Site Risk prediction for Drug Discovery

Solution

Sentienz proposed a prediction model by performing the following…

• Feature analysis – Resulting in the best feature list to create the prediction model by performing advanced feature engineering.
• Created detailed models and analyzed results and outputs, to evaluate and re-evaluate the metrics and identify optimum outcomes
• Stacked Ensemble model combining various Deep learning (DL) and Machine learning (ML) Models
• Principal Component analysis to understand the spread of training and test data.
• Provide explanation of how the predictions are done listing top 10 explanations along with their impact rating.
• Applied Deep Learning models with complex network architectures.
• Performed detailed False Positive (FP) and False Negative (FN) analysis to maximize recall / sensitivity, allowing for maximum accuracy with a minimal impact on the business.