Current Computer-Aided Drug Design
Aims and Scope:
Current Computer-Aided Drug Design (CCADD) is an interdisciplinary journal (or theme/initiative) dedicated to publishing high-quality research at the intersection of computational chemistry, artificial intelligence, and pharmaceutical sciences. The journal aims to accelerate drug discovery by integrating traditional computer-aided drug design (CADD) techniques with cutting-edge artificial intelligence-driven drug design (AIDD) methodologies.
Topics of interest include, but are not limited to:
- AI/ML algorithms for hit identification, optimization, and target prediction
- Deep learning, generative models for de novo drug design
- Multi-omics integration, pharmacogenomics and AI for target identification
- AI-enhanced molecular dynamics and binding free energy calculations
- Structure- and ligand-based drug design
- AI-assisted repurposing of existing drugs and natural products
Current Computer-Aided Drug Design is an international, peer-reviewed journal in all aspects of drug design based on computational techniques, published bimonthly (print & online) by Bentham Science Publishers. Read more...
Editor-in-Chiefcasc:
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Dong-Qing WeiState Key Laboratory of Microbial Metabolism and College of Life Sciences and Biotechnology
Shanghai Jiaotong University
Shanghai
China
ISSN: 1573-4099 (Print)
eISSN: 1875-6697 (Online)
Special Issues With Active Call for Papers
Explainable AI and Expert System Integration for Precision Drug Design
The rapid evolution of drug discovery demands intelligent frameworks that balance predictive accuracy, interpretability, and domain expertise. Traditional computational methods in drug design, such as molecular docking, QSAR modeling, and virtual screening, often struggle to capture the complex biological interactions underlying drug efficacy and toxicity. At the same time, modern machine learning and deep learning approaches, despite their remarkable performance, are frequently criticized for their “black-box” nature, which limits their acceptance in safety-critical domains such... see more
Computer-aided Endoscopy and Colonoscopy Diagnostics (CAD) for Medical Instruments: Data Insights for Prediction and Classification in Medical Imaging
The increasing prevalence of gastrointestinal diseases, particularly colorectal cancer, highlights the critical role of endoscopy and colonoscopy in early detection and diagnosis. These procedures rely on the ability to interpret real-time visual and diagnostic data accurately and efficiently. However, the growing complexity of medical imaging data, coupled with the need for timely decision-making, presents significant challenges for healthcare professionals. This special issue addresses these challenges by focusing on real-time data processing, predictive analytics, and machine... see more
Computational Design of Plant-Based Therapeutics
Plant-based therapeutics have a long history of use in traditional medicine and are gaining renewed interest due to their potential for treating various diseases, including cancer, diabetes, and neurodegenerative disorders. However, the traditional approach to plant-based drug discovery is often time-consuming and resource-intensive. Computational methods offer a powerful and efficient approach to accelerate the discovery and development of plant-based therapeutics. By leveraging the power of computers, researchers can: • Reduce the time and cost associated... see more
Computer-Aided Drug Discoveries for Emerging Diseases
Computer-aided drug design is a rapidly growing research field that continues to gain momentum, attracting increasing interest from the scientific community. This trend is largely driven by the growing utilization of machine learning and artificial intelligence in drug design and discovery. Artificial Intelligence has proven efficacy across various applications, including the prediction of physical-chemical properties and biological activities of small molecules, in silico assessment of absorption, distribution, metabolism, and excretion and toxicological (ADMET) profiles of... see more
Artificial Intelligence in Biomedical Research: Enhancing Data Analysis for Drug Discovery and Development
This thematic issue highlights the transformative impact of Artificial Intelligence (AI), with a particular focus on Machine Learning (ML) and Deep Learning (DL) techniques, in advancing biomedical research. As a result of these cutting-edge AI methodologies, drug discovery and development are revolutionizing data analysis. Rapid advances in artificial intelligence technologies present unprecedented opportunities for accelerating drug discovery, optimizing therapeutic strategies, and enhancing treatment effectiveness. Researchers and clinicians can improve the precision and speed of identifying... see more
Deep Learning Approaches in Bioinformatics for Computer-Aided Drug Development Targeting Brain Tumors
The integration of deep learning and bioinformatics is revolutionizing the field of computer-aided drug development, particularly in the fight against brain tumors one of the most aggressive and lethal types of cancer. Brain tumors present significant challenges due to their heterogeneity and complexity, which require novel approaches for early diagnosis and personalized treatment. By leveraging deep learning algorithms, researchers can extract valuable insights from vast and complex bioinformatics data, identifying novel therapeutic targets and accelerating... see more
Emerging Trends in Computer-Aided Drug and Healthcare Solutions
This special issue aims to further the advancement of knowledge in drug design, healthcare innovations, and medical solutions by leveraging modern computational techniques. In recent years, computer-aided drug design has transformed the approach to medicinal chemistry, accelerating the drug discovery process and fostering innovation. The goal of this issue is to showcase pioneering research that highlights the application of computational methods, including molecular modeling, machine learning, deep learning, and bioinformatics, in the design and development... see more