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What does this mean in the context of drug discovery and design?

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It's possible that by studying how artificial "minds" work, insight can be gained into human minds. The way it's done now is the opposite. Researchers study human minds for clues on how they work and use the knowledge to help design artificial minds

No, AI is not some magic that can magically fix everything. Treatments are ineffective because we have no idea how they work. What they typically do in medicine and psychology/psychiatry is that first they label and categorize a disorder, and do all sorts of blind trial-and-error to see what "works". Obviously this is a very inefficient way of doing things. What they need to be doing is to figure out how a disorder works.

>someone without schizophrenia to correctly perceive schizophrenia.

This has little to do with bias, and more to do with the fluid nature and standards we use to diagnose these conditions. If you look at a DSM or similar diagnostic catalog, over the years, you'll notice that the criteria for diagnoses change. This is a result of learning more about the condition - but at the end of the day, there isn't a simple, mechanistic diagnosis for most mental pathologies.

In the case of schizophrenia, we can't say If you have a protein level <X, you have schizophrenia. It comes down to a number of subjective symptoms, and how the patient experiences them. That's not bias, that's simply not having a clear cut definition for a condition.

Combine this with the fact that many conditions that are considered distinct have similar or overlapping symptom profiles, despite having potentially different causes and potentially different responses to therapeutic strategies.

Emotional bias in research is not the biggest problem. The biggest problem is that defining a clear cut mechanistic cause for these conditions is exceptionally difficult.

In this decade.. hm.. not sure about that, but eventually, yes. It's sort of inevitable right? On a macroscopic scale. The more AI advances and automation takes over, the more great minds are free to study the areas AI has yet to discern, until it does. And I believe those areas will be of continued interest for the foreseeable future.

It doesn't.

Before any sort of experimental drug proceeds to clinical trials, it needs to have robust preclinical data showing A) Safety in animal models and B) some functional data suggesting it would have efficacy in humans. Primary research into neurological conditions and mental pathologies is limited by model systems and the measurements we can take.

The most detailed measurements in neuroscience are terminal, invasive procedures. For obvious reasons, we do not do these in humans. So we have to use a animal model - typically a rodent of some sort. But there are enormous species to species differences that make assessing model systems very difficult.

Consider the primary symptoms of schizophrenia in a human; psychosis, delusions, apathy and others. How do you assess if a mouse or rat is actually experiencing auditory hallucinations? By what criteria can you examine a mouse model, and determine if it is undergoing psychosis? If my hypothesis is that psychosis is caused by X deviation in Y protein, I first need to have an accurate animal model for psychosis. Or I could look at the animal first, and then look at humans after, but that has it's own challenges.

The biggest limits on research in mental pathologies is developing accurate mechanistic causes for conditions such as schizophrenia. Once you have a clear idea on what the problem is - protein expression, inappropriate excitability, etc - you can far more accurately develop new therapeutic drugs, or repurpose old drugs.

It has little to do with emotional bias in research, and far more to do with technical limitations in what data we can actually gather.

Wow that’s super disappointing

>What they need to be doing is to figure out how a disorder works.

And the difficulty with this is that the field is sharply limited on what data we can gather from humans. The highly sensitive research techniques looking at protein function, or electrophysiological data or whatever have 2 things in common. A) They are highly invasive. B) The are either terminal procedures, or highly damaging. For obvious reasons, we don't do these in humans. This research happens in animal models, typically rodents.

But that brings us to problem 2: How do you know that your mouse model of psychosis is actually experiencing psychosis? We can't ask a mouse about it's perception on reality.

And then problem 3 happens: Sure, you may have a reasonable mouse model of psychosis, but treating psychosis in 1 species is exceptionally different than treating it in humans. Protein expression is very different, and what works in a mouse does not work in a human.

Medical research is limited by the measurements we can take, and the model systems we use. Biggest advancements will happen when we can mechanistically define a condition rather than looking at the overlap of subjective symptoms, and THAT requires better measurement techniques.

Even then, the actual genetic differences (and hence protein expression) can be scattered all over the genome - only a few conditions are definitely linked to a single bad gene. There are studies which show that there are about ten genes directly implicated, and that it is mostly an inherited condition. Not an easy target for drug discovery, so mostly we put a lid on the symptoms with antipsychotic drugs, which are not fun and have neurological side effects.

Also, you can have the bad genes and not end up inflicted, just be 'functionally eccentric' or even very creative. (Not so uncommon for talented families to have the curse of madness). So, there's environment and epigenetics and all that.

Tlr;dr: the mechanisms are complex, treatment has been palliative.

Well again the problem is thinking that science is about gathering data and doing measurements. That doesn't really help with figuring out how things work.

As long as people use AI as a tool and don't worship AI as a god like I am perceiving them to. AI can potentially solve many of the world's problems but will probably make mankind obsolete.

>Well again the problem is thinking that science is about gathering data and doing measurements.

