Yutong Zhao (@proteneer) header
Yutong Zhao (@proteneer) profile picture

chips n drugs @nvidia prev @relay_tx @schrodinger @vijaypande | phys/acc

nan, inf
Joined August 2011
Photo by Yutong Zhao (@proteneer)Photo by Yutong Zhao (@proteneer)Photo by Yutong Zhao (@proteneer)Photo by Yutong Zhao (@proteneer)Photo by Yutong Zhao (@proteneer)Photo by Yutong Zhao (@proteneer)Photo by Yutong Zhao (@proteneer)Photo by Yutong Zhao (@proteneer)Photo by Yutong Zhao (@proteneer)Photo by Yutong Zhao (@proteneer)
Vibe coding in a nutshell
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I like big contexts and I cannot lie
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I had no idea they came out with a 3rd edition
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discovering the parenting joys of jingle bells batman smells
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Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from @MSFTResearch AI for Science. #ML #AI #NeuralNetworks #Biology #AI4Science biorxiv.org/content/10.1101/…
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Whoever decided that lambda should be a reserved keyword in python obviously never worked on free energy methods
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Yutong Zhao retweeted
The point of a startup is to make usable technology for others. When you make software, you have to watch at least 10 people use it. Sit next to them and say absolutely nothing. Force yourself to marinate in the failure of your product design. Every version 1 of any software will be absolutely destroyed by first interaction with users. You need to watch your new creation be absolutely misunderstood by users to reform version 1 into the one that actually works. There is only one path: figuring out where the sharp edges, the places people get caught, the assumptions you make as a builder that turn out to be wrong, and then relentlessly sanding it down so that anyone can use it. That is good design. That is the key to a good product. There is no shortcut for this. WATCH USERS AND CRINGE AND THEN FIX IT. SAND DOWN THE EDGES.
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Nobel prize for left handed alpha helix
The 2024 #NobelPrize laureates in chemistry Demis Hassabis and John Jumper have successfully utilised artificial intelligence to predict the structure of almost all known proteins. In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic. Read more about their story: bit.ly/3XI7KK3
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It is turning out to not be the case. "Structure=>function" had seemed easy because there were all these papers solving a key protein's structure on the way to understanding what it does: "ah, these residues here make a pocket and that one moves, and it all makes sense..." 12/
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Yutong Zhao retweeted
It has been 18 years since the first Falcon flight of Falcon 1 failed
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Yutong Zhao retweeted
The only status game that matters is if you made something people want
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“behind mountains are more mountains.”
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Yutong Zhao retweeted
Ahead of your time with GPU bling @ImranSHaque ? Tell me about it.
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Bothered by the lack of long-range interactions in ML potentials? Meet Latent Ewald Summation—our solution to fix "shortfalls" in short-ranged ML potentials for electrostatic and dielectric systems, with only a modest computational cost! arxiv.org/abs/2408.15165
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making dummy atoms smarter
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We're looking for a research engineer to join us. Our mission is to explore the strange parts of chemistry, to seek out new R-groups and new NaNs, to integrate experimentation with computation, and to boldly go where no mol has gone before: job-boards.greenhouse.io/rel…
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Free energy methods developers
Nietzsche can hype you up
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Scientific software: the fancier the graphics, the shittier the tool
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