NAMPT Activator Slows Frailty


The activator of the NAMPT enzyme, combined with nicotinamide, slowed the progression of frailty in old mice. In a preprint from August 7, a team from Sanford Burnham Prebys described the compound SBI-0802162, which activates the NAMPT enzyme. In human cell culture, it more strongly increased NAD+ levels in senescent cells than in dividing cells, and with prolonged exposure, reduced their viability.


In 18-month-old mice, after 12 weeks of combination with nicotinamide, the frailty index remained stable; in the rotating rod test, the authors saw a strong trend towards better performance than in the control. Senescent cells stop dividing after damage or stress, and the authors sought to find a vulnerability in them through NAD+ metabolism - a molecule involved in energy production, DNA repair, and cell stress response.


When NAD+ is depleted, nicotinamide remains; the NAMPT enzyme triggers its recycling back into NAD+. Isotopic labeling allowed the authors to distinguish between the total NAD+ pool and the rate of its synthesis and consumption. In human connective tissue cell culture, translated into a senescent state by radiation, NAMPT was abundant, although NAD+ was formed and consumed more slowly, indicating that part of NAMPT activity could remain unused. The SBI-0802162 compound activates NAMPT.


For more information, see the study published in Nature Aging, July 2026.

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Lifespan.com Launched


Biologist David Sinclair launched Lifespan.com on August 7, a platform that connects scientific media, community, and research support for aging. The new organization combines Lifespan Magazine, Sinclair's show, educational materials, meetings with scientists, and a membership community, while also supporting the Lifespan Foundation, which aids medical research and aging studies.


Lifespan.com offers readers a consistent route: they can read research analyses in Lifespan Magazine, listen to scientists and ask them questions on the show and at meetings, then join the community and support research through the foundation. The magazine sets the topics for discussion, meetings connect the audience with researchers, and the foundation gathers support for new works.


The magazine starts this chain: the reader first receives a research analysis, then decides which questions to ask the scientists and which works to support. Sinclair explained the reason for the launch as follows: "We are entering a period when discoveries in the biology of aging are coming out of the lab and into everyday life, but public understanding is not keeping up." In the founding article, the magazine team promises to indicate the sources of scientific claims and evaluate advice for their validity, benefits, and connection to data, as published in Lifespan Magazine.

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AI Designs Viruses


The recent work where "AI created a virus" has been surrounded by controversy, but the reality is much more subdued. Yes, AI has indeed learned to design entire bacteriophages, but not for apocalyptic purposes, rather for treating infections that no longer respond to antibiotics. Bacteriophages are not separate genes or small DNA fragments, but complete genomes approximately 5,300 nucleotides in length, with their own structure, regulation, and set of proteins.


The study used two language-based genomic AI models, Evo 1 and Evo 2, which generated thousands of phage variants, after which researchers chemically synthesized almost 300 of them and tested them in the laboratory. As a result, 16 fully viable phages were obtained that infected Escherichia coli, had different replication rates, different structures, and even used proteins not found in the natural prototype φX174. One of the synthetic phages incorporated a DNA packaging protein from an evolutionarily distant virus into its capsid, meaning the AI created a combination that does not occur in nature but works.


The most impressive aspect is that a mixture of AI-created phages was able to quickly destroy E. coli strains resistant to the natural φX174, while a mixture of natural phages, even those with similar structures, was unable to accomplish the same task. Generative models can design phages that bypass bacterial resistance, change their infection strategy, and form new evolutionary combinations inaccessible to conventional bioengineering. In essence, this is the first step towards creating phage therapies for specific resistant infections - quickly, precisely, and with specified properties.

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Conduit Brain Signal Collection


Keller Scholl published an essay on Conduit, a company that collects non-invasive neurodata, or brain signals from the surface of the head, to train models to translate them into text. He calls for stopping this work at an early stage, including data collection, device development, and funding.

