谷歌的Deepmind聲稱能從蛋白質的氨基酸序列準確預測蛋白質結構，生物學上重大的突破
倫敦--Alphabet擁有的DeepMind公司開發(fā)了一款人工智能軟件，能夠準確預測蛋白質在幾天內將進入的結構，從而解決了一個50年前的“重大挑戰(zhàn)”，這一挑戰(zhàn)可能為更好地理解疾病和藥物發(fā)現(xiàn)鋪平道路。
每個活的細胞里面都有成千上萬種不同的蛋白質，這些蛋白質能讓它活得很好。預測蛋白質折疊的形狀很重要，因為它決定了蛋白質的功能，幾乎所有疾病，包括癌癥和癡呆癥，都與蛋白質的功能有關。
“蛋白質是最美麗、最華麗的結構，準確預測它們如何折疊的能力真的非常、非常具有挑戰(zhàn)性，多年來一直困擾著許多人，”歐洲生物信息學研究所的珍妮特·桑頓教授在電話中告訴記者。
英國研究實驗室DeepMind的“阿爾法折疊”人工智能系統(tǒng)參加了一個名為CASP（結構預測關鍵評估）的小組組織的比賽。它是一個社區(qū)實驗組織，其任務是加速解決一個問題：如何計算蛋白質分子的三維結構。在過去25年里一直在監(jiān)測該領域的進展情況?！皩嶒烖S金標準?！敝芤?，該公司表示，DeepMind公司的阿爾法折疊系統(tǒng)在蛋白質結構預測方面取得了無與倫比的準確性。
“DeepMind已經(jīng)躍居領先地位，”CASP主席約翰·莫爾特Mount 教授在宣布這一消息前的一次新聞發(fā)布會上說?！坝嬎銠C科學中一項歷時50年的重大挑戰(zhàn)已在很大程度上得到解決。”
Moult補充說，“藥物設計和新興的蛋白質設計領域都有一些重要的影響?！?/p>
擁有大約1000名員工，幾乎沒有收入，DeepMind已經(jīng)成為Alphabet（谷歌的母公司）支持的昂貴公司。然而，它已經(jīng)成為全球人工智能競賽的領導者之一，與Facebook人工智能研究公司、微軟和OpenAI一樣。
谷歌首席執(zhí)行官桑達爾·皮查伊在推特上對這一突破表示歡迎。
DeepMind的聯(lián)合創(chuàng)始人兼首席執(zhí)行官德米斯·哈薩比斯在電話會議上說：“DeepMind背后的最終愿景一直是構建通用人工智能，然后通過大大加快科學發(fā)現(xiàn)的速度，幫助我們更好地了解周圍的世界?！?/p>
谷歌在2014年斥資6億美元收購了這家公司，最出名的是它開發(fā)了能夠玩太空入侵者和中國古代棋盤游戲GO等游戲的人工智能系統(tǒng)。然而，它總是說，它希望有更多的科學影響。
“游戲是有效開發(fā)和測試通用算法的絕佳試驗場，我們希望有一天我們能把這些算法轉移到現(xiàn)實世界中去，比如科學問題，”哈薩比斯說?！拔覀冋J為阿爾法折疊是本文的第一個證明點。這些算法現(xiàn)在已經(jīng)足夠成熟和強大，足以適用于真正具有挑戰(zhàn)性的科學問題?！盌eepMind還參加了2018年的CASP蛋白質折疊大賽。雖然當時的結果令人印象深刻，但DeepMindAlphaFold的負責人JohnJumper表示，該團隊知道，要生產出“真正強大的生物相關性或在實驗中具有競爭力”的東西，還有一定的路要走。
然而，今年的比賽并不是一帆風順的，Jumper表示，DeepMind進行了三個月，沒有取得任何進展。他說：“我們坐在那里擔心我們耗盡了數(shù)據(jù)？”
甚至在比賽截止日期臨近的時候，Jumper和他的團隊仍然擔心自己可能出現(xiàn)失誤。他說：“機器學習系統(tǒng)中總會出現(xiàn)錯誤?！?/p>
但他們的努力似乎得到了回報?！拔覀冋娴恼J為我們已經(jīng)建立了一個系統(tǒng)，為實驗生物學家提供正確的和可操作的信息，”他說?！澳阌幸粋€結構的原因是為了了解一些自然世界，然后問更多的問題。我們認為我們已經(jīng)建立了一個系統(tǒng)，將真正幫助人們做到這一點?！?/p>
以下是DeepMind的公告全文（中文翻譯僅供參考，英文原文為準）
