Quantum Leap Advancements in In-Silico Drug Design (QIDD)
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".
Deadline for manuscript submissions: 30 April 2025 | Viewed by 71
Special Issue Editors
Interests: biophysical chemistry; drug repurposing and molecular modeling; computational chemistry; quantum computing; materials and multi-scale modeling
Special Issues, Collections and Topics in MDPI journals
Interests: structure-based drug design; protein-protein docking, molecular dynamics; targeted drug delivery; nanoparticles
Interests: biomedical informatics; multi-omics; machine learning; quantum computing machine learning; drug design; molecular property prediction; precision medicine
Special Issues, Collections and Topics in MDPI journals
Interests: bioinformatics; computational physics; computational chemistry; quantum computing; drug design; AI drug; protein dynamics; personalized medicine; high pressure physics; energetic materials
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapidly evolving landscape of in silico drug design has witnessed unprecedented advancements that are transforming the approach to drug discovery and development. The Special Issue titled “Quantum Leap Advancements in In-Silico Drug Design (QIDD)” aims to showcase cutting-edge research and technological innovations that represent significant leaps forward in the field. One of the foremost advancements in this arena is the integration of artificial intelligence (AI) techniques. Machine learning and AI have become indispensable tools for predicting molecular properties and drug–target interactions. These technologies enhance the accuracy and efficiency of identifying potential therapeutic candidates by analyzing vast datasets to uncover patterns and relationships that traditional methods might lack. Specifically, the use of generative models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), has revolutionized the design of novel drug-like molecules. These models can generate new compounds with desired properties, significantly accelerating the drug discovery process. Moreover, quantum computing enables the precise prediction of binding affinities, novel molecule design, and detailed analyses of drug reactions by optimizing electronic structures and interactions with biological targets.
Multi-target docking is another critical development in QIDD that this Special Issue will explore. Traditional drug discovery often focuses on single-target interactions, but multi-target docking allows researchers to identify compounds that can interact with multiple targets simultaneously. This approach not only enhances therapeutic efficacy but also reduces the likelihood of side effects by targeting various pathways involved in different therapeutic interventions. Understanding the metabolic pathways influenced by potential drugs is essential for developing effective and safe medications. The utilization of metabolomics data in QIDD provides valuable insights into these pathways, aiding in the identification of biomarkers and the optimization of drug candidates. This holistic understanding of drug metabolism and its impact on the body can lead to more precise and personalized treatments. Incorporating diverse chemical databases into QIDD is another innovative approach highlighted in this Special Issue. By expanding virtual libraries to include a wide range of bioactive molecules from both synthetic chemicals and natural products, researchers can increase the chances of discovering novel therapeutic interventions. Natural products offer a rich source of unique compounds with potential therapeutic benefits. The scalability of virtual screening has been significantly enhanced using cloud computing platforms, which allow researchers to perform large-scale virtual screenings efficiently. High-performance computing (HPC) further complements this by providing the computational power needed to handle and analyze large datasets swiftly, making it feasible to conduct comprehensive virtual screenings and prioritize the most viable compounds for further development.
This Special Issue will unite leading researchers and practitioners to discuss these innovative approaches and their impact on pharmaceutical discovery and development. It will showcase the latest applications of QIDD, providing a comprehensive overview of current trends, challenges, and future directions. The integration of artificial intelligence, machine learning, multi-omics, diverse chemical libraries, cloud computing, and high-performance computing represents a convergence of technologies poised to revolutionize drug discovery. This Special Issue aims to highlight these transformative technologies and foster collaboration and innovation among researchers dedicated to QIDD.
Prof. Dr. Gilles Peslherbe
Dr. Satyavani Kaliamurthi
Dr. Gurudeeban Selvaraj
Prof. Dr. Dongqing Wei
Guest Editors
Manuscript Submission Information
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Keywords
- bioactivity and toxicity prediction
- computer-aided drug design
- cloud computing
- design of novel drug-like molecules
- high-performance computing (HPC)
- quantum computing
- drug delivery
- machine learning and molecular properties prediction
- multi-target docking
- variational autoencoders (VAEs) and generative adversarial networks (GANs)
- virtual screening and molecular dynamics simulations
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