The Conduit project page describes a two-hour conversation between a participant and a language model in a headset, during which the team records brain signals, text, and audio. In December, the company reported approximately 10,000 hours of such recordings from thousands of participants. Each recording gives the model a pair for training: a brain signal and a phrase spoken or typed by the participant.

In Nature Aging, July 2026, a team led by Jerry Tang restored the meaning of perceived and imagined speech using functional MRI, which shows brain activity. This technology can restore communication to people who have lost normal speech or movement. Scholl suggests addressing the fate of such data before they enter a large archive, and warns against the potential for forced application of this technology.

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VirTues Model


The VirTues model compares tissue snapshots with different sets of proteins. On August 5, an article about VirTues was published in Nature: it is a model for spatial proteomics that analyzes proteins in a tissue section along with their arrangement. It was trained on 32 clinical cohorts, data from more than 5,100 patients, and 239 proteins.


The authors tested the model on biopsies of patients with triple-negative breast cancer. In the tumor, cancerous, immune, and connective tissue cells coexist. Spatial proteomics turns a tissue section into a map: it shows which proteins are present in cells and which cells are neighboring each other. Two samples may contain the same types of cells but differ in their arrangement.


The VirTues model associates the signal of each measured protein with its amino acid sequence and its arrangement in the tissue section. During training, the model hides part of the signal and reconstructs it based on neighboring proteins and the layout of the area. This allows it to learn to consider both the individual cell and its environment.


In a strict test, the authors completely excluded the target cohort from training and then asked VirTues to identify cells in its snapshots. The authors counted a cell as correctly identified if the predicted boundary matched the annotation. By this criterion, the system outperformed three specialized programs on eight out of nine datasets. On biopsies of 111 patients with triple-negative breast cancer, taken before treatment, VirTues identified four spatial signatures - combinations of cell types and their co-occurrence.

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Reviving Old T-Cells

The human body's ability to produce new T-cells declines with age, and existing T-cells become slower, less able to divide, and less effective at recognizing threats. A team of scientists sought to determine if they could restore some of the lost function in these cells by reprogramming them from the inside. The main technical challenge was delivering new instructions to old T-cells without damaging them.

The researchers used silicon nanowires - microscopic structures that allow molecular signals to be introduced directly into a cell. With this method, they were able to reprogram more than 90 percent of old T-cells without causing damage. After reprogramming, the cells became more active, dividing more quickly and better attacking infected and cancerous cells, behaving almost like young cells. The team was surprised to find that changing the function of only 4-5 key genes was enough to restore function.

The effect was tested on T-cells from elderly individuals, cancer patients, and cancer survivors, and in all groups, the cells became noticeably more active. The effect currently lasts for around two weeks, but the team is working to extend it, as reported in Nature Aging, July 2026. This does not reverse biological aging, but allows cells to temporarily stop "behaving like old cells" and resume their protective functions.

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Cryopreservation Standards

Maks Mor suggests evaluating cryocenters by their ability to store patients for decades. On August 5, Mor published an essay on biostasis, the preservation of humans after legal death for potential future restoration. He proposes evaluating a cryocenter's work in the first hours and its ability to store a patient for decades.


Mor adds a second criterion to the proposal by Jessica Radley and Ashwin de Wolf to evaluate biostasis methods by neuronal structure preservation, which is the basis of memory and habits. A well-preserved brain will not wait for future medicine if the organization responsible for it does not survive the term. In the first hours, the standby team prepares the body for storage and transports the patient.


Then, the organization buys liquid nitrogen, keeps documents, executes contracts, and passes on this responsibility to people who do not yet work in the organization. Mor formulates the risk directly: "You may be preserved in excellent condition, but you will not see the future because the organization will eventually collapse." The preservation of the body depends on the technique, while the money, documents, and people responsible for storage determine whether the patient will see future medicine, as described in the review of early cryonics by R. Michael Perry.

