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TAIJI: Approaching Experimental Replicates-Level Accuracy for Drug Synergy Prediction

By December 5, 2018Research

MIDAS-affiliated researchers recently published a paper on accurate and fast computational tools for predicting drug synergistic effects.

Title
TAIJI: Approaching Experimental Replicates-Level Accuracy for Drug Synergy Prediction

Published in
Bioinformatics, November 21, 2018

DOI
10.1093/bioinformatics/bty955

Authors
Hongyang Li, Shuai Hu, Nouri Neamati, Yuanfang Guan

Abstract

Motivation

Combination therapy is widely used in cancer treatment to overcome drug resistance. High-throughput drug screening is the standard approach to study the drug combination effects, yet it becomes impractical when the number of drugs under consideration is large. Therefore, accurate and fast computational tools for predicting drug synergistic effects are needed to guide experimental design for developing candidate drug pairs.

Results

Here, we present TAIJI, a high-performance software for fast and accurate prediction of drug synergism. It is based on the winning algorithm in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is a unique platform to unbiasedly evaluate the performance of current state-of-the-art methods, and includes 160 team-based submission methods. When tested across a broad spectrum of 85 different cancer cell lines and 1089 drug combinations, TAIJI achieved a high prediction correlation (0.53), approaching the accuracy level of experimental replicates (0.56). The runtime is at the scale of minutes to achieve this state-of-the-field performance.