Standigm AI is a artificial intelligence-powered drug development system, which saves time and cost.  Standigm AI focuses on predicting new indications for existing drugs, called drug repositioning through deeply trained AI models with molecular features of drug responses and drug uses.

There are 5 sub-projects using Standigm AI system. 

AZ Challenge: Predicting synergetic drug combinations

The goal of the project is to predict synergetic drug combinations that effectively kill cancer cells. The disease context, the characteristics of drugs, and the interaction between drugs and diseases are described using various chemical properties of drugs, their target proteins/signaling pathways of drugs, monotherapy drug response data and genomic data for cell lines, among others. Based on these, three types of input features were designed (cell line-specific, drug-specific, and drug-cell line interaction features). With various combinations of these features and different learning parameters, an ensemble of gradient boosting classifiers was built. This ranked 3rd among 71 teams worldwide in "AstraZeneca-Sanger Drug Combination Prediction Dream Challenge (2015~2016)". The methods were applied to precision medicine, patient stratification, and therapeutic/diagnostic design.

Learning drug-perturbed data

The goal of the project is to predict new indications for existing drugs, called drug repositioning from the features of drug responses and drug uses discovered via deep learning. Here, the drug responses and uses are described by the data of drug-perturbed gene expressions and of therapeutic usage labels. We have been developing various multimodal learning methods (XGBoost, Compact Bilinear Pooling, etc.) for robust feature learning. These models suggest 20 hits with new drug indications. For two of the 20 drug repositioning hits, literature has shown their experimental validations. For the remaining hits, Standigm is designing in-vivo and in-vitro experimental validations to demonstrate Standigm AI’s prediction power. The drug repositioning AI will aid in discovering hidden relational patterns of drugs and indications, ultimately saving the cost of drug development. 

Building & Learning biological knowledge

The goal of the project is to predict the missing relationships between proteins, drugs, and diseases by learning from a biological knowledge, GraphDB. We constructed the biological GraphDB consisting of nodes (32,373 proteins, 7815 drugs, 3721 diseases, etc.), edges (55,618 drug-disease edges, 125,686 drug-protein edges, etc.) and using knowledge from various open and private sources. Users can easily access and visualize the GraphDB via web browsers and navigate the DB using the Cypher query language. We are developing models to predict unknown links between the nodes (proteins, drugs, disease etc.) in the GraphDB. The models will be applied to finding new drug indications, new drug targets, and new disease biomarkers.

Drug Target Prediction

The goal of the project is to select reliable targets of drugs by prediction of the binding affinity of drugs to proteins based on machine learning approaches. 13,308 protein-ligand complex structures with binding affinity values are learned using the combination of various featurization methods and learning algorithms. The inputs of models are experimental protein-drug complex structures or structures generated by docking simulation. The best targets are selected by the ensemble of scoring functions of different models. The methods will be applied to finding new targets(proteins) or the best drug for the protein. 

EMR data representation

The goal of the project is to develop intelligent methods for patient and medical record representation learning, and ultimately predict clinical events such as patient diagnosis, prognosis, or medication categories. We collaborate with Ajou University School of Medicine, Korea. This research is supported by a grant of the Korea Health Technology R&D Project, “Development of the Artificial Intelligence supporting a Clinical Trial System (AI-CTS)”

Standigm AI removes the traditional guesswork from data analysis by using an AI system that automatically examines whole biomedical databases to learn what is hiding just out of sight. By applying the state-of-the art machine learning technology to real data, Standigm AI helps meet the Decent Work and Economic Growth goal. It presents the data that are most pertinent to the treatment of diseases, saving time and money, shortening the development cycle. The Standigm AI project also helps promote the Good Health and Well-being goal by developing new drug candidates that are primed for success.