It literally fucking is?

You propose a hypothesis: EG - I believe that Schizophrenia is caused by changes in X protein, or alterations in Y function in the brain.

You design experiments to test the hypothesis. You gather data to support or disprove that hypothesis: Maybe you look at western blots to see if there are changes in protein expression. Maybe you collect ephys data to see if there are changes in electrical activity due to changes in this protein. Maybe you analyze biomarkers of particular metabolic processes. You analyze that data and determine if there is a significant change between the control condition and experimental (schizophrenic) condition.

>That doesn't really help with figuring out how things work.

How do you think this works? Because if you go to any graduate school or any academic, research, or medical institution and make the argument that science and research isn't about data collection and experimental design, you will be laughed out of the room.

Explain to me precisely how someone establishes a mechanistic pathway WITHOUT gathering data and collecting experimental measurements please. Be as specific as you can.

And how did you exactly come up with that hypothesis? It certainly wasn’t just gathering “data”.

Einstein barely gathered data and instead did a lot of thought experiments to come up with relativity.

>And how did you exactly come up with that hypothesis? It certainly wasn’t just gathering “data”.

You look at previous literature, and previous data? Science is iterative. No one is coming up with completely original ideas that have no grounding in previous experimentation. Either A) You notice something interesting in previous data and design a experiment as an extension of that idea or B) You notice something anomalous in previous experiments, and design experiments to challenge that data.

Every single funded grant has significant preliminary data. Every hypothesis, every question has data to back it, and data is the ONLY way to test a hypothesis.

>Einstein barely gathered data and instead did a lot of thought experiments to come up with relativity.

What relevance does Einstein have to biomedical and life sciences research? You're right of course, if we ignore all the experimentation to validate the theory of relativity, there really isn't a huge emphasis on data gathering.

How do you establish a mechanistic pathway WITHOUT gathering data and collecting experimental measurements? How do you reject or accept a biomedical hypothesis without data? Be as specific as you can.

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And what does that exactly have to do with “data”? If you go all the way down then science started with myths and legends. We thought maybe the earth was flat, maybe the earth was round, etc.

There is not a single scientific theory that contains “data”, because if it did, then by definition it stops being a theory and it just becomes data.

Of course, we test a theory by data, but the theory itself is not data.

Did you not listen to Jack Ma when he interviewed Musk? He intelligently discusses this very topic 4 minutes 44 seconds in.

>There is not a single scientific theory that contains “data”, because if it did, then by definition it stops being a theory and it just becomes data.

You have just proven you don't understand what a "theory" is in science.

The dictionary definition of a Scientific Theory notes specifically that it is an explanation that has been repeatedly and thoroughly tested in accordance with the scientific method. YOU CANNOT HAVE A SCIENTIFIC THEORY WITHOUT DATA CORROBORATING IT. A Scientific Theory, BY DEFINITION, must include data supporting a hypothesis. A Scientific theory without supporting data is not a theory - it is a untested, unsubstantiated hypothesis. The single exception is cases where it is entirely impossible to gather data - which is not a situation that applies to life sciences.

You have a dangerous, fundamental misunderstanding of science.

>Of course, we test a theory by data, but the theory itself is not data.

No, you don't. You test a hypothesis. A theory is something that already has a veritable mountain of supporting evidence in the form of data from experiments. If your theory has no data, 99% of the time it is a hypothesis, not a theory.

If you're going to pedant, at least be accurate.

I'd imagine both, its ultimately just a tool and it depends how it's used. The current best treatment for most psychiatric illnesses is CBT. It's pretty easy to image a chatgpt style CBT app in the future that makes CBT therapy available for free to all.

In reality this probably wont happen and anything like this would still have some ridiculous subscription fees attached to it to keep people priced out.

Yes, it is tested by data, but in no way that a theory contains any data. Nor is it based on any data.

>You have a dangerous, fundamental misunderstanding of science.

Speak for yourself. You are making contradictions because if a theory contained any data, then it ceases to be a theory.

Prove it. Find me a definition of a scientific theory that explicitly excludes data, or a basis in data. I've provided you with plenty of sources. Put your money where your mouth is.

And can you please describe how this (incorrect) emphasis on theory relates to biomedical sciences and establishing mechanistic causes for neurological pathologies?

Can you explain to me specifically how gathering data doesn't help with figuring out how things work? How do you know your hypothesis is correct without data?

I was just using protein changes as an example, since that's what I'm most familiar with.

My main point was that the research techniques we use for establishing mechanisms and pathways are not compatible with humans, since they tend to be very destructive to the individual.

I think there’s a lot of potential for AI to learn to interpret EEG readings of brainwaves, and through interacting with TMS there’s a lot of potential to address the underlying neurological roots of psychiatric disorders (rather than just medicating). It would have to be combined with therapy of course, but there’s a lot of potential especially when combined with heightened neuroplasticity from psychedelics.

