Leo Gendelev

Deep phenotypic profiling of neuroactive drugs in larval zebrafish

The paper trains a deep metric learning model on zebrafish behavioral profiles to identify neuroactive compounds, achieving better performance than correlation distance and validating predictions with in vitro assays.

A high-throughput phenotypic screen combined with an ultra-large-scale deep learning-based virtual screening reveals novel scaffolds of antibacterial compounds

This study combines high-throughput screening with deep learning to identify novel antibacterial compounds, achieving a 90X higher hit rate than traditional methods.

Simultaneous classification of neuroactive compounds in zebrafish

This paper introduces an open platform for classifying neuroactive compounds in zebrafish using phenotypic responses and provides a large dataset of drug effects.

A Simple Representation Of Three-Dimensional Molecular Structure

This paper introduces a 3D molecular representation (E3FP) that improves drug-to-target prediction compared to 2D fingerprints, with experimental validation of novel binding predictions.