Optibrium has published a peer-reviewed study in Applied AI Letters, “Deep Imputation on Large-Scale Drug Discovery Data”, working alongside Takeda Pharmaceuticals’ proprietary global dataset and the Alchemite deep learning method.
The study demonstrated that deep learning generates new and valuable insights on global pharma-scale, high-value and proprietary datasets. Such datasets are complex, with data deriving from many different experiments, including compound activities in biochemical and phenotypic assays, high-throughput screening data and absorption, distribution, metabolism, elimination, and toxicity (ADMET) endpoints.
Making the best decisions on project progression on such data is further complicated by the fact that most potential drug compounds are measured in only a small subset of experiments that pharmaceutical and biotech companies routinely use, resulting in datasets where only a few percent of the possible measurements have been made. Furthermore, measurements are noisy due to the complexity of biological experiments. While these characteristics limit the effectiveness of most machine learning methods, the study confirmed that Augmented Chemistry provided valuable insights on such challenging data.
The study also found that deep learning imputation made more accurate predictions of compounds’ biological properties, including prospective prediction of compound activities in the context of projects. In particular, it showed substantial advantages in predicting complex endpoints, such as cell-based assays, that are resource-intensive and where more accurate predictions result in substantial time and cost reductions.
Following on from a previous study, which demonstrated the effectiveness of deep learning imputation on smaller project-specific datasets, this new study showed that the same method scales to global pharma datasets. The described model was built on 1.8 million data points relating to approximately 700,000 compounds and 1,200 experimental endpoints. When applied on this scale, the insights into high-value compounds and research strategies increase exponentially.
Matthew Segall, CEO at Optibrium, said: “This study corroborates the tremendous results we have seen in many collaborations with pharma, biotech and not-for-profit organisations. We are excited to see the significant benefits our AI technology is producing and the enthusiasm for its uptake in the pharma community.”
