RT Nick Szabo (@NickSzabo4)Perhaps. But you're being even more pessimistic than the creationists!Per some more queries I have just made of ChatGPT, if we take a thousandth of the amino acids of the comets in our solar system, put them on earthlike planet(s), and have them spontaneously form larger peptides and proteins, each amino acid participating in one such new combination per second, then the odds of them generating all the proteins in LUCA in one particular trial, during ten billion years of such tria ...

Scientists have for decades been trying to predict how proteins fold into 3D shapes. A fishly complex process which could hold the key to understanding many diseases. The problem seems to have been cracked by a company founded by a former child chess champion and gamer Demis Hazabes.Alpha folder represents a huge leap forward that I'm hope will really accelerate drug discovery and help us to better understand disease. >> So we created this alpha fold database which is 200 million structures of almost all kn ...

Biological life depends on two families of large molecule: nucleic acids and proteins. The first of our collection of primers explains what they are and how they work https://t.co/mPBhBsBKLS ...

AI驱动的药物发现引擎 - Isomorphic Labs 正在构建一个药物设计引擎，旨在为不同疾病领域和适应症设计新的分子，甚至适用于不同的药物形式[2] - 该引擎由一系列AI模型驱动，需要大约六个类似AlphaFold的突破性技术共同作用[3] - 这些模型包括预测蛋白质结构、分子结合强度以及药物安全性和吸收性的能力[3][6] 传统药物发现的变革 - 传统药物设计是迭代的，耗时且成本高昂，而Isomorphic Labs 正在将这一过程转移到虚拟世界，在计算机上进行设计和测试，从而缩短时间并提高效率[8][9][10] - 通过在计算机上进行多次迭代，选择最佳结果进行实验室测试，从而跳过步骤，更快地获得更好的结果[10] 模型能力与挑战 - AlphaFold 已经解决了蛋白质折叠问题，但仍需要提高模型的准确性，并理解分子如何与身体的不同部分相互作用[11][12][13] - 分子空间巨大（约10的60次方），需要生成模型和搜索过程来有效地搜索整个空间，从而减少需要实验室测试的分子数量[15][16] 数据策略与质量 - 机器学习依赖于高质量的数据，Isomorphic Labs 拥有全面的数据战略，利用公共数据、历史数据以及专门为机器学习模型创建的湿实验室数据[24][25] - 公司在理解、摄取、清理数据以及从中提取信号方面投入了大量精力[26] 通用性与可重用性 - Isomorphic Labs 专注于构建通用的技术，可以应用于任何靶点、任何疾病领域，甚至任何不同的药物形式[27][28] - 通用模型更具挑战性，但可以创造针对长期未解决的问题的全新化学物质[30][31] 疾病领域重点 - 公司专注于免疫学和肿瘤学，因为这些领域的临床试验更易于处理，可以在更短的时间内完成，并且对患者的影响很大[33][34] - 目标是将癌症转变为一种慢性疾病，患者可以通过药物治疗获得正常的寿命，这可能在几年内实现[36][37] 模型能力与应用 - 这些模型可以应用于整个人类蛋白质组，并且由于神经网络模型运行速度快，可以并行分析数千个蛋白质[39] - 这改变了实验设计，因为在实验世界中，一次只能研究一个蛋白质[40] 合作与里程碑 - 与诺华和礼来等公司的合作非常密切，在一些具有挑战性的靶点上取得了良好的进展[44] - 在某些情况下，已经识别出与以前未知的蛋白质结合的首批化学物质[45] 未来展望 - 目标是在2-3年内将AI药物发现过程缩短至几个月，关键在于提高计算机实验结果的预测准确性[47][48][49] - 展望未来，AI工具可以帮助诊断疾病，并提供相应的药物治疗，从而对人类健康和疾病产生重大影响[50][51]

Scientific Discovery - Newly discovered proteins offer potential for paleontological research into animal behavior, diet, and evolution [1] - Molecular tools can now be applied to study animals previously considered too old for such analysis [1]

Scientific Discovery - Palaeontology may experience a game changer due to potential discovery of even older proteins [1] Industry Impact - The discovery of older proteins could have tantalising hints for scientists [1]

Until recently the oldest proteins recovered for reliable, in-depth analysis were around 4m years old. But now, two separate studies have discovered ancient ones some of which could be as old as 29m years https://t.co/9Smb5IZ3Be ...

Scientific Discovery - New discoveries expand the timeline of proteins available for analysis ten-fold compared to DNA [1] - Palaeontologists can now understand organisms that are too old for other ancient molecular analysis [1]

AlphaFold's Impact on Science and Industry - AlphaFold identified over 240 million protein structures in approximately two years, a thousandfold increase compared to the 210 thousand structures discovered in the previous 100 years [2][3] - AlphaFold, an AI system, won the Nobel Prize in Chemistry in 2024, signifying the increasing recognition of AI in scientific advancements [4][5] - AlphaFold accelerates drug discovery, disease understanding, and vaccine development by predicting protein structures in 3D [6][7] - AlphaFold's capabilities are expanding to include DNA, RNA, and small molecules, demonstrating AI's growing influence in diverse scientific fields [7] Implications for Corporate Leadership and Strategy - The business world is characterized as "Barney" (brittle, anxious, nonlinear, and incomprehensible), requiring companies to adapt to rapid changes and disruptions [8][9] - AI managers face the dilemma of balancing emerging technologies, internal operations, and evolving market dynamics [10][11] - Companies need to shift from outdated quantitative management to visionary foresight, emphasizing learning and proactive action [12] - Businesses should explore how AlphaFold-like technologies can be applied beyond medicine and biology, such as in polymers, adhesives, and new materials [13][14] - New physics simulation trains 430 thousand times faster than reality [19] - Route optimization tools like Nvidia Cosmos can significantly improve logistics and shipping efficiency [20] Call to Action for Boardrooms and Executives - Boardrooms and executives must embrace and experiment with new technologies to envision the future and make informed decisions [23] - Leaders need to transition from traditional management approaches to visionary foresight to stay ahead of competitors [24] - The advancements enabled by AlphaFold represent a groundbreaking era for society, environment, technology, and business [26]