DEEP LEARNING FOR DRUG DISCOVERY AND CANCER RESEARCH: AUTOMATED ANALYSIS OF VASCULARIZATION IMAGES

Abstract

Likely drug candidates which are identified in traditional pre-clinical drug screens often fail in patient trials, increasing the societal burden of drug discovery. A major contributing factor to this phenomenon is the failure of traditional in vitro models of drug response to accurately mimic many of the more complex properties of human biology. We have recently introduced a new microphysiological system for growing vascularized, perfused microtissues that more accurately models human physiology and is suitable for large drug screens. In this work, we develop a machine learning model that can quickly and accurately flag compounds which effectively disrupt vascular networks from images taken before and after drug application in vitro. The system is based on a convolutional neural network and achieves near perfect accuracy while committing potentially no expensive false negatives.

Existing System

Extracellular matrix (ECM)

Proposed System

In this paper, we develop a convolutional neural network to automatically classify images of vasculature networks formed in our MPS into no-hit, soft-hit, and hard-hit categories. The accuracy of our best model is significantly better than our minimally-trained human raters and requires no human intervention to operate. This model is a first step toward automation of data analysis for high-throughput drug screening. Alternative examples of applications of machine learning in drug discovery can be found in, for instance. Most of these applications use machine learning models to predict drug-related properties of small molecules such as binding affinity, toxicity, and solubility.

Architecture

BLOCK DIAGRAM