Solving protein folding doesn’t only give you the ability to know how existing proteins fold. It also gives you the ability to design new proteins.
I don’t agree with this claim. AlphaFold gives you the ability to calculate how a given amino acid sequence is likely to fold. That is very different from being able to predict an amino acid sequence that performs a specific function or even has a given shape. Small modifications of known shapes or functionalities would be tractable using AlphaFold’s technology, but there are other ways to get that, for example directed evolution. Search in the space of amino acid sequences is possible in principle but even with several orders of magnitude increase in compute the size of the search space still seems intractable to me.
If the big pharma companies would be functional, it would be appropriate for each of them to spend a billion per year on AI.
Isn’t this significantly more than DeepMind spends? I realize increased competition for ML talent would drive up salaries but I just can’t see that kind of budget allocation happening for something that pharma companies don’t consider to be core to their business.
Thanks again for engaging. It’s been fun to see how someone in the Silicon Valley mindset looks at the biopharma landscape.
AlphaFold gives you the ability to calculate how a given amino acid sequence is likely to fold. That is very different from being able to predict an amino acid sequence that performs a specific function or even has a given shape. Small modifications of known shapes or functionalities would be tractable using AlphaFold’s technology, but there are other ways to get that, for example directed evolution.
This is basically why you need more then just licensing the technology from AlphaFold. You actually need to employ a bunch of programmers for a few years. It’s a hard problem but it’s solveable.
Isn’t this significantly more than DeepMind spends? I realize increased competition for ML talent would drive up salaries but I just can’t see that kind of budget allocation happening for something that pharma companies don’t consider to be core to their business.
Tesla is today worth more then the other car makers combined. The legacy car makers largely understood to late that driverless cars are going to be a core part of their business.
It might be that currently the idea of some big pharma companies is to wait for the equivalent of Cruise and then buy it.
Besides understanding what goes on in a cell with computer models and protein design, predicting phase 3 trial outcomes better based on phase 1 data would be another task where a lot of data can be gathered and analysed with machine learning. There’s an incredible amount of profit in prediction phase 3 trial outcomes before doing the trial.
I don’t agree with this claim. AlphaFold gives you the ability to calculate how a given amino acid sequence is likely to fold. That is very different from being able to predict an amino acid sequence that performs a specific function or even has a given shape. Small modifications of known shapes or functionalities would be tractable using AlphaFold’s technology, but there are other ways to get that, for example directed evolution. Search in the space of amino acid sequences is possible in principle but even with several orders of magnitude increase in compute the size of the search space still seems intractable to me.
Isn’t this significantly more than DeepMind spends? I realize increased competition for ML talent would drive up salaries but I just can’t see that kind of budget allocation happening for something that pharma companies don’t consider to be core to their business.
Thanks again for engaging. It’s been fun to see how someone in the Silicon Valley mindset looks at the biopharma landscape.
This is basically why you need more then just licensing the technology from AlphaFold. You actually need to employ a bunch of programmers for a few years. It’s a hard problem but it’s solveable.
Tesla is today worth more then the other car makers combined. The legacy car makers largely understood to late that driverless cars are going to be a core part of their business.
It might be that currently the idea of some big pharma companies is to wait for the equivalent of Cruise and then buy it.
Besides understanding what goes on in a cell with computer models and protein design, predicting phase 3 trial outcomes better based on phase 1 data would be another task where a lot of data can be gathered and analysed with machine learning. There’s an incredible amount of profit in prediction phase 3 trial outcomes before doing the trial.