chemistry 4.0

Made Smarter helps SMEs embrace Chemistry 4.0

The chemical sector is experiencing a tectonic shift towards automation of processes and products. However, this presents barriers for SMEs, from finance and a lack of digital skills, to uncertainty over where to start and the struggle to find time to capitalise on opportunities.

Helping SMEs keep up with the pace was the foundation of the Made Smarter Adoption programme, which was first launched in the North West, and is now set to expand across the UK. Launched as a £20m government-funded industryled pilot in 2019, it has since worked with over 1,200 businesses, including many in the chemical sector, offering impartial technology advice, digital transformation workshops, a leadership programme, and funded digital technology internships. The programme has supported 201 technology projects which are forecast to deliver an additional £150m in GVA for the North West economy over the next three years, create over 920 new jobs, and upskill 1,764 existing roles. Some have invested in technology to integrate systems, capture and analyse data, and create simulations of their plants and processes. Others are using 3D-printing, automation, and robotics to solve business challenges and meet increased demand. For example, Organica UK, a Birkenhead-based manufacturer of environmentally friendly household cleaning products, invested in two technology projects which have created new jobs and increased turnover. Sensors now capture the volumes of ingredients going into and coming out of its blending tanks and other parts of its filling process, introducing real-time monitoring and analytics which have resulted in a 20% productivity increase. A second project will create a bespoke, cloud-based ERP solution to help keep track of orders, production and stock, and is forecast to improve efficiency by 25%, reduce energy consumption by 10% per ton of product, and reduce waste by up to 20%.

WHERE TO START

A Digital Transformation Workshop offers a streamlined diagnostic of products, services, processes and people to find practical solutions. Manufacturers are then given a bespoke guide with recommended first steps and a digital roadmap. This roadmap helps target the right technology to grow progressively and sustainably, and avoid wasted time, effort and money.

SKILLS AND LEADERSHIP

Manufacturers can upgrade their skill sets with a Leadership Programme to give them the strategic view and skills needed to pursue smarter manufacturing, using a hybrid model of classroom learning and site visits.

Andrew Mooney, Managing Director of Actikem, a chemical manufacturer, based in Warrington, benefited from the programme which helped the business navigate the impact of the pandemic. Made Smarter also offers specialist advice about organisational and workforce development, and funded Digital Technology Internships for university students and graduates. Such has the been the success of the North West programme, it has now been expanded to the North East, Yorkshire and the Humber, and the West Midlands.

More details at madesmarter.uk

Ulysses is the world’s first fully automated drug discovery platform developed and operated by Arctoris based in Oxford, Boston and Singapore

AI and Cloud accelerate closed loop drug discovery

A collaboration between IBM Research and Arctoris is investigating the application of AI and automation to accelerate closed loop molecule discovery.

IBM Research has developed RXN for Chemistry, an online platform leveraging state-of-the-art Natural Language Processing (NLP) architectures to automate synthetic chemistry. Representing chemical reactions via SMILES (Simplified Molecular Input Line Entry System), the system is able to perform highly accurate reaction predictions using its powerful AI. Optimised synthetic routes are then used as input for RoboRXN, an automated platform for molecule synthesis. Oxford-based technology company Arctoris has developed Ulysses, an end-to-end automated platform for drug discovery research. The platform ensures accuracy, precision, and reproducibility by leveraging robotic experiment execution and digital data capture technologies across cell and molecular biology and biochemistry/ biophysics. Experiments conducted with Ulysses generate more than 100 times more datapoints per assay compared to industry standard, leading to deeper insights and accelerated progress compared to manual methods. The two platforms are now being combined for the first time in a research collaboration that will see new small molecule inhibitors for undisclosed targets being designed, made, tested, and analysed (DMTA) in an autonomous, closed loop approach. Concretely, IBM Research will design and synthesize novel chemical matter (Design, Make), to be profiled and evaluated by Arctoris (Test, Analyze), with the resulting data informing the subsequent iteration of the DMTA cycle. Thomas A. Fleming, Arctoris co-founder and COO, explained: “The future of drug discovery is computational, with AI and robotics paving the way for better treatments to reach patients sooner. We are excited about partnering with IBM Research on a world-first closed loop drug discovery project bringing together two leaders in the field of AI and robotics-powered drug discovery. This collaboration will showcase how the combination of our unique technology platforms will lead to accelerated research based on better data enabling better decisions.” Dr Teodoro Laino, Distinguished Scientist at IBM Research Europe – Zurich, said: “This collaboration is a great example of the enablement that AI, Cloud and Automation can have in the space of material design. The integration between the two complementary technologies reveals how it is more and more important in R&D to turn great research into great viable products.” Project co-ordinator Dr Matteo Manica, Research Scientist at IBM Research Europe – Zurich, added: “This is a unique opportunity to quantify the impact of AI and automation technologies in accelerating scientific discovery. In our collaboration, we demonstrate a pipeline to perform iterative design cycles where generative models suggest candidates that are synthesized with RoboRXN and screened with Ulysses. The data produced by Ulysses will then be used to establish a feedback loop to retrain the generative AI and improve the proposed leads in a completely data-driven fashion.”