蛋白質對生命至關重要，幾乎支撐著生命的所有功能，它們是復雜的大分子，由氨基酸鏈組成，蛋白質的作用很大程度上取決于其獨特的三維結構。蛋白質是由氨基酸鏈組成的復雜大分子，蛋白質的作用主要取決于其獨特的三維結構。弄清蛋白質折疊成什么形狀被稱為 '蛋白質折疊問題'，在過去的50年里，它一直是生物學領域的一個重大挑戰(zhàn)。作為一項重大的科學進展，我們最新版本的人工智能系統(tǒng)AlphaFold被兩年一度的蛋白質結構預測關鍵評估（CASP）的組織者認可為解決這一重大挑戰(zhàn)的方法。這一突破表明了人工智能對科學發(fā)現(xiàn)的影響，以及它在一些解釋和塑造我們世界的最基本領域大幅加速進展的潛力。
蛋白質的形狀與它的功能密切相關，預測這種結構的能力可以讓我們更好地了解它的作用和工作原理。世界上許多最大的挑戰(zhàn)，如開發(fā)疾病的治療方法或尋找分解工業(yè)廢物的酶，從根本上說都與蛋白質及其所起的作用有關。近50年來，我們一直停留在這一個問題上--蛋白質如何折疊起來?？吹紻eepMind拿出了一個解決方案，我個人在這個問題上研究了這么久，經(jīng)歷了這么多的停頓和開始，不知道是否能達到目的，這是一個非常特別的時刻。JOHN MOULT教授馬里蘭大學CASP創(chuàng)始人兼主席。多年來，這一直是深入科學研究的重點，使用各種實驗技術來檢查和確定蛋白質結構，如核磁共振和X射線晶體學。這些技術以及冷凍電子顯微鏡等新方法都依賴于大量的試錯，每個結構可能需要數(shù)年的艱苦卓絕的工作，并需要使用數(shù)百萬美元的專用設備。
蛋白質折疊問題 在1972年諾貝爾化學獎的獲獎感言中，Christian Anfinsen提出了一個著名的假設：理論上，蛋白質的氨基酸序列應該完全決定其結構。這一假設引發(fā)了長達5年的探索，希望能夠僅根據(jù)蛋白質的1D氨基酸序列來計算預測蛋白質的3D結構，作為這些昂貴且耗時的實驗方法的補充。然而，一個主要的挑戰(zhàn)是，理論上，蛋白質在形成最終的三維結構之前可能會有許多種折疊方式，這是一個天文數(shù)字。1969年，Cyrus Levinthal指出，通過蠻力計算來列舉一個典型蛋白質的所有可能構型所需要的時間比已知宇宙的年齡還要長--Levinthal估計一個典型蛋白質的可能構型有10^300種。然而在自然界中，蛋白質會自發(fā)地折疊，有的在幾毫秒內就折疊完畢--這種二元對立有時被稱為Levinthal悖論。
CASP14評估結果 1994年，John Moult教授和Krzysztof Fidelis教授創(chuàng)立了CASP，作為一個兩年一次的盲評，以促進研究，監(jiān)測進展，并建立蛋白質結構預測的技術狀態(tài)。它既是評估預測技術的黃金標準，也是建立在共同努力基礎上的獨特的全球社區(qū)。最重要的是，CASP選擇最近才通過實驗確定的蛋白質結構（有些結構在評估時仍在等待確定）作為團隊測試其結構預測方法的目標；這些結構不會提前公布。參賽者必須盲目地預測蛋白質的結構，而這些預測隨后會在獲得地面真實的實驗數(shù)據(jù)時與之進行比較。我們非常感謝CASP的組織者和整個社區(qū)，尤其是那些實驗者，他們的結構使得這種嚴格的評估成為可能。
CASP用來衡量預測準確性的主要指標是全局距離測試（Global Distance Test，GDT），范圍為0-100。簡單來說，GDT大約可以認為是指氨基酸殘基（蛋白質鏈中的珠子）與正確位置的閾值距離內的百分比。據(jù)Moult教授介紹，GDT在90分左右，非正式地認為與實驗方法得到的結果具有競爭力。在今天公布的第14屆CASP評估結果中，我們最新的AlphaFold系統(tǒng)在所有目標中總體達到了92.4 GDT的中位數(shù)。這意味著我們的預測平均誤差（RMSD）約為1.6埃，與一個原子的寬度（或0.1個納米）相當。即使對于最難的蛋白質目標，即那些最具挑戰(zhàn)性的自由建模類別，AlphaFold也實現(xiàn)了87.0 GDT的中位數(shù)得分（數(shù)據(jù)見此處）。
這些令人振奮的結果為生物學家開辟了將計算結構預測作為科學研究的核心工具的潛力。我們的方法可能被證明對重要的蛋白質類別特別有幫助，例如膜蛋白，這些蛋白質很難結晶，因此對實驗測定具有挑戰(zhàn)性。這項計算工作代表了蛋白質折疊問題上的一個驚人進展，這是生物學中一個長達50年的大挑戰(zhàn)。它的發(fā)生比該領域的許多人預測的要早幾十年。看到它將以多種方式從根本上改變生物學研究，這將是令人興奮的。VENKI RAMAKRISHNAN教授諾貝爾-勞雷特和王室協(xié)會主席的報告
