI agentic platforms like this article implies. Switching costs are too high even outside of tech, the problem runs deeper than that.
Most of these organizations are looking for customizations that B2B SaaS struggles to provide since they have to walk a line of catering to a market segment broadly then building customization for specific clients.
I've seen a huge surge in organizations investing in small software development teams to do internal builds for things that they just aren't getting from these tools. Technology is not the value center for these companies.
I work in healthcare, so my perspective is heavily contextualized by that, but I'm seeing providers (especially specialty providers) build internal engineering teams to create ancillary systems that sit on top of their EHR. They are doing this instead of buying similar modules that might be up sold by the EHR.
Anyway, I just feel like these market trends are deeper than what this article implies.
FWIW, building your own tools/workflows on top of a standard software typically dramatically increase the lock-in in my experience.
It is typically non-trivial to port this functionality to a new system.
So it would be in favor of existing SaaS vendors.
I'm getting flashbacks from my computer engineering curriculum. Probably the first place I'd start is replacing comparison operators on the ALU with binary arithmetic since it's much faster than branch logic. Next would probably be changing the `step` function from brute iterators on the instructions to something closer to a Btree? Then maybe a sparse set for the memory management if we're going to do a lot of iterations over the flat memory like this.
The only time FPGAs / ASICS are better is if there's gains we can make by innovating on the hardware architecture itself. That's pretty hard to do considering GPUs are already heavily optimized for this use case.
Great write up, we're working on a drug discovery CAD tool and MD has been one of our focal points. Extremely challenging and fun problem to work on!
What complicates things is the experimental data we get back from labs to validate MD behavior is extremely tricky to work with. Most of what we're working with is NMR data which shows flexibility in areas of the proteins, but even then we're left with these mathematical models to attempt to "make sense" of the flexibility and infer dynamics from that. Sometimes it feels like an art and a science trying to get meaningful insights for lab data like this.
It's extremely difficult to experimentally verify any MD model since, as mentioned in the article, most of the data we're working with are static mugshots in the form of crystal structures.
There are also techniques that combine both. In my experience (as an experimental structural biologist working in drug design), they frequently disagree.
That's so cool! What's the software like, compared to say, PyMol? Is it like PyMol, integrated with docking? Are you using MD to position the drugs instead of trying different combos, like Vina does?
hello, I have an undergrad degree in computer science and I'm trying to reach myself informatics to get into this field. do you have any tips, or perhaps an internship available?
if you can reach out at all, you can find me at [masterfully dot blundered] on the normal g-domain. I briefly skimmed your profile for contact info but could not find any.
>The increased amounts of calcium, phosphate, and fluoride in the drinks limited the severity of erosion by changing the solubility of the enamel [82]. The decline in enamel’s surface microhardness and mineral loss were both dramatically halted by the addition of CaGP to the carbonated drinks.
Most seltzer water has fluoride in it, and your tap water has fluoride in it (if you're making your own at home).
Also the methodology in this study was purely in vitro, not real world conditions. Notably, the lack of saliva.
>Under normal circumstances, human saliva forms a physical barrier, a film, and prevents direct contact between the tooth enamel surface and acidic beverages, thus protecting teeth from erosive attack by acids [45,84,85,86]. However, the erosion tests were carried out without saliva
Also, seems like the study was more on soft drinks in particular and not "other acidic drinks" which may include seltzer water.
>Soft drink consumption during meals was linked to mild to severe tooth damage [65]. No matter when they were consumed, other acidic meals and beverages were not linked to tooth damage [40].
Anyway, net is this: I'm not saying sparkling water carries absolutely no risk, but linking a study like this and cherry picking quotes to make it sound like sparkling water is going to destroy your teeth is misleading.
If drinking sparkling water helps you kick your soda habit, please definitely make the switch. It's so much better for you. The increased risk from drinking sparkling water compared to still water is not worth worrying about if sparkling water provides a quality of life increase for you.
Everything in healthcare is about moderated risk and counterbalancing it against lifestyle.
Same! I run the engineering department for a medium sized software consultancy, we're on contract with several large enterprises. In terms of output, we run circles around their internal delivery teams. Not because our engineers are better than them, we've just fully embraced shape up as our development process.
This has also let us bid on projects as fixed rate instead of T&M since we budget time as appetite instead of how long we think something will take to build.
There are still some hurdles we have to overcome, like we can still run into unexpected things that we didn't know about during the betting process, but deep behind the philosophy of Shape Up it feels like something that translates extremely well to more creative R&D development projects like what we do.
