Basecamp Research says it has augmented the AlphaFold2 artificial intelligence tool for predicting protein structures to create a platform that is more accurate and better at modelling small-molecule interactions.

Basecamp Research表示，它增强了用于预测蛋白质结构的AlphaFold2人工智能工具，从而创建了一个更准确、更善于模拟小分子相互作用的平台。

The new model, called BaseFold, enables more reliable 3D structure predictions for larger and more complex proteins and, according to the company, is “poised to greatly accelerate AI-based drug discovery efforts.”

该新模型称为BaseFold，能够对更大更复杂的蛋白质进行更可靠的3D结构预测，据该公司称，“有望大大加速基于人工智能的药物发现工作。”

Google DeepMind and EMBL’s freely available AlphaFold2 has had a big impact on research into the role of proteins in disease since its launch in 2021 and is already being used to develop new medicines that can bind to problem proteins more effectively.

自2021年推出以来，谷歌DeepMind和EMBL免费提供的AlphaFold2对蛋白质在疾病中的作用的研究产生了巨大影响，并且已经被用于开发可以更有效地结合问题蛋白质的新药。

Basecamp says that BaseFold builds on that model, which predicts the 3D structure of a protein based on its amino acid sequence, by layering in a foundational dataset (BaseGraph) that considers how proteins behave in the ‘real world’, i.e., in the context of more than six billion chemical, biological, and metagenomic relationships..

Basecamp表示，BaseFold建立在该模型的基础上，该模型通过在基础数据集（BaseGraph）中分层来预测蛋白质的3D结构，该数据集考虑了蛋白质在“现实世界”中的行为，即在60多亿种化学，生物和宏基因组关系的背景下。。

The result is a deep learning model that improves the accuracy of AlphaFold2’s predicted structures by up to six times, and delivers a three-fold improvement in modelling accuracy for small molecule interactions with protein targets, according to Basecamp, which has just published a paper on BaseFold on the biorxiv.org preprint server..

Basecamp刚刚在biorxiv.org预印本服务器上发表了一篇关于BaseFold的论文，据其介绍，其结果是一个深度学习模型，将AlphaFold2预测结构的准确性提高了六倍，并将小分子与蛋白质靶标相互作用的建模精度提高了三倍。。

“We have redesigned and rebuilt the entire data acquisition process, making us the first team ever to collect and annotate biodiversity data with the same quality as human clinical genetic data – all purpose-built for the AI era,” said Dr Phil Lorenz, Basecamp’s chief technology officer.

Basecamp首席技术官Phil Lorenz博士说：“我们重新设计并重建了整个数据采集过程，使我们成为有史以来第一个以与人类临床遗传数据相同的质量收集和注释生物多样性数据的团队，这些数据都是专为人工智能时代而构建的。”。

There are other augmented versions of AlphaFold2 available, including CollabFold, ESMFold, OpenFold, and RoseTTAFold. Basecamp says, however, that their performance relies on “public protein databases that are widely seen as unfit for biotech’s AI era,” as they rely on proteins from lab organisms - representing less than 0.000001% of life on Earth..

还有其他可用的AlphaFold2增强版本，包括CollabFold、ESMFold、OpenFold和RoseTTAFold。然而，Basecamp表示，他们的表现依赖于“被广泛认为不适合生物技术人工智能时代的公共蛋白质数据库”，因为他们依赖的蛋白质来自实验室生物，占地球生命的不到0。。

For example, AlphaFold2 draws on the public MGnify database, which Basecamp claims has issues with incomplete sequences that make it hard to predict the structure of larger proteins.

例如，AlphaFold2利用了公共MGnify数据库，Basecamp声称该数据库存在序列不完整的问题，因此很难预测较大蛋白质的结构。

“AlphaFold is one of the most useful AI tools in drug discovery, and for good reason. It enables researchers to better predict how medicines may interact with proteins in the body, shaving off years of work,” said Basecamp co-founder Dr Glen Gowers.

Basecamp联合创始人Glen Gowers博士说：“AlphaFold是药物发现中最有用的人工智能工具之一，这是有道理的。它使研究人员能够更好地预测药物如何与体内蛋白质相互作用，从而减少了多年的工作。”。

“However, AlphaFold still has significant room for improvement – particularly when being used to predict large, complex, and underrepresented proteins, which are often the most critical for the development of new therapeutics,” he added. “Even just a few percentage points of error can have major implications in accurately predicting protein-molecule interactions.”.

“然而，AlphaFold仍有很大的改进空间，特别是在用于预测大型，复杂且代表性不足的蛋白质时，这通常是开发新疗法的最关键因素，”他补充道。“即使只有几个百分点的误差也可能对准确预测蛋白质-分子相互作用产生重大影响。”。

Alongside the publication and launch, Basecamp also announced a collaboration with NVIDIA to include BaseFold within NVIDIA BioNeMo, the tech company’s AI platform for drug discovery.

除了出版和发布之外，Basecamp还宣布与NVIDIA合作，将BaseFold纳入科技公司药物发现AI平台NVIDIA BioNeMo。