与「ToX」相关的搜索结果

AI Drug Discovery Is Becoming a Bottleneck Trade I get excited when an industry starts going through a real regime change. AI drug discovery (“AIDD”) increasingly looks like one of those moments. Two things are happening: 1/ Upstream: the frontier AI labs are piling in. 2/ Downstream: The supply chain is clearly moving. Supply-chain checks suggest the upstream picks-and-shovels of discovery and early preclinical R&D are starting to feel the increase in experimental volume. DNA → protein → assays → sequencing → automation → preclinical testing Names across that stack include $TWST , @GenScript , $ILMN , $TXG , lab-automation vendors and CROs. $TWST expects triple-digit percentage growth in AI-enabled drug-discovery orders in FY26, and another year of triple-digit order growth in FY27. @GenScript's AIDD business doubled YoY in 1H26. Its current platform advertises industrial-scale validation of 4,000+ designs/day, with integrated sequence-to-data workflows. Our channel checks suggest the ramp is moving even faster: roughly 8,000 designs/day currently, with a path toward ~16,000/day by YE26. --- Why does AI drive more wet-lab demand? 1/ AI makes hypothesis generation almost free → way more shots on goal. The bottleneck is moving from expert-driven design to biological validation. 2/ AI models need continuous experimental feedback — and both good and bad data are useful. Traditionally, only the highest-conviction A+ candidates might get pushed into expensive validation. With AI, even the B/C candidates can be valuable because failed experiments generate training data. @GenScript has said its sequence-to-binding workflow can return data in 4–7 days, and that faster cycle times matter because AI models depend on continuous experimental feedback. @Anthropic is a clean example. @claudeai designed 1,320 protein binders. @adaptyvbio converted those digital sequences into DNA, expressed the proteins and tested binding. Only 354 actually bound. And the 966 failures are not wasted. They are useful negative labels: what does not express, what does not bind, what has poor affinity. Those results help train the next model iteration. 3/ Wet labs are no longer just making drugs. They are making training data. $TWST / @GenScript increasingly look like biological data foundries. $TWST explicitly talks about generating model-ready data from AI-designed sequences. In some workflows, the customer may care less about receiving the physical protein than about getting structured experimental results back into the model. Traditional drug discovery asks: “Does candidate X work?” AI drug discovery also asks: “What can this experiment teach the model?” --- TAM of AIDD If AI is simply a better R&D tool, the relevant spending pool is the $300–400B of annual global pharma R&D. If AI meaningfully increases the number of viable drug programs, the opportunity is larger because it expands downstream demand for DNA synthesis, protein production, assays, and preclinical work. Near term, we can also size demand from AI-company spending. If Anthropic reaches $80B of ARR in 2026 and spends just 1% on AIDD, that alone would imply ~$800M of annual investment. --- Trade setup This is a trade that could have long legs. It’s hard to really stop working until PhaseI/II results (2028+) It smells a lot like the bottleneck trade we just saw in semis: GPUs → HBM → networking → power/cooling. In biology, It basically follows the drug discovery process downstream: AI models → designs → DNA/protein → assays → preclinical capacity. After the upstream picks-and-shovels, animal testing could become the next bottleneck. Monkey prices are already near prior highs and CRO capacity is tight. AIDD pushing more candidates into preclinical development would only add demand. ?? But clinical trials are still the bottleneck? This is the biggest pushback I keep coming back to. No matter how fast discovery becomes, drugs still need to go through preclinical → Phase I → Phase II → Phase III → approval. You still need patients, time and capital. But that doesn’t mean the bottleneck trade won’t work. More viable candidates — especially with higher success rates — still means more demand throughout the development process. And who knows: clinical trials themselves may eventually be optimized by AI. ?? What breaks the trade? Near term, the picks-and-shovels trade breaks if experimental budgets stop growing, AI-generated designs don’t translate into useful wet-lab hits, or capacity catches up too quickly. Longer term, the thesis breaks if AI drugs look great in discovery / Phase I but fail at normal rates in Phase II/III. That is why Phase II matters so much. ?? Milestones Late 2026–2027: first Isomorphic-designed drugs enter human trials; more AI-native programs move into IND-enabling work / tox. 2028–2030: clinical trial results start telling us whether AI-designed drugs actually perform better than conventional drugs. Calling all the "bottleneck bros". :) @jukan05 @zephyr_z9 @aleabitoreddit @ParadisLabs +++ More comprehensive analysis:

I left Silicon Valley 6 years ago Why? Because it's a monoculture The most extreme group think of any place on the planet Show up to a dinner party and everyone is in tech So what do they do? They start stack-ranking each other And it's a totally ordered set: being founder of co $100B market cap > founder of co with $50B market cap > managing partner of tier 1 venture firm > hired CEO of $50B public co > founder of $10B co > etc etc Pure toxicity that nerfs creativty And then as a corollary, people are especially afraid to speak truth to power as everyone knows their place in "The Hierarchy" Reject this nonsense. Stay weird. Think independently. Be honest with yourself. Have fun.

