– Financing will support development of a foundational machine learning model of medicinal chemistry that can accurately predict small molecule drug candidates for any protein –

–资金将支持开发药物化学的基础机器学习模型，该模型可以准确预测任何蛋白质的小分子候选药物–

– Releases unprecedented dataset publicly to address critical challenges in drug discovery with machine learning –

–公开发布前所未有的数据集，以通过机器学习解决药物发现中的关键挑战–

SALT LAKE CITY, April 05, 2024 (GLOBE NEWSWIRE) -- Leash Biosciences, an artificial intelligence and machine learning (AI/ML)-native biotechnology company unleashing machine learning to solve medicinal chemistry, today announced the completion of a $9.3 million seed financing round to advance its mission of revolutionizing medicinal chemistry through modern computational methods and massive biological data collection.

盐湖城，2024年4月5日（环球通讯社）--人工智能和机器学习（AI/ML）本土生物技术公司Leash Biosciences今天宣布完成一轮930万美元的种子融资，以推进其通过现代计算方法和大规模生物数据收集彻底改变药物化学的使命。

The oversubscribed round was led by Springtide Ventures with participation from MetaPlanet, Top Harvest Capital, Mitsui Global Investment, MFV Partners, Recursion CEO and co-founder Chris Gibson, and Recursion co-founder Blake Borgeson..

这轮超额认购由Springtide Ventures牵头，MetaPlanet、Top Harvest Capital、Mitsui Global Investment、MFV Partners、Recursion首席执行官兼联合创始人克里斯·吉布森（ChrisGibson）以及Recursion联合创始人布莱克·博格森（Blake Borgeson）参与了此次超额认购。。

Leash aims to develop a foundational and generalizable machine learning model of medicinal chemistry that can accurately predict small molecule drug candidates for any protein in silico, and more broadly, interactions between any protein and any chemical. To achieve this, Leash is producing bespoke, expansive datasets of protein targets binding to chemicals.

Leash旨在开发一种基本且可推广的药物化学机器学习模型，该模型可以准确预测计算机中任何蛋白质的小分子候选药物，以及更广泛地预测任何蛋白质和任何化学物质之间的相互作用。为了实现这一目标，Leash正在生产定制的，可扩展的蛋白质靶标与化学物质结合的数据集。

To date, the Company has physically generated over 17 billion high-quality protein-chemical interaction measurements. In its new Salt Lake City headquarters, Leash plans to screen 500+ protein targets against many millions of machine learning-designed, proprietary chemicals by 2025..

迄今为止，该公司已经实际生成了超过170亿个高质量的蛋白质化学相互作用测量结果。在盐湖城新总部，Leash计划到2025年针对数百万机器学习设计的专有化学品筛选500多个蛋白质目标。

“ML improvements in chess, Go, image recognition, language translation, text generation, and protein folding all were driven by the collection and curation of massive datasets. We believe a similar strategy will revolutionize how we approach medicinal chemistry,' said Ian Quigley, CEO of Leash Biosciences.

Leash Biosciences首席执行官伊恩·奎格利（IanQuigley）说：“大规模数据集的收集和管理推动了ML在国际象棋、围棋、图像识别、语言翻译、文本生成和蛋白质折叠方面的改进。我们相信类似的策略将彻底改变我们对待药物化学的方式。”。

'We are thrilled to have the support of this group of top-tier investors who share our vision for transforming drug discovery through an ML-first approach.'.

“我们很高兴得到这群顶级投资者的支持，他们分享了我们通过ML-first方法转变药物发现的愿景。”

To advance its machine learning engine, Leash will use the funding to scale its data collection and computational capabilities. The Company’s ML engine will also support advancing multiple internal therapeutics programs toward in vivo studies.

为了改进机器学习引擎，Leash将利用这笔资金来扩展其数据收集和计算能力。该公司的ML引擎还将支持推进多种内部治疗计划，以进行体内研究。

'Leash's platform stands apart with its combined excellence in machine learning, experimental biology, and medicinal chemistry,' said Claire Smith, Lead Investor at Springtide Ventures. 'We are excited to back this exceptional team as they leverage cutting-edge tech to tackle the toughest drug discovery challenges.'.

Springtide Ventures首席投资者克莱尔·史密斯（ClaireSmith）说，“Leash的平台以其在机器学习、实验生物学和药物化学方面的综合优势而独树一帜。”我们很高兴能够支持这个杰出的团队，因为他们利用尖端技术来应对最严峻的药物发现挑战。”

Alexey Morgunov of MetaPlanet added, 'Leash sits at the forefront of innovating the next paradigm of AI-driven, scalable, and rapid drug design. We are honored to partner with them as thought leaders in this space.'

MetaPlanet的Alexey Morgunov补充道，“Leash处于创新人工智能驱动，可扩展和快速药物设计的下一个范例的前沿。我们很荣幸能与他们合作，成为这个领域的思想领袖。”

The Leash team is comprised of TechBio veterans with expertise spanning AI/ML, biology, and chemistry. Five of the company's six employees are former Recursion employees with experience building and scaling transformational drug discovery platforms. The team also brings experience from Eikon Therapeutics, Myriad Genetics, insitro Biosciences, LinkedIn, Stripe, and other leading technology and biotechnology players..