我們解決蛋白質折疊問題的方法 我們在2018年首次進入CASP13，我們的初始版本AlphaFold在參與者中取得了最高的準確性。之后，我們在Nature上發(fā)表了一篇關于我們CASP13方法的論文，并附上了相關的代碼，這啟發(fā)了其他工作和社區(qū)開發(fā)的開源實現(xiàn)?，F(xiàn)在，我們開發(fā)的新的深度學習架構推動了我們在CASP14中的方法的變化，使我們能夠達到無與倫比的準確度。這些方法從生物學、物理學和機器學習領域獲得靈感，當然也包括過去半個世紀以來許多科學家在蛋白質折疊領域的工作。一個折疊的蛋白質可以被看作是一個 '空間圖'，其中殘基是節(jié)點，而邊緣則連接著相鄰的殘基。這張圖對于理解蛋白質內部的物理相互作用，以及它們的進化史非常重要。對于在CASP14上使用的AlphaFold的最新版本，我們創(chuàng)建了一個基于注意力的神經(jīng)網(wǎng)絡系統(tǒng)，經(jīng)過端到端的訓練，它試圖解釋這個圖的結構，同時對它正在構建的隱含圖進行推理。它使用進化相關序列、多序列對齊（MSA）和氨基酸殘基對的表示來完善這個圖。
通過迭代這個過程，系統(tǒng)會對蛋白質的底層物理結構進行強有力的預測，并能在幾天內確定高度精確的結構。此外，AlphaFold還可以通過內部置信度來預測每個預測的蛋白質結構中哪些部分是可靠的。我們在公開的數(shù)據(jù)上訓練了這個系統(tǒng)，這些數(shù)據(jù)包括來自蛋白質數(shù)據(jù)庫的約17萬個蛋白質結構，以及包含未知結構的蛋白質序列的大型數(shù)據(jù)庫。它使用了大約128個TPUv3核心（大致相當于約100-200個GPU）運行了幾周，這在當今機器學習中使用的大多數(shù)大型最先進模型的背景下是一個相對適度的計算量。與我們的CASP13 AlphaFold系統(tǒng)一樣，我們正在準備一篇關于我們系統(tǒng)的論文，以便在適當?shù)臅r候提交給同行評審的期刊。
對現(xiàn)實世界的潛在影響 當DeepMind在十年前成立時，我們希望有一天人工智能的突破能幫助我們作為一個平臺，推動我們對基本科學問題的理解?，F(xiàn)在，經(jīng)過4年的努力打造AlphaFold，我們開始看到這一愿景的實現(xiàn)，并對藥物設計和環(huán)境可持續(xù)性等領域產生影響。馬克斯-普朗克發(fā)育生物學研究所所長、CASP評估員Andrei Lupas教授讓我們知道，'AlphaFold驚人精確的模型讓我們解決了一個卡了近十年的蛋白質結構，重新啟動了我們理解信號如何跨細胞膜傳遞的努力。' 我們對AlphaFold對生物研究和更廣泛的世界的影響感到樂觀，并很高興與其他人合作，在未來幾年了解更多關于它的潛力。在撰寫同行評審論文的同時，我們還在探索如何以可擴展的方式為該系統(tǒng)提供更廣泛的使用權。同時，我們還在研究蛋白質結構預測如何有助于我們對特定疾病的理解與少數(shù)專家小組，例如，通過幫助識別功能失常的蛋白質并推理它們如何相互作用。這些見解可以使藥物開發(fā)的工作更加精確，補充現(xiàn)有的實驗方法，更快地找到有希望的治療方法。
AlphaFold是一代人一次的進步，以令人難以置信的速度和精度預測蛋白質結構。這一飛躍性進展表明計算方法是如何改變生物學研究的，并在加速藥物發(fā)現(xiàn)過程中大有可為。阿瑟-D-萊文森博士，CALICO創(chuàng)始人兼CEO，GENENTECH前董事長兼CEO。我們還看到有跡象表明，作為科學界開發(fā)的眾多工具之一，蛋白質結構預測可能在未來的大流行病應對工作中發(fā)揮作用。今年早些時候，我們預測了SARS-CoV-2病毒的幾種蛋白質結構，包括ORF3a，其結構以前是未知的。在CASP14上，我們預測了另一個冠狀病毒蛋白ORF8的結構。令人印象深刻的是，實驗人員的快速工作現(xiàn)在已經(jīng)證實了ORF3a和ORF8的結構。盡管它們具有挑戰(zhàn)性，而且相關序列很少，但與實驗確定的結構相比，我們的預測都達到了很高的準確度。
