ktamiola

Peptone, a startup using A.I. to find drugs for tricky ‘disordered’ proteins, raises $40 million in venture funding

I do remain skeptical.

That's what I have also thought about this!

Excellent point!

Karma is a bitch.

And yes, the product right now is Keras and Tensorflow database integration + the interactive database interface. The tools around are currently under stringent testing.

This is an excellent question. When we have our network up and running we will compare against Ad's methodology. Ad is known for top-notch solutions.

Thank you for very kind comment. We are now finishing a predictor, which utilizes protein propensity data for mass-scale disorder and order predictions.

The training times obviously vary on the network architecture, software and hardware. I can safely say you can process 7200+ protein sequence with average sequence length of 120 amino acids in 2h on 2 x NVIDIA Titan XP

Excellent reference!

BTW, greetings from GROMACS group in Groningen :) I happend to do my PhD in NMR and Molecular Dynamics.

Coming back to your comments about the canonical secondary structures; I couldn't agree more with you. The problem is quite simple, how are we going to convince the >90% of structural biochemistry society to simply accept the fact proteins are bloody dynamic and X-ray / eye candy structures may have quite little to do with the "real" picture at room temperature?

Cing! Thank you for very flattering comment. Obviously, this is database only paper. Please bare in mind, the vast majority, or perhaps even >95% of protein structure prediction methods deal with canonical secondary structure classes. We want to provide a coherent data set as a benchmark + source of information.

We have in "stock" a network (obviously another paper) that will aim at propensity prediction, still in trivial alpha/coil/beta phase space.

Probably rolling out of laughter. You don't need ML to "predict" properties of molecules. Not a single physicist will but ML predicted molecule properties.

Yet another Arxiv "paper"...

I am curious to see what will happen to Tensor Flow. I hope the code will get clean up... I also hope they will eventually pay somebody to do it, as the open source option clearly generates heterogeneous nightmare.

Nothing to add!

They are for sure! Obviously, all depends on the complexity of the problem and the willingness of programmers to install tests :/

In my company, we are ridiculously pedantic about unit testing, but even with proper level of attention to detail we sometimes fail with getting 100% code coverage.

The biggest pain are log(x)/ln(x) issues in numerical optimization.

Nothing is as easy as both of us wish it was :)

http://www4.ncsu.edu/~jwilson/files/mathsigns.pdf

I had a one more look at the article, and you are right! I need to add an log(10) = 2.303 example

I wouldn't dare to suggest that, yet that's the route that physics and all derivatives have adopted!

We are essentially at the crossroad and obviously, programming, which develops nowadays a bit faster than theoretical physics or mathematics, pushes in one direction.

There you go! It is a freaking zoo of formalism!

Examples? Which examples? Do you mean the snippets of documentation I am attaching at the end? Sure, they do!

The problem is the actual math/physics formalism is not!

https://en.wikipedia.org/wiki/Protein_pKa_calculations

And that's exactly why it is such an annoying problem to spot and debug!

I am afraid I am not. I wish I was, as it would save me ridiculous amount of time and work.

Start reading here: https://en.wikipedia.org/wiki/Protein_pKa_calculations

This is just the tip of the "log(x) vs ln(x)" iceberg.

I have copy pasted the ln(10) straight from Apple Calc. Thanks for pointing out.

Natural sciences are a disaster nowadays. I also see this trend whilst talking to people interested working for my startup.

If you start searching, you will find the data. There is a decent body of work on the attention span issues and cognitive abilities. The most of it in the context of "social media" revolution.

It's not my area of expertise, though. My personal experience at Cambridge and Groningen University was always very good with students. Yet, these are peculiar places.

Lovely stuff!

Excellent news!

and what would it be? ... What have Apple developed that goes beyond currently available peer-reviewed work, done by Deep Mind?

'Teaching highly motivated students to code is actually not that hard.' - Well... It is hard. Motivation is not enough...

Basic mathematical knowledge + ability to think logically = success

Haha! Long live "freedom" of speech!