How long will it take science to tap into the full potential? That’s a different story…

I think there’s a lot of potential for AI to learn to interpret EEG readings of brainwaves, and through interacting with TMS there’s a lot of potential to address the underlying neurological roots of psychiatric disorders (rather than just medicating). It would have to be combined with therapy of course, but there’s a lot of potential especially when combined with heightened neuroplasticity from psychedelics.

How long will it take science to tap into the full potential? That’s a different story…

That's a good question. Has anyone else mentioned the idea of a chatbot that is actually a therapist?

We'll either create person/gene specific drugs using AI to stop those sickness or we'll just stop altogether. Instead of fixing sickness using drugs we'll just synthesize tissue for a "fresh" start

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No, once the pt realizes a bot is treating them it will probably make their condition worse.

There’s been work using ai to decode brain recordings as patients with depression respond to brain stimulation therapy. It’s not nothing

Absolutely. Look up Megasyn drug development. It’s an AI that can create never before created molecules based on a desired effect on the human body. Unfortunately it can as easily be used new toxins and shit for chemical warfare. Crazy stuff.

This! Some are scared of the creative destruction that will happen with AI. I think as long as we have UBI, humans with focus on solving other problems that we didn't have time to before. And mich more efficiently.

You mean will a computer be developed that can problem solve things that are not set in there parameters. That type of intelligence.

Exactly. What I suspect, that AI will help us vastly with data aggregation.

As these sample sets grow so to will the insite we have about how these conditions are formed. As such I believe we will alter our definition of these structures to include all the abstract interactions that go into the individual expressing the gene. We will gain better understanding as to the not so obvious, the more nunoncied structures that manufacture these symtomes like anxiety and environment.

We already have highly developed AI chatbots that can be used for multiple things. So I don't see why it WOULDN'T accelerate the treatment of such medical cases.

Hey? No hey cool meme that got me thinking or anything?

I'm curious as to what you've witness that made you think people worship AI?

It may equip doctors with tools that result in a better outcome for treatment of psychiatric disorders. Gotta watch how you say stuff

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The progress will come from advances in genomics and neuroscience and it'll probably take longer than a decade for two reasons. One is that we're starting from a place of almost complete ignorance and the other is that even when we understand how the brain works, it's the most complex system known and it'll be the hardest thing humanity has ever tried to fix. I could see AI improving the efficiency of the discovery process, such as speeding up drug discovery and analyzing data generated by the actual scientists, but it's not going to be the deciding factor. There are very smart people who think it's possible to solve these problems but it's going to take a lot of time unfortunately.

theory
noun.

a formal statement of the rules on which a subject of study is based or of ideas that are suggested to explain a fact or event or, more generally, an opinion or explanation:

economic theory

scientific theory

Darwin's theory of evolution

noun.

something suggested as a reasonable explanation for facts, a condition, or an event, esp. a systematic or scientific explanation:

https://dictionary.cambridge.org/dictionary/english/theory

Darwin's theory of evolution doesn't have any data or basis in data. It's an explanation of data.

Please explain specifically how data collection doesn't help with "figuring out how things work".

I've only asked you several times now. Maybe this time, you'll actually defend your stance. Who knows, I'm an optimist.

Or maybe, you're full of shit and have no way of explaining how data analysis somehow doesn't help with "figuring out how things work". I don't think asking someone to defend their own stance is unreasonable, but well, here we are.

Because just collecting data doesn't allow us to find causal connections, or "explanations" for that data. The data just might be a bunch of garbage data.

We need explanations or theories to know what data is relevant in the first place.

That's not what I asked. I'm not saying that hypotheses are not important.

Here is your original statement.

>Well again the problem is thinking that science is about gathering data and doing measurements. That doesn't really help with figuring out how things work.

For this to be true, you must be able to "figure out how things work" without data and measurements.

I'm not the one taking the stance that hypotheses are not important. You are the one taking a stance that data is not important. Defend it.

Explain to me how you validate a hypothesis without any data. Don't use Darwin or Einstein, their contributions were based in explaining previous data, as well as explaining anomalous data. Therefore, you cannot use them as an example here.

Obviously I said "science is about gathering data and doing measurements". Science is about coming up with theories and explanations. Otherwise you might just have a bunch of garbage data and measurements that don't help with anything.

>Obviously I said "science is about gathering data and doing measurements".

Wow, you don't even know what you said.

No, you didn't say that "science is about gathering data and doing measurements".

You specifically said that

>Well again the problem is thinking that science is about gathering data and doing measurements.

This conversation has run it's course. There is no worthwhile discussion to be had with someone that doesn't even understand their own written claims. Just lead with that next time, and save us all some effort.

Can you actually read? I said "Science is about coming up with theories and explanations." after that sentence. Obviously I included the "problem" part in its meaning.

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Sure, you might be able to cure and give way better treatment. But just remember, how long do you want to live for and do you want to accelerate the aging population around the world?