Akash Surendran

This study evaluates contrastive learning-based approaches for activity cliff prediction in drug discovery, finding they improve embedding separation but have dataset-dependent accuracy impacts.

This study examines how undersampling techniques affect hyperparameter selection for non-linear dimensionality reduction methods in chemical space visualization, showing reduced computational costs and improved local structure preservation.

The paper introduces a method to visualize chemical space by projecting molecular data onto a lower-dimensional space using linear methods, outperforming non-linear techniques in efficiency and neighborhood preservation.

The paper introduces methods to identify and avoid activity cliffs in drug design using the BitBIRCH clustering algorithm.

The paper demonstrates how to generate a metric consistent with Tanimoto similarity and explores its properties and relationships with other similarity measures.