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Had deep neural networks been invented before Newton, physics would not have looked the same.
Huafeng Xu retweeted
Drug discovery has always been a race against time. Fathom Therapeutics is trying to change the pace entirely — using physics + AI to design drugs fast enough to outrun disease.
We joined their recent $47M Series A alongside @shv and are excited to support @hardsphere and team.
Ironically, we now pin our hopes of transforming biology into a predictable, reproducible, and deterministic engineering discipline on neural networks, a technology inspired by biology and, rare among engineered systems, itself unpredictable and non-deterministic.
Rich people are different. They chase what money cannot buy: they chase what only an excessive amount of money can buy.
The discovery of a new medicine is a rare event. Can AI help turn the improbable into the likely?
My (digressive) thoughts on how AI may impact drug discovery in a meandering post. Title inspired by Blade Runner. Style warped by sleep deprivation.
medium.com/@huafeng/do-neura…
The most notable effect of AI on science will not be the solution of outstanding scientific problems but the shortening of author list in scientific publications. We might soon see single author papers describing complex scientific works that previously call for a team.
I like a watermelon because “you can eat, drink, and wash your face all at the same time.” What is this monstrosity that takes away two thirds of the fun?
I share my thoughts on the enabling elements that drive rapid technological advances: public attention, scientific foundation, talent pool and empowering infrastructure, focus on value creation, technology integration, and sufficient investment. tinyurl.com/5n8srpsc
Wenfeng Liang, who founded and led the DeepSeek team, is one of the over one hundred authors of the DeepSeek paper, but he is neither the first nor the last author, and there is no asterisk next to his name. That alone is a distinction.









