First look: Guide your compound design strategy with new visual, industry-leading affinity predictions
Accurate predictions of binding affinity are the holy grail of early-phase discovery, enabling teams to significantly reduce the synthesis and…
Want to avoid late-stage failure of your drug candidates? Understanding the metabolism of your compounds is crucial.
In silico modelling provides an early opportunity to spot problems, such as potential highly reactive sites of metabolism, or possible toxic metabolites which could be formed, and resolve them by guiding the design of improved compounds. Traditionally, predictive modelling has targeted human Cytochrome P450s, a key enzyme family involved in drug metabolism. However, P450s aren’t the only enzymes to consider; there is a whole range which should be evaluated to reduce risks of unexpected drug metabolism, including additional phase I enzymes, such as AO and FMO, and phase II enzymes including UGT and SULT.
Watch Optibrium CEO Matt Segall and Principal Scientist Mario Öeren as they explore ground-breaking new quantum mechanics and machine learning models which go beyond P450s and provide insights on a broad range of enzymes involved in drug metabolism.
Watch as they discuss:

Matt has a Master of Science in computation from the University of Oxford and a PhD in theoretical physics from the University of Cambridge. As Associate Director at Camitro (UK), ArQule Inc. and then Inpharmatica, he led a team developing predictive ADME models and state-of-the-art intuitive decision-support and visualization tools for drug discovery. In January 2006, he became responsible for management of Inpharmatica’s ADME business, including experimental ADME services and the StarDrop software platform. Following acquisition of Inpharmatica, Matt became Senior Director responsible for BioFocus DPI’s ADMET division and in 2009 led a management buyout of the StarDrop business to found Optibrium.
Mario is a Principal Scientist at Optibrium. He has a background in computational chemistry, with a PhD in Natural Sciences from Tallinn University of Technology, where he has also held roles as a Lecturer and Research Assistant. Since joining Optibrium in 2017, Mario has led much of the company’s research and development efforts into metabolism prediction, developing new models based on quantum mechanics and machine learning for predictive modelling.
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