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Colorectal Adenoma Map

Researchers have linked the GDF15 protein to tissue-renewing cells and lower CD8 cell density in a study published on August 7 in a preprint. The authors analyzed spatial measurements of RNA and proteins in 22 adenomas and compared them to 101 fragments of normal tissue, adenomas, and carcinomas from 16 patients.


The study used spatial multi-omics to examine tissue sections while preserving cell arrangement, allowing researchers to see which genes and proteins are active in each area. The authors looked for senescence, a state of prolonged cell division arrest, in specific areas of the polyp and found that these areas were neighboring two groups of altered cells: those with a senescence program and those with a tissue-renewing program.


The authors compared advanced and early adenomas, areas with high and low stemness, as well as senescent areas of adenomas and normal epithelium, and found six proteins that cells secrete outward. They chose GDF15, a stress response protein, as a candidate for local mediator, and found that when they statistically accounted for GDF15, the connection between senescence and stemness in advanced adenomas weakened.


Around areas with high GDF15 levels, CD8 cells, immune cells that can destroy cells with tumor characteristics, were less common. This local pattern was observed in both protein and RNA markers in human adenomas, continuing a research line on GDF15 that started in 2019 with a study showing that senescent fibroblasts, cells of connective tissue, secrete GDF15 and support the growth of adenoma cells in organoids and cell systems.

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ARPA-H DNA Project


The American agency ARPA-H has commissioned GE HealthCare to assemble a system for on-demand DNA production and verification. On August 4, DNA Script, a developer of enzyme-based DNA synthesis technology, announced its participation in the four-year FLASH project. ARPA-H has contracted with primary contractor GE HealthCare for up to $26 million; the agency's card indicates the project began on March 16.

The team is to assemble an automated system for DNA manufacturing, purification, and verification, which will be transferred to the program's early users. A researcher starts with a digital sequence, but for the experience, they need a physical DNA molecule. It needs to be manufactured, purified from byproducts, and verified to match the ordered sequence. According to ARPA-H's card, the FLASH project is described as an automated system that should connect these operations.

The first step in this path is DNA Script's enzyme-based synthesis. Enzymes are proteins that initiate chemical reactions; in the company's technology, one such enzyme adds one nucleotide, the DNA building block, to the growing chain at a time. A temporary blocking group on the added nucleotide stops the chain elongation. It is removed, and the enzyme adds the next link. The sequence is assembled one nucleotide per cycle. In the project, DNA Script is responsible for enzyme-based synthesis, while GE HealthCare adds scaling technology to increase production volume.

The program should connect synthesis with purification, verification of the finished DNA's match to the ordered sequence, and automation. In June, synthetic DNA suppliers were called to verify both the ordered sequence and the buyer. For FLASH, ARPA-H requires biosecurity and cybersecurity in addition to production stages. According to the agency's plan, after development, the system will be transferred to the program's early users: researchers working on DNA-based medicines will be able to produce the necessary sequences on demand, as described in the FLASH project overview.

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AI Lab Hub


Gladstone and Stanford University are creating an AI hub to model the flow of laboratory experience. On August 6, Le Cong and Kathy Pollard announced the creation of a joint AI hub between Gladstone and Stanford University. A related preprint, published on August 4, describes an AI system that stores all important information about the experiment in one constantly updated record.

The AI model can suggest increasing the concentration of a reagent, and the instrument can add it to the samples. However, such a step requires data that is often stored in the human mind and in different files: the laboratory technician replaced a batch of reagent, the instrument needs calibration, the protocol was changed, and the new result requires re-verification.

Without this information, the model builds a plan based on an incomplete description of the laboratory. The authors of the preprint call such a shared memory model a laboratory state model. It should store information about samples, reagents, instruments, protocols, observations, human interventions, and uncertainty.

According to this scheme, a robot receives a command to increase the concentration of a reagent only after the system checks the sample volume, available reagent batch, instrument calibration, and protocol limitations. Then it prepares the next step allowed by the protocol or passes the question to the scientist.