晚点关于华为汽车的这篇文章，太敢写了。 而且存活了 4 天，还没有被投诉下架。 里面写道了问界诞生的前因后果，以及为何 3 个月就能造出来一款车。

Extracts from article in Spectator 16th August by Brendan O’Neill: Behold the human wreckage of the cult of diversity. The tragedy of Jason Arday is that he was both built up and destroyed by the ruthless machinery of DEI (Diversity, Equity, and Inclusion). The academy made him into the golden boy of its philistine project of ‘decolonisation’ and gave not one thought to what the consequences might be if his thin story unravelled, as it inevitably would. No one can deny it now: wokeness makes false gods and real tragedies. . . This is the poison chalice of the state religion of diversity. It lays traps for the very people it celebrates. It handpicks members of ‘the oppressed’ for moral anointment and institutional gushing while leaving them open to exposure as others start to query their credentials. That’s the toxic bargain of identity politics: it builds teetering pedestals for ethnic-minority thinkers, meaning their fall from grace is often twice as fatal. And what a pedestal they made for Arday. They worshipped him as ‘the best in the world’. They beatified him as a black wunderkind whose very presence in a place like Cambridge might help to wash away its sins of whiteness. They canonised him as a living monument to ‘decolonisation’. It was precisely this, their giddy, narcissistic desire to manufacture a kind of black saviour, that made them turn a blind eye to the questionable elements of Arday’s CV and life story. They sacrificed their due diligence to the god of decolonisation. And Arday suffered. He was both poster boy and sacrificial lamb of the diversity crusade. Even as he gained from it – reaching the dizzying heights of a Cambridge professorship – he was being damned by it. The foundation of myth and sand his starry career was built on was untenable. Its collapse was a given. It is a testament to the ideological blindness of the inhabitants of our ivory towers that none of them foresaw it. . . . The Faustian ruthlessness of it all is mindblowing. . . . The media is the only institution in this entire tragic scandal that did its job properly. Journalists found a story and they told it. It was the academy and Arday’s colleagues who failed to do their jobs. Who failed to prize merit over identity, and failed to foresee the public embarrassment that would befall their falsely promoted friend. They ignored the truth – the media found it. I and others have been arguing for years that the politics of identity is a disaster for all, black and white alike. White kids are overlooked, black kids are patronised. Whites are branded ‘privileged’, blacks ‘oppressed’. Whites are seen as the beneficiaries of historic crimes who must atone for past sins, blacks as history’s perma-victims who require the gracious helping hand of the state. We are demonised or infantilised according to the accident of our race. Enough. Let our monument to Jason Arday be the restoration of the true ideal of equality. We couldn’t save Jason but we can save merit from the monstrous jaws of identity.

We’re updating Claude Fable 5’s biology safeguards to reduce false positives. In our testing, this update reduced biology-related fallbacks by about 85% across our product surfaces. Fable can now assist on a wider range of everyday health and educational questions. We believe the biggest positive impacts of AI will be in biology and medicine, and we’re committed to putting frontier intelligence safely into the hands of as many researchers as possible. Fable will continue to fallback to Opus 5 for requests we consider dual-use—including virology, toxicology, and molecular design—so it isn't yet usable for professional biology research and drug development. We're committed to closing that gap through trusted access pathways for frontier biology capabilities.

我正处于王虹在北大discouraged的时间。 应试教育cult卷过了，考过全市前二十，考研考过top2，雅思托福gre，cfa都考过了…… 中美big name的大厂也去体验过…… 互联网每个工种都体验过了 卷过…… 做交易一把子梭哈死了算了的日子也体验过了，亏麻。 也体验过某些拜高踩低，结果请客吃饭必开发票的某种金融行业。 总结下来，我的20s前半段完全在体验一种内卷毒化的toxic positivity，一种典型的意识形态，不允许脆弱、必须光鲜、必须积极进取，不能落后要当第一名，完全不可以average，不仅要竞争还要要体面，要不动声色，要融入集体的“奥德赛时期”。 但是你不想做的事情都是消耗你的。 凡是你想做的事情都是滋养你的。 关键是这样的价值观我以前也觉得没什么问题挺好的，直到我考研的时候开始呕吐，一直到我英文面试大厂的时候也会呕吐，一直到我开始排斥用英文。 事到如今感觉early 20s 经历的一切都像是一场邪教，我最震惊的是在我20出头听到一个很厉害的姐姐歇斯底里说她已经25岁了，再不成功就老了。 其实相对来讲我已经是相当松驰的一个人，但不可避免卷入优绩主义的漩涡中也会迷失自己，但是当初最恐惧的事情，比如生存的危机，比如一个人出国留下来，赚钱，这些都解决之后， 还是会怅然想一想自己最擅长最想要的是什么。 决定把最具创造力的年华奉献给什么东西呢，那个东西肯定不应该是赚钱了，但是是什么，我却完全不知道。

A new study published in the Open Access journal Toxins developed a portable air sampling system capable of detecting airborne cyanobacterial toxins in Southwest Florida, USA. Read more here ⬇️

Would you consider this a toxic trait? 🧐 Learn more of the inner workings of @xdavebakerx and @NicoleGoux's minds, only on #CreatorxCreator#! Make sure to enter the #giveaway# for a copy of #PunkNHeads#! Link in our bio. #Podcast# #Comics# #Artists# #GraphicNovels#