Author:
Liu Youzhi,Xing Linlin,Zhang Longbo,Cai Hongzhen,Guo Maozu
Abstract
AbstractPredicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Reference52 articles.
1. Bolten, B. M. & DeGregorio, T. Trends in development cycles. Nat. Rev. Drug Discov. 1, 335 (2002).
2. van der Schans, S. et al. The impact of patent expiry on drug prices: Insights from the Dutch market. J. Mark. Access Health Policy 9, 1849984 (2021).
3. Martens, E. & Demain, A. L. The antibiotic resistance crisis, with a focus on the united states. J. Antibiot. 70, 520–526 (2017).
4. Mittal, P., Chopra, H., Kaur, K. P. & Gautam, R. K. New drug discovery pipeline. In Computational Approaches in Drug Discovery, Development and Systems Pharmacology, 197–222 (Elsevier, 2023).
5. Khot, S., Naykude, S. & Adnaik, P. An overview of drug drug development process. J. Pharma Insights Res. 1, 067–074 (2023).
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