The new text adds to this video data from instruments, analysis results, and scientist decisions: all of them should update the common record of the experiment state. The authors propose testing such a system in a scenario that simulates the course of an experiment: can it link data from different tools, propose a testable hypothesis, reliably execute a plan, and timely pass the decision to a human.

The system should recognize the normal course of work, an erroneous signal in the data, a failure that can be corrected, or a moment when it is necessary to repeat the experiment, change the protocol, or stop the work. In this architecture, the scientist asks the question, interprets the result, and makes unexpected, ambiguous, and high-risk decisions, as described in the preprint published in August.

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Marcos Arrut Proposes


Marcos Arrut suggests evaluating progress in combating aging using three scales. He responds to the question of what results indicate an approach to treating aging by proposing separate assessments of biological feasibility, tissue control, and clinical testing in humans. On August 9, researcher and entrepreneur Marcos Arrut responded to aging researcher Joao Pedro de Magalhaes in a debate about how quickly science is approaching the possibility of treating aging.


De Magalhaes points to the engineering challenge: intervention must reach the right tissues, work in them, and maintain cell specialization. Arrut proposes evaluating progress based on three independent questions: can we change age-related processes in a living organism, can we control such intervention in tissues, and has it undergone clinical testing in humans. Complete reprogramming can return a cell to an unspecialized state.


In a 2016 article in Cell, Alejandra Ocampo's team cyclically introduced four reprogramming factors, OSKM, in mice - proteins that temporarily change gene function. In a mouse model of premature aging, a range of age-related features improved, and lifespan increased. In normal old mice, researchers also tested recovery after metabolic disruption and muscle injury. In a 2020 study, researchers introduced three factors, OSK, in the mouse retina - tissue on which vision depends. This study linked OSK induction with the restoration of nerve terminals and improved vision metrics. Together, these experiments showed results in both the mouse body and the retina - a tissue with a specific function.


The second scale is control. Partial reprogramming changes gene function, but the cell must maintain its previous role. Arrut describes the task as follows: it is necessary to separate changes in gene function that return youthful characteristics to the cell from those that cause dedifferentiation - loss of specialization. Therefore, in each tissue, such intervention needs to be precisely controlled. The third scale is clinical testing: it checks whether the intervention can be applied to humans. In the first such study, OSK is introduced only into one eye to limit the dose and risk to the entire organism. Experiments on mice answer the question of biological possibility, tissue control - the possibility of managing the method, and clinical testing - application in humans. For Arrut, these are three separate measures of progress.

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CRISPR in Monkey Brain

Researchers delivered the CRISPR DNA editor to the brains of two macaques and modified the MSH3 gene. On August 6, authors of a preprint introduced modified extracellular vesicles into two areas of the brain of two adult macaques, delivering a ready-made CRISPR DNA editor targeted at the MSH3 gene.


The DNA editor can alter the desired site, but first, it needs to be delivered to the cells. While editors can already be delivered to the liver, a separate method is needed for the brain. A viral vector brings the instruction for the editor into the cell, and after introduction, the cell continues to produce it.


The preprint authors chose a different path: producer cells pre-assemble the Cas9 protein, which cuts DNA, with a guide RNA molecule that points to the desired site. The cells then package the ready-made complex into an extracellular vesicle. After delivery, this complex acts for a limited time. Researchers modified the guide RNA to bind better to Cas9 and delayed the release of the complex until packaging.


In cell experiments, these changes increased the editing efficiency by about 300 times. The system was first tested in cells and organoids, small models of the human brain, and then in the brains of mice. After that, it was tested on primates. For the primate experiment, researchers chose the MSH3 gene, which is involved in DNA repair associated with the lengthening of CAG repeats in neurons.


