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We built an 8000 sqft. high-throughput wet-lab in 30 days, to solve molecular discovery - starting with pesticides. Most teams in drug-discovery don’t receive target-organism verification until clinical trials. Pesticides allow us to rapidly validate our model in the target organism directly. With the help of our high-throughput lab,...

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Navvye Anand

3,921 subscribers

566,106 просмотров • 2 дней назад •via X (Twitter)

Здоровье и велнесНаука и технологии
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Комментарии: 47

Фото профиля Navvye Anand
Navvye Anand2 дней назад

Building a wet-lab is not easy, but we believe that the future of molecular discovery relies on such high-throughput, low-latency wet labs, which will enable us to scale data. The largest agricultural CRO quoted us 1 year to provide the same amount of data we generated in 1 week. To learn more about how we built this lab in 30 days, visit:

Фото профиля Navvye Anand
Navvye Anand2 дней назад

In order to ensure that our AI does not go rogue, we have a digital clone of the lab - where the AI generates rollouts before instructing humans to carry out the experiments in the lab. We believe that it’s extremely important to be safe when dealing with chemicals!!

Фото профиля Navvye Anand
Navvye Anand2 дней назад

We’re always willing to talk to people who are interested in building the next generation of biotech! Feel free to reach out to us at [email protected].

Фото профиля Aaditya Nalawade
Aaditya Nalawade2 дней назад

@tylerarose0 @shaurya_sinha7 crazy stuff!!

Фото профиля Archie Sengupta
Archie Sengupta2 дней назад

@tylerarose0 @shaurya_sinha7 congrats @navvye @tylerarose0 👏🙌

Фото профиля Navvye Anand
Navvye Anand2 дней назад

@tylerarose0 @shaurya_sinha7 thank you archie!!

Фото профиля anish lakkapragada
anish lakkapragada2 дней назад

@tylerarose0 @shaurya_sinha7 extremely high aura interactive website

Фото профиля Navvye Anand
Navvye Anand2 дней назад

@tylerarose0 @shaurya_sinha7 proximal website is fucking cool

Фото профиля Yacine Mahdid
Yacine Mahdid2 дней назад

@tylerarose0 @shaurya_sinha7 hell yeah man keep it up!

Фото профиля John Zeratsky
John Zeratsky2 дней назад

@tylerarose0 @shaurya_sinha7 so fun to see this announcement!!

Фото профиля Lan Xuezhao
Lan Xuezhao2 дней назад

@tylerarose0 @shaurya_sinha7 Really cool to see!!

Фото профиля Arav Kumar
Arav Kumar2 дней назад

@tylerarose0 @shaurya_sinha7 my goats

Фото профиля Dhruv Bindra ⏳
Dhruv Bindra ⏳2 дней назад

@tylerarose0 @shaurya_sinha7 SO COOL

Фото профиля Navvye Anand
Navvye Anand2 дней назад

@tylerarose0 @shaurya_sinha7 Love you :)

Фото профиля george
george2 дней назад

@tylerarose0 @shaurya_sinha7 Tough

Фото профиля Samyak Baid
Samyak Baid2 дней назад

@tylerarose0 @shaurya_sinha7 Congrats!!

Фото профиля Andrea Li
Andrea Li2 дней назад

@tylerarose0 @shaurya_sinha7 let's go!

Фото профиля Navvye Anand
Navvye Anand2 дней назад

@tylerarose0 @shaurya_sinha7 ty andrea :)

Фото профиля nabajit (bio/acc)
nabajit (bio/acc)2 дней назад

@tylerarose0 @shaurya_sinha7 congrats, very exciting !!!

Фото профиля Ethan Goodhart
Ethan Goodhart2 дней назад

@tylerarose0 @shaurya_sinha7 Can’t wait to have better pesticides

Фото профиля JB Michel
JB Michel2 дней назад

@tylerarose0 @shaurya_sinha7 Fire

Фото профиля sudarshan
sudarshan2 дней назад

@ycombinator @tylerarose0 @shaurya_sinha7 Wow pretty incredible Navye

Фото профиля Navvye Anand
Navvye Anand2 дней назад

@ycombinator @tylerarose0 @shaurya_sinha7 Thank you Suds

Фото профиля Chandra Suda
Chandra Suda2 дней назад

@tylerarose0 @shaurya_sinha7 let’s go, excited for this future

Фото профиля joe
joe2 дней назад

@tylerarose0 @shaurya_sinha7 goated website holy

Фото профиля Navvye Anand
Navvye Anand2 дней назад

@tylerarose0 @shaurya_sinha7 Thank you!!

Фото профиля Yug
Yug2 дней назад

@tylerarose0 @shaurya_sinha7 Crazy !!!