除了加快對已知疾病的理解，我們還對這些技術探索我們目前沒有模型的數(shù)億蛋白質的潛力感到興奮--這是一片未知生物學的廣闊天地。由于DNA指定了構成蛋白質結構的氨基酸序列，基因組學革命使得大規(guī)模讀取自然界的蛋白質序列成為可能--在通用蛋白質數(shù)據(jù)庫（UniProt）中，有1.8億個蛋白質序列并在不斷增加。相比之下，考慮到從序列到結構所需的實驗工作，蛋白質數(shù)據(jù)庫（PDB）中只有約17萬個蛋白質結構。在未確定的蛋白質中，可能有一些具有新的、令人興奮的功能，就像望遠鏡幫助我們更深入地觀察未知的宇宙一樣，像AlphaFold這樣的技術可能會幫助我們找到它們。
開啟新的可能性 AlphaFold是我們迄今為止最重要的進展之一，但與所有科學研究一樣，仍有許多問題需要回答。并非我們預測的每一個結構都是完美的。還有很多東西需要學習，包括多種蛋白質如何形成復合物，它們如何與DNA、RNA或小分子相互作用，以及我們如何確定所有氨基酸側鏈的精確位置。在與其他人的合作中，如何最好地將這些科學發(fā)現(xiàn)用于開發(fā)新藥、管理環(huán)境的方法等方面也有很多需要學習的地方。對于我們所有從事科學領域計算和機器學習方法的人來說，像AlphaFold這樣的系統(tǒng)展示了人工智能作為輔助基礎發(fā)現(xiàn)工具的驚人潛力。就像50年前安金森提出了一個當時科學遠遠無法企及的挑戰(zhàn)一樣，我們的宇宙還有很多方面是未知的。今天公布的進展讓我們進一步相信，人工智能將成為人類拓展科學知識前沿最有用的工具之一，我們期待著未來多年的努力和發(fā)現(xiàn)!
Proteins are essential to life, supporting practically all its functions. They are large complex molecules, made up of chains of amino acids, and what a protein does largely depends on its unique 3D structure. Figuring out what shapes proteins fold into is known as the “protein folding problem”, and has stood as a grand challenge in biology for the past 50 years. In a major scientific advance, the latest version of our AI system AlphaFoldhas been recognised as a solution to this grand challenge by the organisers of the biennial Critical Assessment of protein Structure Prediction (CASP). This breakthrough demonstrates the impact AI can have on scientific discovery and its potential to dramatically accelerate progress in some of the most fundamental fields that explain and shape our world.
A protein’s shape is closely linked with its function, and the ability to predict this structure unlocks a greater understanding of what it does and how it works. Many of the world’s greatest challenges, like developing treatments for diseases or finding enzymes that break down industrial waste, are fundamentally tied to proteins and the role they play.