The vesicles were introduced into the tail and shell of the striatum. Scanning showed where the solution with vesicles reached in the striatum. Analysis of DNA from individual tissue samples revealed up to 60-75% editing of MSH3 in some areas. If only the tissue that received the solution is considered, the editing share exceeded 40% in the shell and 60% in the tail, as reported in the preprint.

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RiboX Trial Begins


RiboX reported that the FDA has allowed them to start the first phase of RXIM002, a treatment that equips T-cells with artificial receptors directly in the body. On August 8, the company announced that RXIM002 will be tested for autoimmune cytopenias, diseases in which the immune system destroys blood cells.


The treatment is designed to deliver ring RNA, instructions for assembling an artificial receptor, to T-cells. The trial is called POPULUS-1. Unlike traditional CAR-T therapy, where T-cells are taken from the patient, equipped with a CAR receptor in a laboratory, and then returned to the patient, RXIM002 uses a different approach. A lipid nanocapsule, a tiny container made of fat-like molecules, is used to deliver the ring RNA to the T-cell.


The cell then reads the instructions and assembles the CAR itself. The target of this CAR is CD19, a protein on B-cells that produce antibodies. In autoimmune cytopenias, some of these antibodies may be directed against blood cells. The receptor is designed to recognize CD19 and direct T-cells to B-cells, as described in a Nature Aging, July 2026 study.

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TheoremDB Launch


TheoremDB has introduced a shared journal for AI solving mathematical problems. In the early public version of TheoremDB, work on a problem is broken down into attempts, calculations, and partial results. The next agent sees these records before starting new work; formal proofs can be verified with Lean, a program that checks proofs in strict symbolic form.


TheoremDB released an early public version of a shared workspace for machine mathematics on August 9. It stores work on each problem in separate records: formulations, attempts, calculation files, partial results, and proof states. A separate agent session can leave a calculation, partial result, or failed attempt.


In the analysis of Theo-Conjecture, a counterexample saved in the attempt journal forced the system to change the formula. Articles, libraries, and personal notes usually store the outcome; the next agent run starts without the working memory of the previous one. In TheoremDB, an agent first reads the records on the problem, matches its plan with what has already been done, and adds the result to the journal. These records can be read by anyone, and a new entry is linked to the author's account.


The article summarizes the work; the journal keeps separate steps while the problem is still moving. This memory is useful where two attempts can be exactly matched. In formal proof, Lean brings the proof state to a single form and gives it an exact fingerprint. In computational search with predefined boundaries, an agent matches the already checked range, the spent computational budget, and the result: the next agent sees exactly what has already been checked and how many calculations it took.

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Cellular Cleanup Molecule Found


A team from Dana-Farber and Harvard described a method to find small molecules that guide the cell's disposal system to a chosen protein in Nature on 5 August. The search among seven E3-ligases and 5,000 compounds led to M12. A cellular enzyme attaches a glutathione molecule to M12, and the resulting product binds the DCAF11 ligase to the DDX18 protein.

Many proteins lack a convenient site for a drug to attach. A molecular glue temporarily connects the target protein to an E3-ligase. The ligase puts a ubiquitin mark on the protein, and the proteasome, the cell's disposal system, breaks it down. For such glues, only a small number of well-studied E3-ligases have been used so far.

The authors started with seven ligases and a library of compounds to find a new pair. They fixed the ligases on particles, added a library of 5,000 compounds, and cellular lysate. Mass spectrometry detected DDX18 in one of the mixtures. Experiments with each fixed ligase indicated DCAF11, and smaller groups of compounds pointed to M12.

The lysate turned out to be part of the search method. With purified DCAF11 and DDX18, no complex formed; in the lysate, it appeared, and after boiling the lysate, it disappeared. The authors found that the GST enzyme attaches glutathione to M12. The resulting GSH-M12 compound is retained in the DCAF11 site and creates a surface for contact with DDX18.

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National Geographic Report


National Geographic has released a report on attempts to rejuvenate cells while preserving their specialization. The report showcases partial cellular reprogramming, an effort to restore a young state to old or damaged cells while maintaining their specialization, on different scales: from a single eye to claims of rejuvenating the entire organism.


