Computational Methods for Drug Discovery and Biological Big Data Analysis

Dear Colleagues,

This Special Issue, “Computational Methods for Drug Discovery and Biological Big Data Analysis”, aims to bring together innovative research at the intersection of mathematical theory, computational methods, and intelligent systems for both drug discovery and the analysis of large-scale biological data. Recent advances in artificial intelligence, mathematical algorithms, and high-throughput biological data analytics have presented new opportunities to address fundamental challenges in pharmaceutical research and complex biological systems.

We welcome contributions focusing on drug target prediction, drug response modeling, drug combination analysis, and drug repurposing, as well as studies leveraging biological big data—such as multi-omics integration, sequence data analysis, and large-scale biological networks—to generate new biological insights. The Special Issue also highlights the emerging roles of non-coding RNAs and biomarker discovery, where computational models and machine learning provide powerful tools for biological interpretation and clinical translation. Studies integrating applied mathematics—such as optimization, probabilistic modeling, and network analysis—with modern computing frameworks are particularly encouraged.

By gathering interdisciplinary works from mathematics, computer science, bioinformatics, and biomedical engineering, this Special Issue seeks to advance methodological innovation and strengthen collaborations that accelerate discovery in drug development and biological big data analysis. We invite high-quality original research contributing to intelligent, efficient, and interpretable computational solutions for complex biomedical challenges.

Dr. Chunchun Wang
Guest Editor

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