Arctoris Ltd, a tech-enabled biopharma platform company, has appointed three globally recognized experts in Alzheimer’s disease, Machine Learning applied to closed loop discovery, and automated chemistry as members of its Scientific Advisory Board: Professor John Davis (University of Oxford), Professor Rafael Gómez Bombarelli (MIT), and Dr Teodoro Laino (IBM Research).
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Joining Arctoris as scientific advisors are Rafael Gómez Bombarelli (MIT), John Davis (University of Oxford) and Teodoro Laino (IBM) (left to right) (Photo: Business Wire)
“We are delighted to see Professor Davis, Professor Gómez Bombarelli and Dr Laino join our company’s Scientific Advisory Board at this exciting moment in Arctoris’ growth,” said Martin-Immanuel Bittner MD DPhil FRSA FIBMS, CEO and Co-Founder of Arctoris. “Professor Davis is a world-renowned expert in dementia research, while Professor Gómez Bombarelli and Dr Laino are pioneers in chemical computation and accelerated discovery. These two are key areas for our technology and our pipeline development, and we are grateful for the support of these highly distinguished individuals.”
John Davis is the Chief Scientific Officer of the Centre for Medicines Discovery (Oxford) and Director of Business Development for the Alzheimer’s Research UK - Drug Discovery Alliance. He has over 25 years of drug discovery expertise all the way from target identification to successful clinical proof of concept for a range of drug candidates in neurological disorders. Following postdoctoral training at the Ludwig Institute and the Salk Institute, he joined GlaxoSmithKline where he led a variety of non-clinical pharmacology research departments for pain and neurodegenerative diseases. In 2010, Professor Davis co-founded the spinout company Convergence Pharmaceuticals, which he later left to become Director of Discovery for Selcia, and CSO and cofounder of Cypralis before joining the University of Oxford.
Rafael Gómez Bombarelli is the Jeffrey Cheah Assistant Professor in Engineering in MIT’s Department of Materials Science and Engineering. His research is focused on accelerated discovery cycles and machine learning approaches for molecular design and optimisation. Professor Gómez Bombarelli’s work has been published in journals such as Science, Nature Chemistry, and Nature Materials, and has been featured in MIT Technology Review and Wall Street Journal. He earned a BS, MS and PhD in chemistry from the Universidad de Salamanca, followed by postdoctoral work at Heriot-Watt University, Harvard University and Kyulux North America before taking up his post at MIT.
Teodoro Laino leads the chemical computation and automated synthetic chemistry efforts at the Department of Cognitive Computing and Industry Solutions at the IBM Research Zurich Laboratory. He is interested in the application of machine learning to chemistry and materials science problems with the purpose of developing scalable, tech-enabled solutions to significantly improve chemical synthesis (e.g., IBM RXN for chemistry). A chemist by background, Dr Laino has a PhD in computational chemistry, after which he worked as a post-doctoral researcher at the University of Zurich developing algorithms for molecular dynamics simulation.
New member of the Scientific Advisory Board John Davis, said, “Neurodegenerative diseases and especially Alzheimer’s Disease are an area of significant unmet clinical need. As a company, Arctoris’ strategy and focus for the development of its pipeline of assets is governed by the very best scientific and clinical evidence in the field, and I am pleased to support their scientific and leadership team with therapeutic area expertise.”
“Finding efficient ways to speed up the Design-Make-Test-Analyse cycle is crucial for the rapid development of new and improved materials or treatments. Together with Arctoris, my team at IBM Research has spent the past two years exploring potential integrations between our own IBM Research Accelerated Discovery Platform and Arctoris’ own flagship technology, Ulysses. Today, I am thrilled to announce that I will be serving as an expert on automated synthesis and machine learning modelling in chemistry in the Scientific Advisory Board of Arctoris, further strengthening our mutually beneficial collaboration,” said Teodoro Laino.
“My research focuses on accelerated discovery and molecular design optimization, and I am convinced that Arctoris’ Ulysses platform and their approach to combining wet lab and dry lab approaches are the future for small molecule drug discovery. I am excited to contribute my expertise in closed loop and machine learning approaches to accelerated scientific discovery, where Arctoris is building a truly unique platform and company,” said Rafael Gómez Bombarelli.
ABOUT ARCTORIS LTD
Arctoris is a tech-enabled biopharma platform company founded and headquartered in Oxford, UK with its US operations based in Boston and its Asia-Pacific operations based in Singapore. Arctoris combines robotics and Machine Learning with a world-class team for accelerated small molecule discovery. Ulysses, the unique technology platform developed by Arctoris, enables the company and its partners to conduct their R&D – from target to hit, lead, and candidate – significantly faster, and with considerably improved data quality and depth. The company's end-to-end automation platform is capable of generating large and precise datasets across hundreds of experiment types and assays. The resulting data assets are captured and passed through automated analytical pipelines and feed directly into Arctoris’ Machine Learning capabilities, creating powerful predictive models capable of identifying superior molecules faster. Bringing together the expertise of seasoned biotech and pharma veterans with its proprietary technologies, Arctoris leads to higher success rates and an accelerated progression of programs towards the clinic. Arctoris pursues an internal pipeline of assets in oncology and neurodegeneration and also collaborates with select biotech and pharma partners in the US, Europe, and Asia-Pacific, including several Top 10 Pharma.
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