Here is a concept of how the human brain mechanizes all its information functions for the rest of the nervous and other systems. Clusters of neurons make it possible to have [their] electrical and chemical signals in sets. Some clusters may have one set, some may have several sets, but electrical and chemical signals decide to function in sets, in clusters of neurons. It is these respective sets [of signals] that hold or organize information to define—and carry out—functions.

Simply, for any function that the brain does, electrical signals [in a set] have to interact with chemical signals [in a set] to make the function possible, conceptually. Functions are categorized as memory, feelings, regulation of internal signals, and emotions. There are several subdivisions of these functions.

When a set of electrical signals [ions] relay, they hold a summary of functional information, which they deliver to a set of chemical signals [molecules], as a match. This delivery [interaction] does two things. It gives, and it tries to fit, then takes. Simply, sets of chemical signals have configurations that specify functions, differentiating one from the next. But these configurations are not often [readily] available, and may sometimes be incomplete, conceptually.

It is when a set of electrical signals arrive to interact that they deliver what they have, then strike [or nudge] for completion for what the set of chemical signals hold, before obtaining a summary of the configuration and relay again. Electrical signals have sequences of relays, old and new, conceptually, so they often know where to find a fit [for an input] rather than distribute everywhere—most times.

[Neuroscience has established transduction as the "translation of a sensory signal to an electrical signal in the nervous system." This implies that electrical signals convey information.]

Electrical signals, in a set, also split, conceptually, with some going ahead of others to interact with chemical signals, to find a primary fit and obtain configurations, before moving forward. This early-split results in initial perceptions, explaining what is termed predictive coding, processing, and prediction error.

Electrical signals split as a feedback as well, but forward feedback, not backward. Since the incoming electrical signals may go in the same direction as the initial or in another direction—if the input does not match, correcting the "error."

This concept advances from the view that neurotransmitters are simply triggered after an action potential. The concept posits that rather than just triggering neurotransmitters, ions, in a set, relay to deliver what they bear, then strike at an about-to-be-ready configuration of molecules into completion, then take [a summary of the formation] from them again, for departure.

Configurations of molecules are their formation—or how they have to assemble—to define functions. Configurations are made possible, conceptually, by the collective effort of synaptic vesicles, receptors, and clearance enzymes, such that what is available, as chemical signals, is what matters to functions. So, those factors shape their availability and assembly for configurations in all clusters of nerve cells.

Why is this plausible? This is the first concept that proposes why neural clusters, groups, or populations define specific functions because the set of [electrical and chemical] signals make those clusters definitive. It also explains that there are several qualifiers of functions that grade their extents. For example, split is a grader, so are sequences.

Chemical signals also have their own graders, like variations in volume from one end of the set to the other. This volume variation defines subjectivity. There is also a maximum possible volume that defines prioritization—or attention—for any set among the whole, conceptually.

Intentionality is a grader as well because there are spaces of constant diameter in some sets of chemical signals. It is these spaces that become the pivot for anything that can be controlled—including speech. Though, non-intent may override intent if the spaces are operated due to certain volume variations, for pre-/prioritization or subjectivity.

Simply, there are functions and graders of those functions, defined by the coordination of electrical and chemical signals, conceptually.

Medications: Side-Effects

Conceptually, for all the medications that have an effect on the brain, they affect a balance of the functions and features of electrical and chemical signals across sets. This means that if there is a volume—or configuration—of molecules for a function, they might increase it or reduce it by actions towards one—more than the others, and so forth. This may affect features like prioritization. They may also affect what electrical signals obtain when they leave as well as what they meet [to complete] when they arrive. Some medications that act on ions may shape what is available in sets.

While the medications, especially psychoactive, may be effective against some symptoms, they may result in side-effects, since some chemical signals [or others like receptors, and so on], that they target, are present across sets.

So, using this concept, how can side effects of medications be mapped, such that as drug development and solutions proceed it is possible to prepare against side effects in ways that would be helpful?

