Drugs are typically small organic molecules that achieve their desired activity by binding to a target site on a receptor. The first step in the discovery of a new drug is usually to identify and isolate the receptor to which it should bind, followed by testing many small molecules for their ability to bind to the target site. This leaves researchers with the task of determining what separates the active (binding) compounds from the inactive (non-binding) ones. Such a determination can then be used in the design of new compounds that not only bind, but also have all the other properties required for a drug (solubility, oral absorption, lack of side effects, appropriate duration of action, toxicity, etc.). The goal of this competition is to allow you to develop predictive models that can determine, given a particular compound, whether it is active (1) or not (0). As such, the goal would be develop the best binary classification model. A molecule can be represented by several thousands of binary features which represent their topological shapes and other characteristics important for binding. Since the dataset is imbalanced the scoring function will be the F1-score instead of Accuracy.

1. Used SMOTE over sampler for imbalanced data.
2. Used Truncated SVD for reducing dimensions.
3. Used Decision tree classifier with weights attached to classes.