AstraZeneca iLab: The automated lab of the future

Summary:人工智能(AI), automation and robotics have the potential to transform how we discover and develop new medicines. 当澳门第一赌城在线娱乐将这些功能嵌入到R&D, they offer the prospect of making better decisions faster. 哥德堡的澳门在线赌城娱乐实验室, 瑞典, is our prototype of a fully automated medicinal chemistry laboratory which, through seamless integration with our Molecular AI group, is propelling the design-make-test-analyse cycle (DMTA) of drug discovery to new heights of efficiency.

Speeding up drug discovery to get medicines to patients

The concept of automation in drug discovery is not new but the complete automation of the DMTA cycle has not yet been achieved and is the ambition of the AstraZeneca iLab. The potential of automation to transform medicinal chemistry is huge, especially when integrated with AI and machine learning capabilities.

Small molecule drug discovery is driven through multiple iterations of the DMTA cycle. 这包括设计全新的分子, 通过化学合成来制造它们, testing them in a series of biological assays and analysing any improvements made, 在开始下一轮设计之前. 这是一个漫长而耗时的过程. AI, automation and robotics have the potential to drive this cycle much more rapidly and our aim is ultimately to identify potential drug candidates in half the time it takes today.

澳门第一赌城在线娱乐在2017年开始了这一旅程, with an ambition to build and optimise a prototype that could automatically synthesise small molecule compounds, purify them and make screening-ready solutions for testing. 一旦化合物经过测试, AI steps in to analyse the data and suggest new compounds to make and test.

We have also developed a new make-and-test technology called nanoSAR, a miniaturised high frequency synthetic process coupled with biophysical screening, which is allowing us to explore a wide range of molecules around a key lead compound much more quickly.

How AI and machine-learning are transforming what to make next and how to make it

The iLab works closely with our world-leading Molecular AI group which drives the ‘design’ and ‘analyse’ elements of the DMTA cycle – in other words ‘what to make next’ and ‘how to make it’. This group harnesses AI and machine learning to help our chemists make better decisions faster. 该团队最近发表在 自然-机器智能 describes novel AI-based models that use conditional recurrent neural networks to enable our chemists to work interactively with computers to speed up the exploration of chemical space and the design of potential new drug molecules.

Holistically uniting chemistry, automation and artificial intelligence technology

We are still on the journey but have achieved so much over the past two to three years. We are now capable of synthesising several small molecule compounds in parallel and automatically purifying them. Much of the traditionally manual laboratory tasks can now be done by robots. Working with specialist vendors in hardware and software, primarily BioSero and Zinsser Analytic (now part of Ingersoll Rand), we have reached the third generation of our prototype platform, 哪一个每天都在进步. 在接下来的几年里, we aim to have a fully functional automated chemistry lab both in 哥德堡 and in 剑桥, UK.

The iLab has also been featured in an article on innovations in automating drug discovery in 自然评论 在最近的一次会议报告中 化学性质.