We have been stuck on this one problem – how do proteins fold up – for nearly 50 years. To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment.
PROFESSOR JOHN MOULT
CO-FOUNDER AND CHAIR OF CASP, UNIVERSITY OF MARYLAND
This has been a focus of intensive scientific research for many years, using a variety of experimental techniques to examine and determine protein structures, such as nuclear magnetic resonance and X-ray crystallography. These techniques, as well as newer methods like cryo-electron microscopy, depend on extensive trial and error, which can take years of painstaking and laborious work per structure, and require the use of multi-million dollar specialised equipment.
The ‘protein folding problem’
In his acceptance speech for the 1972 Nobel Prize in Chemistry, Christian Anfinsen famously postulated that, in theory, a protein’s amino acid sequence should fully determine its structure. This hypothesis sparked a five decade quest to be able to computationally predict a protein’s 3D structure based solely on its 1D amino acid sequence as a complementary alternative to these expensive and time consuming experimental methods. A major challenge, however, is that the number of ways a protein could theoretically fold before settling into its final 3D structure is astronomical. In 1969 Cyrus Levinthal noted that it would take longer than the age of the known universe to enumerate all possible configurations of a typical protein by brute force calculation – Levinthal estimated 10^300 possible conformations for a typical protein. Yet in nature, proteins fold spontaneously, some within milliseconds – a dichotomy sometimes referred to as Levinthal’s paradox.
Results from the CASP14 assessment
In 1994, Professor John Moult and Professor Krzysztof Fidelis founded CASP as a biennial blind assessment to catalyse research, monitor progress, and establish the state of the art in protein structure prediction. It is both the gold standard for assessing predictive techniques and a unique global community built on shared endeavour. Crucially, CASP chooses protein structures that have only very recently been experimentally determined (some were still awaiting determination at the time of the assessment) to be targets for teams to test their structure prediction methods against; they are not published in advance. Participants must blindly predict the structure of the proteins, and these predictions are subsequently compared to the ground truth experimental data when they become available. We’re indebted to CASP’s organisers and the whole community, not least the experimentalists whose structures enable this kind of rigorous assessment.
The main metric used by CASP to measure the
accuracy of predictions is the Global Distance Test (GDT) which ranges from 0-100. In simple terms, GDT can be approximately thought of as the percentage of amino acid residues (beads in the protein chain) within a threshold distance from the correct position. According to Professor Moult, a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods.
In the results from the 14th CASP assessment, released today, our latest AlphaFold system achieves a median score of 92.4 GDT overall across all targets. This means that our predictions have an average error (RMSD) of approximately 1.6 Angstroms, which is comparable to the width of an atom (or 0.1 of a nanometer). Even for the very hardest protein targets, those in the most challenging free-modelling category, AlphaFold achieves a median score of 87.0 GDT (data available here).
These exciting results open up the potential for biologists to use computational structure prediction as a core tool in scientific research. Our methods may prove especially helpful for important classes of proteins, such as membrane proteins, that are very difficult to crystallise and therefore challenging to experimentally determine.
This computational work represents a stunning advance on the protein-folding problem, a 50-year-old grand challenge in biology. It has occurred decades before many people in the field would have predicted. It will be exciting to see the many ways in which it will fundamentally change biological research.
PROFESSOR VENKI RAMAKRISHNAN
NOBEL LAUREATE AND PRESIDENT OF THE ROYAL SOCIETY
Our approach to the protein folding problem
We first entered CASP13 in 2018 with our initial version of AlphaFold, which achieved the highest accuracy among participants. Afterwards, we published a paper on our CASP13 methods in Nature with associated code, which has gone on to inspire other work and community-developed open source implementations. Now, new deep learning architectures we’ve developed have driven changes in our methods for CASP14, enabling us to achieve unparalleled levels of accuracy. These methods draw inspiration from the fields of biology, physics, and machine learning, as well as of course the work of many scientists in the protein folding field over the past half-century.
A folded protein can be thought of as a “spatial graph”, where residues are the nodes and edges connect the residues in close proximity. This graph is important for understanding the physical interactions within proteins, as well as their evolutionary history. For the latest version of AlphaFold, used at CASP14, we created an attention-based neural network system, trained end-to-end, that attempts to interpret the structure of this graph, while reasoning over the implicit graph that it’s building. It uses evolutionarily related sequences, multiple sequence alignment (MSA), and a representation of amino acid residue pairs to refine this graph.