The clinical route, ER-100, is an experimental gene therapy introduced into one eye, and is currently recruiting people with open-angle glaucoma or ischemic optic neuropathy - damage caused by disrupted blood supply. The ClinicalTrials.gov registry lists up to 18 participants for the study. On June 9, Life Biosciences announced the first administration of ER-100.


The report considers the ongoing trial as one of the routes to cellular rejuvenation. ER-100 is administered in one eye, and the treatment involves a modified virus carrying instructions for three proteins - OCT4, SOX2, and KLF4, or OSK. These proteins regulate which genes are active in the cell, and eight weeks of doxycycline intake enable their production. The first phase of the trial involves doctors selecting the dose and checking safety, including how participants tolerate the treatment.


Their health and vision will be monitored for up to five years. This route grew out of experience in 2020 on mice, where OSK shifted chemical marks on DNA, regulating gene function, to a younger state; stimulated the recovery of neuronal projections after damage; and improved vision in mice with a laboratory model of glaucoma. The eye allows for the first human test of this approach on a limited area, as explained by Sinclear: "The FDA likes it when an intervention can be limited to one isolated area of the body: it's safer."


In the same report, there are also long-term plans. Cellular reprogramming researcher Juan Carlos Izpisua Belmonte talks about creating the foundation for future medicine, and Sinclear describes SL-100 as a program for rejuvenating the entire organism. Alongside the single-eye trial are laboratory projects and claims of intervention in the entire body. Shin'ya Yamanaka links aging to several processes: changes in epigenetic marks regulating gene function; DNA damage; changes in mitochondrial function and the environment around cells. He believes future strategies will likely combine multiple approaches and calls for public discussion of the consequences of significantly extending life, saying "Significant life extension may affect questions of human existence and values deeper than AI."

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Math Priority Rule


Tеренс Тао proposes a new rule for determining priority in mathematics, considering the impact of ИИ on the field. He suggests that priority should be given to the person who first publishes a manuscript, provides a clear explanation understandable by colleagues, and includes a formal verification, which will become more common in the future. The release date, according to Тао, should be considered the moment when the last of these materials is ready.


In the past, during his graduate studies, Тао notes that priority was usually determined by the date of the article in a journal. Later, the public timestamp in open archives like arXiv allowed authors to record when their work became available, eliminating concerns that a reviewer might release a similar result earlier. However, Тао writes that ИИ accelerates this race, as authors may announce answers on social media before verification and clear explanation, just to put a timestamp earlier than others.


Тао proposes separating these tasks among different materials: a manuscript to record the result, a journal dialogue with ИИ to show how the solution was found, Lean - a special language in which a computer verifies the logical steps of the proof, to check its formal record, and a lecture or video to explain the idea to people. "Priority is then determined by who first presents a complete set of materials," Тао writes. Early announcement can inform colleagues that the work is still in progress and help competing groups to unite their efforts or negotiate a synchronized publication. Тао also suggests using a cryptographic hash - a computable fingerprint of a file, which allows later verification of whether the published file matches the recorded version.

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Biotech Labs Expand


Ginkgo, a biotech company, is building four university labs for remote experiments, while the National Science Foundation has allocated $380 million to 20 teams creating a network of such facilities. On August 5, the company announced new projects for the California Institute of Technology, Massachusetts Institute of Technology, University of Maryland, and Northwestern University.


Three of these projects are part of a four-year federal program, with the Astera Institute promising to add up to $20 million to the funding. Artificial intelligence can propose a biological hypothesis, but it can only be tested through physical experiments, such as those involving cell cultures, bioreactors, or microscopes.