Leash团队由TechBio资深人员组成，他们拥有AI/ML，生物学和化学方面的专业知识。该公司的六名员工中有五名是前Recursion员工，拥有构建和扩展转型药物发现平台的经验。该团队还带来了来自Eikon Therapeutics、Myriad Genetics、insitro Biosciences、LinkedIn、Stripe和其他领先技术和生物技术参与者的经验。

In parallel, Leash announced the launch of its inaugural machine learning Kaggle competition, the Big Encoded Library for Chemical Assessment (BELKA). Leveraging a dataset of unprecedented scale, BELKA sets out to address one of the most critical challenges in drug discovery: predicting the likelihood of chemical materials binding to pharmaceutically-relevant targets.

与此同时，Leash宣布启动其首届机器学习Kaggle竞赛，即化学评估大编码库（BELKA）。利用前所未有的规模数据集，BELKA着手解决药物发现中最关键的挑战之一：预测化学物质与药物相关靶标结合的可能性。

The competition will be hosted on the Kaggle platform, the world’s largest data science community..

比赛将在全球最大的数据科学社区Kaggle平台上举行。

'By providing participants with access to such a comprehensive dataset, we are empowering the global scientific community to develop innovative solutions that could revolutionize the way we identify potential drug candidates,” said Ian Quigley, Leash Bio CEO.

Leash Bio首席执行官伊恩·奎格利（IanQuigley）表示：“通过为参与者提供如此全面的数据集，我们正在授权全球科学界开发创新解决方案，从而彻底改变我们识别潜在候选药物的方式。”

About the Kaggle Competition: Predict New Medicines with BELKA (Big Encoded Library for Chemical Assessment)

关于Kaggle竞争：使用BELKA（用于化学评估的大型编码库）预测新药

BELKA aims to contribute to groundbreaking advancements in predictive modeling for pharmaceutical research by harnessing the capabilities of artificial intelligence and machine learning. Participants will be tasked with analyzing a vast dataset comprised of 133 million physically-measured activities for each of three key protein targets..

BELKA旨在利用人工智能和机器学习的能力，为药物研究预测建模的突破性进展做出贡献。参与者将负责分析一个庞大的数据集，该数据集由三个关键蛋白质靶标中的每一个的1.33亿个物理测量活动组成。

Leash rigorously produced a dataset that exceeds all existing small molecule binding datasets combined. With 133 million molecules screened against each protein and evaluated with deep sequencing coverage and many replicates, participants will have access to an unparalleled wealth of data in scale and depth.

Leash严格地生成了一个数据集，该数据集超过了所有现有的小分子结合数据集的总和。针对每种蛋白质筛选了1.33亿个分子，并通过深度测序覆盖率和多次重复进行了评估，参与者将能够在规模和深度上获得无与伦比的丰富数据。

Importantly, this competition dataset is larger than the world’s largest existing drug-target dataset (PubChem), providing a unique opportunity for groundbreaking insights and discoveries. It represents a small fraction of Leash’s screening data..

重要的是，这个竞争数据集比世界上最大的现有药物靶标数据集（PubChem）更大，为开创性的见解和发现提供了独特的机会。它代表了Leash筛选数据的一小部分。

Committed to transparency and collaboration in scientific research, Leash plans to publicly release the full dataset of all conditions and replicates aggregated for the contest dataset, some 3.6 billion physically-measured interactions, at the conclusion of the competition. This resulting collection, expected to be released in the summer of 2024, will be approximately 10 times larger than the largest publicly available dataset to date and 1,000 times larger than higher-quality, curated public datasets, providing researchers worldwide with an invaluable resource for future drug discovery efforts..

致力于科学研究的透明度和合作，Leash计划在比赛结束时公开发布所有条件的完整数据集，并为比赛数据集（约36亿物理测量的交互）进行复制。预计将于2024年夏季发布的这一最终数据集将比迄今为止最大的公开可用数据集大约大10倍，比更高质量的精选公共数据集大1000倍，为全球研究人员提供了宝贵的资源，用于未来的药物发现工作。

The BELKA competition is open for registration and concludes on July 8, 2024. For more information, including participation criteria and registration, visit the competition page on Kaggle.

BELKA竞赛开放注册，于2024年7月8日结束。有关更多信息，包括参与标准和注册，请访问Kaggle上的竞赛页面。

About Leash

关于皮带

Leash Biosciences is a biotechnology company unleashing machine learning to solve medicinal chemistry, with headquarters in Salt Lake City, UT. Powered by a team of experts, Leash aims to expand the boundaries of what’s possible in drug discovery. Through the combination of leading-edge machine learning and large-scale chemical and biological datasets, Leash aims to rapidly design novel small molecule therapeutics.

Leash Biosciences是一家利用机器学习解决药物化学问题的生物技术公司，总部位于犹他州盐湖城。在专家团队的支持下，Leash旨在扩大药物发现的可能性。通过结合前沿机器学习和大规模化学和生物数据集，Leash旨在快速设计新型小分子疗法。