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Background: Digital design, assisted by data science, experienced significant progress over recent years within the continuous manufacturing in the pharmaceutical sector. Research Objective: The project focuses the fundamental research on robust numerical and visual performance indicators for assessing performance for many-objective optimisation algorithms under multiple constraints Methods: A surrogate model-based machine learning algorithm is used, to train data-driven models that capture the manufacturing process behaviour. Then use optimisation algorithms to get optimal solutions. Results: >75% dissolution release could be achieved in 45 minutes.

The CMAC Digital Twin for medicines development and manufacture incorporates the data, models and knowledge that describe the materials, products, and processes delivering the required product attributes.

We report on the use of a single, unified dataset for machine learning (ML)-driven solubility prediction across the chemical space. This was a departure from the solvent-specific datasets more commonly used.

© 2017 American Chemical Society. IKKβ plays a central role in the canonical NF-kB pathway, which has been extensively characterized. The role of IKKα in the noncanonical NF-kB pathway, and indeed in the canonical pathway as a complex with IKKβ, is less well understood. One major reason for this is the absence of chemical tools designed as selective inhibitors for IKKα over IKKβ. Herein, we report for the first time a series of novel, potent, and selective inhibitors of IKKα. We demonstrate effective target engagement and selectivity with IKKα in U2OS cells through inhibition of IKKα-driven p100 phosphorylation in the noncanonical NF-kB pathway without affecting IKKβ-dependent IKappa-Bα loss in the canonical pathway. These compounds represent the first chemical tools that can be used to further characterize the role of IKKα in cellular signaling, to dissect this from IKKβ and to validate it in its own right as a target in inflammatory diseases.

The Open Source Malaria (OSM) consortium is developing compounds that kill the human malaria parasite, Plasmodium falciparum, by targeting PfATP4, an essential ion pump on the parasite surface. The structure of PfATP4 has not been determined. Here, we describe a public competition created to develop a predictive model for the identification of PfATP4 inhibitors, thereby reducing project costs associated with the synthesis of inactive compounds. Competition participants could see all entries as they were submitted. In the final round, featuring private sector entrants specializing in machine learning methods, the best-performing models were used to predict novel inhibitors, of which several were synthesized and evaluated against the parasite. Half possessed biological activity, with one featuring a motif that the human chemists familiar with this series would have dismissed as "ill-advised". Since all data and participant interactions remain in the public domain, this research project "lives" and may be improved by others.

The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the problem of increasing resistance to our frontline treatments. The Open Source Malaria (OSM) consortium has been developing compounds ("Series 4") that have potent activity against Plasmodium falciparum in vitro and in vivo and that have been suggested to act through the inhibition of PfATP4, an essential membrane ion pump that regulates the parasite’s intracellular Na+ concentration. The structure of PfATP4 is yet to be determined. In the absence of structural information about this target, a public competition was created to develop a model that would allow the prediction of anti-PfATP4 activity among Series 4 compounds, thereby reducing project costs associated with the unnecessary synthesis of inactive compounds.In the first round, in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably, all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition was undertaken, in 2019, again with freely-donated models that other participants could see. The best-performing models from this second round were used to predict novel inhibitory molecules, of which several were synthesised and evaluated against the parasite. One such compound, containing a motif that the human chemists familiar with this series would have dismissed as ill-advised, was active. The project demonstrated the abilities of new machine learning methods in the prediction of active compounds where there is no biological target structure, frequently the central problem in phenotypic drug discovery. Since all data and participant interactions remain in the public domain, this research project “lives” and may be improved by others.