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.
If you take a problem like the one of NK cells and cancer immunity against them there will be cases where the machinery that NK cells have by human nature won’t be enough and you need to design new proteins for them to properly recognize the cancer cells.
You won’t get there by just licensing some protein folding technology from Google.
The sheer amount of compute involved puts it out of the reach of nearly everyone else, plus pharma companies would have found it hard to hire away the caliber of ML talent DeepMind attracts.
Compute and programmers are something you can hire. If the big pharma companies would be functional, it would be appropriate for each of them to spend a billion per year on AI.
A few years ago I spoke a few times with someone doing new business development at Pfizer. According to his perspective Pfizer is too bureaucratic to effective develop software.
It might be that new biotech companies that can actually manage to employ programmers in a productive way without stiffling them with too much bureaucracy will win out.
While not in the cancer area Proteon Pharmaceuticals would be a company where bioinformatics is at the core of their phage therapy product. As they grow and slowly push out most of the antibiotics from the market they have a need for more IT investment to better simulate how changes in phages and hosts interact and which mutations have which effects.
It might be that after they have a really good system to simulate phages, they go and reapply their knowledge to NK cells.
A company like Moderna or BioNTech that focus on cancer vaccines might also make major IT investments to optimize target selection and fully simulate the cancer cells to know which antigens they actually present.
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.
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.
If you take a problem like the one of NK cells and cancer immunity against them there will be cases where the machinery that NK cells have by human nature won’t be enough and you need to design new proteins for them to properly recognize the cancer cells.
You won’t get there by just licensing some protein folding technology from Google.
Compute and programmers are something you can hire. If the big pharma companies would be functional, it would be appropriate for each of them to spend a billion per year on AI.
A few years ago I spoke a few times with someone doing new business development at Pfizer. According to his perspective Pfizer is too bureaucratic to effective develop software.
It might be that new biotech companies that can actually manage to employ programmers in a productive way without stiffling them with too much bureaucracy will win out.
While not in the cancer area Proteon Pharmaceuticals would be a company where bioinformatics is at the core of their phage therapy product. As they grow and slowly push out most of the antibiotics from the market they have a need for more IT investment to better simulate how changes in phages and hosts interact and which mutations have which effects.
It might be that after they have a really good system to simulate phages, they go and reapply their knowledge to NK cells.
A company like Moderna or BioNTech that focus on cancer vaccines might also make major IT investments to optimize target selection and fully simulate the cancer cells to know which antigens they actually present.
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.