Фото профиля ishaan
ishaan2 дней назад

@tylerarose0 @shaurya_sinha7 this is so cool

Фото профиля Naman Ambavi
Naman Ambavi2 дней назад

@tylerarose0 @shaurya_sinha7 Too good! Congrats 🙌

Фото профиля Janik Ludwig
Janik Ludwig2 дней назад

@tylerarose0 @shaurya_sinha7 Cool

Фото профиля SEAR
SEAR2 дней назад

@tylerarose0 @shaurya_sinha7 8,000 sqft in 30 days is serious execution

Фото профиля Agastya Sinha
Agastya Sinha2 дней назад

@tylerarose0 @shaurya_sinha7 Building the future 💪

Фото профиля Tyler Rose
Tyler Rose2 дней назад

@shaurya_sinha7 good stuff

Фото профиля eth
eth2 дней назад

@tylerarose0 @shaurya_sinha7 @Sharckus these guys seem pretty cool

Фото профиля Jay
Jay2 дней назад

@tylerarose0 @shaurya_sinha7 Congrats this is awesome

Фото профиля Rishab Jain
Rishab Jain2 дней назад

@tylerarose0 @shaurya_sinha7 Congrats team, this is very exciting!

Фото профиля Dev
Dev2 дней назад

@tylerarose0 @shaurya_sinha7 so cool!!

Фото профиля Navvye Anand
Navvye Anand2 дней назад

@tylerarose0 @shaurya_sinha7 bless up!!

Фото профиля sudarsh
sudarsh2 дней назад

@tylerarose0 @shaurya_sinha7 Insanely cool!

Фото профиля Navvye Anand
Navvye Anand2 дней назад

@tylerarose0 @shaurya_sinha7 ty sudarsh!

Фото профиля Kyle Vedder
Kyle Vedder2 дней назад

@tylerarose0 @shaurya_sinha7 congrats @navvye , im extremely excited to see what your rapid feedback loop can do for bio discovery models

Фото профиля fresh prince of bombay
fresh prince of bombay2 дней назад

@tylerarose0 @shaurya_sinha7 Sick!

Фото профиля Shaurya Sinha
Shaurya Sinha2 дней назад

@tylerarose0 Linsanity

Фото профиля Navvye Anand
Navvye Anand2 дней назад

@tylerarose0 chat is this racist?

Фото профиля Pranav
Pranav2 дней назад

@tylerarose0 @shaurya_sinha7 this is sick!

Фото профиля Phosphor Capital
Phosphor Capital2 дней назад

@tylerarose0 @shaurya_sinha7 congrats!

Фото профиля Navvye Anand
Navvye Anand2 дней назад

@tylerarose0 @shaurya_sinha7 thank you!

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Today, we’re announcing the first major discovery made by our AI Scientist with the lab in the loop: a promising new treatment for dry AMD, a major cause of blindness. Our agents generated the hypotheses, designed the experiments, analyzed the data, iterated, even made figures for the paper. The resulting manuscript is a first-of-a-kind in the natural sciences, in which everything that needed to be done to write the paper was done by AI agents, apart from actually conducting the physical experiments in the lab and writing the final manuscript. We are also introducing Robin, the first multi-agent system that fully automates the in-silico components of scientific discovery, which made this discovery. This is the first time that we are aware of that hypothesis generation, experimentation, and data analysis have been joined up in closed loop, and is the beginning of a massive acceleration in the pace of scientific discovery that will be driven by these agents. We will be open-sourcing the code and data next week. Robin is a multi-agent system that uses Crow, Falcon, and Finch, the agents on our platform, to generate novel hypotheses, plan experiments, and analyze data. We asked Robin to find a new treatment for dry age-related macular degeneration. Robin considered the disease mechanisms associated with dry AMD, proposed a specific experimental assay that could be used to evaluate hypotheses in the wet lab, and proposed specific molecules we could test in that assay. We tested the molecules and gave it the resulting data, which it analyzed before proposing more experiments. In the end, it identified Ripasudil, a Rho Kinase inhibitor (ROCK inhibitor) that is approved in Japan for several other diseases, which seems very promising as potential treatment for dry AMD. It also identified specific molecular mechanisms that might underlie the effects of Ripasudil in RPE cells, from an RNA sequencing experiment it proposed. To be clear, no one has proposed using ROCK inhibitors to treat dry AMD in the literature before, as far as we can find, and I think it would have been very difficult for us to come up with this hypothesis without the agents. We have also run the proposed treatment by several experts in AMD, who confirm that it is interesting and novel. Moreover, this project was fast: with Robin in hand, the entire project took about 10 weeks, which is way shorter than it would have taken if we had been doing all of the in-silico components ourselves. Important caveats: We are real biologists at FutureHouse, so I want to be clear that although the discovery here is exciting, we are not claiming that we have cured dry AMD. Fully validating this hypothesis as a treatment for dry AMD will take human trials, which will take much longer. Also, this discovery is cool, but it is not yet a "move 37"-style discovery. At the current rate of progress, I'm sure we will get to that level soon. Congratulations to the team. Congratulations in particular to Robin, which generated the hypotheses, proposed the experiments, analyzed the data and generated the figures. And major congratulations also to the human team, which built Robin: Michaela Hinks, Ali Ghareeb, Benjamin Chang, Ludovico Mitchener, Mo Razzak, Kiki Szostkiewicz, and Angela Yiu.
5:57

