Abstract
BackgroundThe ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded by the wide range of data platforms and data scarcity.ResultsIn this paper, we modeled the prediction of drug-target interactions as a binary classification task. Using transcriptome data from the L1000 database of the LINCS project, we developed a framework based on a deep-learning algorithm to predict potential drug target interactions. Once fully trained, the model achieved over 98% training accuracy. The results of our research demonstrated that our framework could discover more reliable DTIs than found by other methods. This conclusion was validated further across platforms with a high percentage of overlapping interactions.ConclusionsOur model’s capacity of integrating transcriptome data from drugs and genes strongly suggests the strength of its potential for DTI prediction, thereby improving the drug discovery process.
Highlights
The ability to predict the interaction of drugs with target proteins is essential to research and development of drug
In this study, supported by increased availability of GPU computing and expanded data sources, we explored the possibility of deep learning method to discovery new drug-target interactions (DTIs) based on transcriptome data from drug perturbation and gene knockout trials in the L1000 database
Inspired by the intrinsic nonlinear patterns revealed by the Library of Integrated Network-based Cellular Signatures (LINCS) project, we proposed a framework that offers better prospects for inferencing and for DTI prediction [12]
Summary
The ability to predict the interaction of drugs with target proteins is essential to research and development of drug. The traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded by the wide range of data platforms and data scarcity. The identification of drug-target interactions (DTIs) is significant to drug research and development (R&D). The ability to predict DTIs has been applied widely for drug repositioning and for anticipating adverse reactions [1, 2]. High-throughput screening technology is available, the traditional strategy used for discovering new DTIs is still time consuming and costly. Researchers have developed a variety of computational algorithms to facilitate the prediction of DTIs. For example, Campillos et al proposed an algorithm to predict
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