Imputation of sensory properties using deep learning
With Optibrium and IFF | Now published in the Journal of Computer-Aided Molecular Design
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Alchemite™ machine learning identifies potential antimalarial drug compound
With Optibrium and Open Source Malaria | Now published in the Journal of Medicinal Chemistry
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Materials and process development with Lucideon
Lucideon – materials and process development | Supporting simulation and experiment
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Material and component design with NASA
NASA – material and component design | Alchemite™ gives accurate predictions
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Design of automotive catalysts at Johnson Matthey
Johnson Matthey – automotive catalysts | Reduced the amount of experiment required
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Optimising kinase profiling programs with Optibrium and Genentech
Optibrium and Genentech - drug discovery | Accurate models despite sparse data
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Improving materials data quality at Matmatch
Matmatch – specialist scientific data | Added value by gap filling and quality checking data
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Data-driven additive manufacturing with AMRC and Boeing
AMRC and Boeing - process parameters for AM | Reduce experimentation
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Improving hardfacing consumables with Welding Alloys Group
Welding Alloys Group – hardfacing materials | Improved cost, performance, eco impact
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Predicting pharmacokinetics with Optibrium and AstraZeneca
Optibrium and AstraZeneca - drug discovery | Successfully predicts pharmacokinetics
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Formulation design with Domino Printing Sciences
Domino Printing Sciences – ink formulation | Maximised value from lab resources
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New alloys for additively manufactured heat exchangers with GKN Aerospace
GKN Aerospace - alloy design | Virtual experiments deliver results in seconds
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