Today, we’re announcing the first major discovery made by our AI Scientist with the lab in the loop: a promising new treatment for dry AMD, a major cause of blindness. Our agents generated the hypotheses, designed the experiments, analyzed the data, iterated, even made figures for the paper. The resulting manuscript is a first-of-a-kind in the natural sciences, in which everything that needed to be done to write the paper was done by AI agents, apart from actually conducting the physical experiments in the lab and writing the final manuscript. We are also introducing Robin, the first multi-agent system that fully automates the in-silico components of scientific discovery, which made this discovery. This is the first time that we are aware of that hypothesis generation, experimentation, and data analysis have been joined up in closed loop, and is the beginning of a massive acceleration in the pace of scientific discovery that will be driven by these agents. We will be open-sourcing the code and data next week. Robin is a multi-agent system that uses Crow, Falcon, and Finch, the agents on our platform, to generate novel hypotheses, plan experiments, and analyze data. We asked Robin to find a new treatment for dry age-related macular degeneration. Robin considered the disease mechanisms associated with dry AMD, proposed a specific experimental assay that could be used to evaluate hypotheses in the wet lab, and proposed specific molecules we could test in that assay. We tested the molecules and gave it the resulting data, which it analyzed before proposing more experiments. In the end, it identified Ripasudil, a Rho Kinase inhibitor (ROCK inhibitor) that is approved in Japan for several other diseases, which seems very promising as potential treatment for dry AMD. It also identified specific molecular mechanisms that might underlie the effects of Ripasudil in RPE cells, from an RNA sequencing experiment it proposed. To be clear, no one has proposed using ROCK inhibitors to treat dry AMD in the literature before, as far as we can find, and I think it would have been very difficult for us to come up with this hypothesis without the agents. We have also run the proposed treatment by several experts in AMD, who confirm that it is interesting and novel. Moreover, this project was fast: with Robin in hand, the entire project took about 10 weeks, which is way shorter than it would have taken if we had been doing all of the in-silico components ourselves. Important caveats: We are real biologists at FutureHouse, so I want to be clear that although the discovery here is exciting, we are not claiming that we have cured dry AMD. Fully validating this hypothesis as a treatment for dry AMD will take human trials, which will take much longer. Also, this discovery is cool, but it is not yet a "move 37"-style discovery. At the current rate of progress, I'm sure we will get to that level soon. Congratulations to the team. Congratulations in particular to Robin, which generated the hypotheses, proposed the experiments, analyzed the data and generated the figures. And major congratulations also to the human team, which built Robin: Michaela Hinks, Ali Ghareeb, Benjamin Chang, Ludovico Mitchener, Mo Razzak, Kiki Szostkiewicz, and Angela Yiu.

Sam Rodriques

1,108,238 просмотров • 1 год назад

We’ve been quiet about protein design at XairaTherapeutics . Here’s what we’ve been building: Introducing X-Design, with results from two active drug programs that show what we think this field needs to deliver: speed to a useful lead, and access to targets that have resisted discovery. → 7 weeks from DNA synthesis to a preclinical lead on an oncology target, with the epitope and reagents already prepared. → A functional antibody antagonist against a challenging GPCR where conventional library screening AND llama immunization had failed. The bar we set for our model goes well beyond binding. The antibodies need to be human-like, functional, cross-reactive across species, and developable. Properties that have to come together in the same molecule for a program to move forward. That’s what makes these results exciting to me: designs tested in the lab, meeting the requirements of real programs in our pipeline. My bet is that the biggest impact of AI protein design will be expanding which diseases and targets we can pursue. We want to choose targets because the biology is compelling and to build the ability to make the right molecules. This is our first look at X-Design. Much more to come. See our blog:
0:28

We’ve been quiet about protein design at XairaTherapeutics . Here’s what we’ve been building: Introducing X-Design, with results from two active drug programs that show what we think this field needs to deliver: speed to a useful lead, and access to targets that have resisted discovery. → 7 weeks from DNA synthesis to a preclinical lead on an oncology target, with the epitope and reagents already prepared. → A functional antibody antagonist against a challenging GPCR where conventional library screening AND llama immunization had failed. The bar we set for our model goes well beyond binding. The antibodies need to be human-like, functional, cross-reactive across species, and developable. Properties that have to come together in the same molecule for a program to move forward. That’s what makes these results exciting to me: designs tested in the lab, meeting the requirements of real programs in our pipeline. My bet is that the biggest impact of AI protein design will be expanding which diseases and targets we can pursue. We want to choose targets because the biology is compelling and to build the ability to make the right molecules. This is our first look at X-Design. Much more to come. See our blog:

Bo Wang

17,944 просмотров • 7 дней назад