It is established in neuroscience that a mechanism of Ozempic is correlated with preproglucagon neurons [PPG] that work with glucagon-like peptide-1 [GLP-1]. This means a set of neurons [PPG] have a closer specificity of the function, even though GLP-1 receptors are present elsewhere. [Ozempic Quiets Food Noise in the Brain—But How?]

Using the concept, this indicates that [sets of] chemical signals [which are not just neurotransmitters, but inclusive of neuropeptides, neurohormones and others], have a configuration to specify what they do, getting information from [sets of] electrical signals—and distributing information forward.

Whatever side-effects that Ozempic is said to have, how can this concept be used to roughly plot functional and qualifier shifts, such that it is possible to predict and lessen those in future?

One of the most important solutions that AI can do now is not just for drug discovery, but to explore distributions [of the configurations] of functions across the nervous system to understand where effects might emanate. If the regular configuration of a set is say something alphanumeric, when the side-effects of a medication occur, what changes in that set and how does it affect others? How can this define addictions as well?

AlphaFold : AlphaProteo

Google DeepMind has a project to discover protein structures, which would be useful to curing diseases. One of the areas where solutions are necessary is psychiatry, where serious mental illnesses like schizophrenia, bipolar disorder, major depression, generalized anxiety, and several others can be debilitating.

There are existing psychiatric medications with awful side effects that answers are sought around them. This means that it is not just important to extricate protein structures but to mitigate side-effects as a whole.

Also, while molecular interactions in the brain have been proposed to be responsible for memory longevity [KIBRA anchoring the action of PKMζ maintains the persistence of memory], brain functions and balance are theorized to be the operations of ions and molecules in sets, including how they relay—which is particularly vital.

Modeling for side-effects is an immediate need for AI. Also, there are conditions that AlphaFold: AlphaProteo may want to solve that the theory in the lead would be more useful to the solution than just having molecular interactions. For example, while Google Connectomics is progress, there is no novel theory behind it, and cannot be applied yet to solving medication side-effects in psychiatry. [6 incredible images of the human brain built with the help of Google's AI]

Cellular and molecular neuroscience have not been able to provide enough answers to biological psychiatry because of side-effects. Some patients have resisted psychiatric medications due to side-effects. Exploring a major project with AI to model side-effects using sets of electrical and chemical signals could become decisive in making a major leap forward in global psychiatry.

There is a recent report on CBS News, Weight loss drugs allegedly landed this woman in the hospital, prompting lawsuit about drug label warnings, stating that, "Millions of Americans have turned to prescription medications to lose weight and treat diabetes. But do drug labels warn enough about potential side effects? She's now suing prescription drugmaker Novo Nordisk, claiming its drug labels do not adequately warn patients and doctors about potentially serious side effects, including gastroparesis or stomach paralysis, and bowel obstruction. Novo Nordisk also said semaglutide, the drug sold under the Ozempic and Wegovy brand names, has been extensively examined in robust clinical development programs and large real world evidence studies."

There is a recent blog by Google DeepMind, AlphaProteo generates novel proteins for biology and health research, stating that, "We introduce AlphaProteo, our first AI system for designing novel, high-strength protein binders to serve as building blocks for biological and health research. This technology has the potential to accelerate our understanding of biological processes, and aid the discovery of new drugs, the development of biosensors and more. AlphaProteo can generate new protein binders for diverse target proteins, including VEGF-A, which is associated with cancer and complications from diabetes. This is the first time an AI tool has been able to design a successful protein binder for VEGF-A. AlphaProteo also achieves higher experimental success rates and 3 to 300 times better binding affinities than the best existing methods on seven target proteins we tested."

There is a blog on Google, AlphaFold 3 predicts the structure and interactions of all of life’s molecules, stating that, "Introducing AlphaFold 3, a new AI model developed by Google DeepMind and Isomorphic Labs. By accurately predicting the structure of proteins, DNA, RNA, ligands and more, and how they interact, we hope it will transform our understanding of the biological world and drug discovery."