Core Viewpoint - Latent Labs has developed a groundbreaking generative AI model, Latent-X, which enables the design of functional protein binders with atomic-level precision, significantly improving the drug discovery process [6][7][26]. Group 1: Company Background - Simon Kohl, a former researcher at DeepMind, founded Latent Labs after leaving the company in late 2022, focusing on advanced protein design models to aid biopharmaceutical companies [2]. - Latent Labs secured $50 million in funding in February 2025 to further its mission in drug development [2]. Group 2: Technology and Innovation - Latent-X can design functional protein binders, including macrocyclic peptides and small protein binders, with unprecedented efficiency and accuracy [6][7]. - The model generates reliable protein binders by solving geometric challenges at the atomic level, producing high-affinity and specificity binders [7][20]. - Latent-X demonstrated a significant improvement in efficiency, requiring only 30-100 candidates per target to achieve results that typically need millions of candidates [7][18]. Group 3: Performance Validation - The research team tested Latent-X on seven benchmark target proteins related to viral infections, tumor regulation, and neurodegeneration [11][12]. - Latent-X achieved a hit rate of 91%-100% for macrocyclic peptides and 10%-64% for small protein binders across the target proteins [18]. - The best-designed macrocyclic peptides reached micromolar affinity, while small protein binders achieved picomolar affinity, surpassing other design models [19]. Group 4: Features and Usability - Latent-X allows users to generate both protein sequences and structures simultaneously, outperforming previous methods that generated them sequentially [23]. - The platform is user-friendly, requiring no coding skills, and provides a complete workflow for laboratory validation [21][29]. - Latent-X is scalable and has successfully generated various therapeutic binders, with plans for further expansion [22]. Group 5: Competitive Advantage - Latent-X excels in generating binders for previously unseen targets, achieving higher simulation hit rates with fewer samples compared to other models [24][28]. - The model adheres to atomic-level biochemical rules, creating structures that are chemically viable and suitable for drug development [28].

Core Viewpoint - The article discusses the imminent transformation brought by artificial intelligence (AI) and robotics, emphasizing that 8 billion people are already involved in this change, with predictions that robots will outnumber humans in the next decade [1][10]. Group 1: AGI Development - Predictions suggest that Artificial General Intelligence (AGI) could be achieved within five years, requiring the integration of three intelligence waves: generative AI, robotics, and AI for Science [5]. - Current focus remains on information intelligence, with advancements in generative AI like ChatGPT demonstrating conversational capabilities, while natural image and video generation still require 4-5 years of development [5]. Group 2: Challenges in AGI - AGI development faces challenges such as "boundary cognition loss" in large language models, leading to the generation of "hallucinations" or fabricated information [6]. - While the hallucination rate has decreased, new model issues have emerged, necessitating context-specific responses in applications like art creation and information retrieval [6]. Group 3: AI for Science - AI for Science is viewed as a transformative force in research, with initiatives like the Tsinghua University Intelligent Industry Research Institute focusing on creating cross-disciplinary foundational models to enhance drug discovery and molecular screening [8]. - AI can significantly narrow down drug candidate searches from billions to millions, improving efficiency in addressing the over 90% of diseases lacking effective treatments [8]. Group 4: Robotics and Future Predictions - Human-like robots are identified as a breakthrough in AI physical intelligence, with predictions that their numbers will surpass humans in ten years [10]. - China is expected to lead the global humanoid robot industry, leveraging its complete supply chain, a young engineering talent pool, and a large unified market to replicate the success of the mobile internet era [10].