By iterating this process, the system develops strong predictions of the underlying physical structure of the protein and is able to determine highly-accurate structures in a matter of days. Additionally, AlphaFold can predict which parts of each predicted protein structure are reliable using an internal confidence measure.
We trained this system on publicly available data consisting of ~170,000 protein structures from the protein data bank together with large databases containing protein sequences of unknown structure. It uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks, which is a relatively modest amount of compute in the context of most large state-of-the-art models used in machine learning today. As with our CASP13 AlphaFold system, we are preparing a paper on our system to submit to a peer-reviewed journal in due course.
The potential for real-world impact
When DeepMind started a decade ago, we hoped that one day AI breakthroughs would help serve as a platform to advance our understanding of fundamental scientific problems. Now, after 4 years of effort building AlphaFold, we’re starting to see that vision realised, with implications for areas like drug design and environmental sustainability.
Professor Andrei Lupas, Director of the Max Planck Institute for Developmental Biology and a CASP assessor, let us know that, “AlphaFold’s astonishingly accurate models have allowed us to solve a protein structure we were stuck on for close to a decade, relaunching our effort to understand how signals are transmitted across cell membranes.”
We’re optimistic about the impact AlphaFold can have on biological research and the wider world, and excited to collaborate with others to learn more about its potential in the years ahead. Alongside working on a peer-reviewed paper, we’re exploring how best to provide broader access to the system in a scalable way.
In the meantime, we’re also looking into how protein structure predictions could contribute to our understanding of specific diseases with a small number of specialist groups, for example by helping to identify proteins that have malfunctioned and to reason about how they interact. These insights could enable more precise work on drug development, complementing existing experimental methods to find promising treatments faster.
AlphaFold is a once in a generation advance, predicting protein structures with incredible speed and precision. This leap forward demonstrates how computational methods are poised to transform research in biology and hold much promise for accelerating the drug discovery process.
ARTHUR D. LEVINSON
PHD, FOUNDER & CEO CALICO, FORMER CHAIRMAN & CEO, GENENTECH
We’ve also seen signs that protein structure prediction could be useful in future pandemic response efforts, as one of many tools developed by the scientific community. Earlier this year, we predicted several protein structures of the SARS-CoV-2 virus, including ORF3a, whose structures were previously unknown. At CASP14, we predicted the structure of another coronavirus protein, ORF8. Impressively quick work by experimentalists has now confirmed the structures of both ORF3a and ORF8. Despite their challenging nature and having very few related sequences, we achieved a high degree of accuracy on both of our predictions when compared to their experimentally determined structures.
As well as accelerating understanding of known diseases, we’re excited about the potential for these techniques to explore the hundreds of millions of proteins we don’t currently have models for – a vast terrain of unknown biology. Since DNA specifies the amino acid sequencesthat comprise protein structures, the genomics revolution has made it possible to read protein sequences from the natural world at massive scale – with 180 million protein sequences and counting in the Universal Protein database (UniProt). In contrast, given the experimental work needed to go from sequence to structure, only around 170,000 protein structures are in the Protein Data Bank (PDB). Among the undetermined proteins may be some with new and exciting functions and – just as a telescope helps us see deeper into the unknown universe – techniques like AlphaFold may help us find them.
Unlocking new possibilities
AlphaFold is one of our most significant advances to date but, as with all scientific research, there are still many questions to answer. Not every structure we predict will be perfect. There’s still much to learn, including how multiple proteins form complexes, how they interact with DNA, RNA, or small molecules, and how we can determine the precise location of all amino acid side chains. In collaboration with others, there’s also much to learn about how best to use these scientific discoveries in the development of new medicines, ways to manage the environment, and more.
For all of us working on computational and machine learning methods in science, systems like AlphaFold demonstrate the stunning potential for AI as a tool to aid fundamental discovery. Just as 50 years ago Anfinsen laid out a challenge far beyond science’s reach at the time, there are many aspects of our universe that remain unknown. The progress announced today gives us further confidence that AI will become one of humanity’s most useful tools in expanding the frontiers of scientific knowledge, and we’re looking forward to the many years of hard work and discovery ahead!
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