In its August 5 announcement, Ginkgo described four new university projects that will allow researchers to launch such experiments remotely. In the spring, Ginkgo had already launched Cloud Lab, a service where researchers can remotely order experiments according to a standard protocol. Now, the company is building labs at Caltech, MIT, the University of Maryland, and Northwestern, with three projects part of the federal program to create labs accessible for remote process launches, as described in the National Science Foundation publication.

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BMS Adopts AI Agent

The pharmaceutical company Bristol Myers Squibb (BMS) plans to deploy Bunsen, an AI agent for molecular calculations, after Schrödinger announced an agreement with BMS on 5 August. The companies will jointly develop Bunsen, and BMS also plans to use RetroSynth, a program for planning the synthesis of selected molecules. The search for a drug begins with the selection of a target protein, a protein in the body that the drug should affect.


The researcher formulates a scientific goal in ordinary language using Bunsen, which translates it into a sequence of calculations on the Schrödinger platform, launches them, and returns an interpretation of the results. This combines the calculations needed for the chemist's task, and physical models evaluate the properties of candidate molecules. After calculations, the selected molecule needs to be assembled from available substances.


RetroSynth searches for and evaluates chains of chemical reactions, as well as checks proposed reactions, so the selection of a molecule is immediately linked to the route by which it can be obtained in the laboratory. On 20 July, BMS reported on the planned merger of two computational clusters, which should provide research teams with shared data and computing power.

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David Sinclair Update


Researcher David Sinclair described the search for a single molecule instead of a cellular cocktail on the August 7 Joe Rogan Experience podcast. Computational screening had narrowed down a trillion molecules to 200 candidates, which his team planned to test on human cells. The search for a single molecule grew out of work with OSK, three proteins that reprogram gene function.


In a 2020 article, Sinclair's group had introduced OSK into mouse retina cells and checked if the cells regained younger characteristics, regrew damaged neuronal projections, and improved vision in a glaucoma model. This combination became the starting point for the search for a single molecule that could perform multiple actions in a cell. In conversation with Joe Rogan, Sinclair named the goal more precisely: one molecule should perform three or four actions of the cocktail in a cell.


The computer first narrowed down the enormous number of options to a list that could be tested in the lab by matching potential molecules with ferments - proteins involved in cellular chemical reactions. According to Sinclair, the work took several months, but without computational screening, it would have taken about 160 years. After calculation, 200 substances remained for the next experiment, as cited in Nature Aging, July 2026.

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Petter Attia on Peptides


Petter Attia, author and host of The Drive podcast, released a breakdown of peptides on August 10. He suggests evaluating each promise based on the specific molecule, product, dose, indication, and person it is intended for. In the new episode of The Drive, Attia starts by defining a peptide as a short chain of amino acids. This chemical class includes insulin, which has specific indications and clinical data. The market also uses this term to describe substances that are claimed to have energy, healing, and longevity benefits.


Attia evaluates each promise with five questions. Does the substance have a target - a place in the body where it is supposed to act - and a clear chain to the claimed effect? Has this effect been shown in humans? In what form and dose was the substance studied, how does it behave in the body, and what risk does it pose? Is the balance of benefits and risks suitable for a specific person and their goal? Is there already a studied way to treat the same problem? A study evaluates a specific product: its composition, purity, dose, and method of production. Its result applies to the form of the product and situation that was studied.


As an example, Attia breaks down BPC-157, a peptide associated with healing in the wellness market. In his scheme, he relates this substance to means without scientific support and again goes through questions about the target, result in humans, dosing, and risk. In reviews, the action of the substance is mixed with expectations, concomitant treatment, and changes in behavior. A controlled study separates the effect of the substance itself from these factors. "Nature Aging, July 2026" and other studies are not mentioned in this context, but generally, a substance that worked for one disease, in one group of people, or in one dose, cannot be automatically considered to work just as well after changing any of these conditions. For a decision on application, information is needed about the molecule, product, dose, indication - the disease or condition for which it is applied - and person. When at least one of these parts changes, the result of the previous study needs to be verified in a new situation.

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