Core Viewpoint - The article discusses the introduction of AlphaGenome, a new AI tool by DeepMind that predicts the effects of single nucleotide mutations in human DNA sequences, enhancing the understanding of gene regulation and disease biology [2][3]. Group 1: AlphaGenome Overview - AlphaGenome is a DNA sequence model that can process up to 1 million base pairs and predict various molecular characteristics related to gene regulation [2][9]. - The model builds on previous DeepMind models like Enformer and complements AlphaMissense, focusing on the 98% of the genome that is non-coding and crucial for gene regulation [10][12]. Group 2: Unique Features of AlphaGenome - AlphaGenome offers high-resolution predictions in the context of long DNA sequences, allowing for detailed biological insights without compromising on sequence length or resolution [12]. - It provides comprehensive multi-modal predictions, enabling scientists to gain a deeper understanding of complex gene regulation processes [13]. - The model can efficiently score mutations, assessing their impact on various molecular characteristics in just one second [14]. - AlphaGenome can directly model splicing sites, which is significant for understanding rare genetic diseases [15]. - It achieves state-of-the-art performance across various genomic prediction benchmarks, outperforming or matching existing models in multiple evaluations [16][18]. Group 3: Applications and Research Directions - AlphaGenome can aid in disease understanding by accurately predicting the effects of gene disruptions, potentially identifying new therapeutic targets [23]. - Its predictions can guide the design of synthetic DNA with specific regulatory functions [24]. - The model accelerates basic research by helping to map key functional elements of the genome [25]. - DeepMind researchers have utilized AlphaGenome to explore mechanisms related to cancer mutations, demonstrating its capability to link non-coding mutations to disease genes [26][27]. Group 4: Limitations and Future Directions - Despite its advancements, AlphaGenome faces challenges in capturing the effects of regulatory elements that are far apart in the genome [32]. - The model has not been specifically designed or validated for individual genome predictions, limiting its application in complex traits or diseases influenced by broader biological processes [32]. - DeepMind is continuously improving the model and collecting feedback to address these limitations [32]. - Currently, the API is open for non-commercial use, focusing on scientific research rather than direct clinical applications [32].

撰文丨王聪 编辑丨王多鱼 排版丨水成文 2021 年， AlphaFold2 的问世曾让整个科学界沸腾，它用深度学习 （ Deep Learning） 技术解决了困扰 生物学 50 年的蛋白质折叠难题，实现了对蛋白质结构的快速、精准预测，并于 2024 年获得了诺贝尔奖的 认可。 2025 年 5 月 23 日， 南开大学统计与数据科学学院 郑伟 教授联合新加坡国立大学张阳教授、密歇根大学 安娜堡分校及密歇根州立大学的研究人员，在 Nature 子刊 Nature Biotechnology 上发表了题为 ： Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER 的研究论文。 这些结果凸显了一条新途径——把深度学习与基于经典物理学的折叠模拟相结合，从而实现高精度的蛋白 质结构和功能预测，这些预测可用于全基因组范围的应用。 为什么 AlphaFold 不是终点？ AlphaFold 通过海量数据训练神经网络，直接从蛋白质的氨基酸序列来预测其三维立体结构，但其局限性也 逐渐显现： ...

整理｜冬梅、核子可乐 去年成功斩获诺贝尔奖之后，Demis Hassabis 决定与一位国际象棋世界冠军打场扑克以示庆 祝。Hassabis 一直痴迷于游戏，这股热情也成为他 AI 先驱之路上的契机与驱力。 近日，做客一档名为《60 分钟》的访谈栏目，讲述了他如何带领众多研究者追逐新的技术"圣 杯"——通用人工智能（AGI），一种兼具人类灵活性与超人般速度与知识储备的硅基智能形态。 除此之外，他也在访谈中透露了 DeepMind 未来的研究方向以及有可能亮相的产品和技术。 "天才少年"Hassabis AI 之旅始于国际象棋 Hassabis 于 2010 年与他人共同创立了了 AI 公司 DeepMind，2014 年该公司被谷歌以 5 亿多 美元收购。2017 年，他发明了 AI 算法 AlphaZero，它只需要国际象棋规则和四个小时的自对 弈，就能成为有史以来最强的国际象棋选手，击败人类国际象棋大师。 2024 年，Hassabis 与同为诺贝尔化学奖得主的 DeepMind 总监约翰·江珀 (John Jumper) 共同 获得了诺贝尔化学奖，获奖原因是他创建了一个 AI 模型 AlphaFold2 ...