Cheminformatics in Drug Discovery and Material Design

Dear Colleagues,

Cheminformatics is a rapidly advancing interdisciplinary field that plays a crucial role in drug and material discovery and development. The use of computational methods and tools has revolutionized the process of drug discovery by enabling efficient identification of potential drug targets and lead compounds. Cheminformatics tools can predict the properties of a new molecule based on its chemical structure, reducing the need for time-consuming and expensive laboratory experiments. These tools can also help optimize drug potency and reduce toxicity by predicting how a drug interacts with its target in the body. Similarly, cheminformatics can be used for the design of new materials with specific functional properties, such as catalytic activity or mechanical, optical, thermal and electronic properties, as required for particular applications. Cheminformatics has significantly contributed to accelerating the drug discovery and material design processes, ultimately leading to the development of more effective and efficient drugs and materials and reducing the risk of unintended toxicities. As such, it is essential to continue exploring and advancing the field to tackle some of the most significant societal challenges. This Special Issue aims to bring together researchers from various disciplines to present their latest findings and discuss future directions in cheminformatics research.

Specific topics include but are not limited to the following:

• Development of novel machine learning algorithms for predicting drug activity and toxicity.
• Application of deep learning techniques to analyze large-scale chemical datasets for drug or materials discovery.
• Development of new computational tools for material property and function prediction and design, including the enhancement of safety and sustainability.
• Integration of molecular docking and molecular dynamics simulations for optimizing drug–protein and material–protein (biomolecule) interactions.
• Application of cheminformatics approaches for the identification of drug repurposing candidates.
• In silico models and tools for material design for dual use.
• Development of quantitative structure–activity relationship (QSAR) models for predicting drug and material properties.
• Use of molecular descriptors and fingerprints for similarity analysis and virtual screening of chemical and material libraries.
• Database development and FAIRification of data.

We welcome publications in all the above related fields and beyond and hope that this Special Issue will serve as a valuable reference and help stimulate advances in the field.

Dr. Georgia Melagraki
Guest Editor

Manuscript Submission Information

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• chemoinformatics
• nanoinformatics
• machine learning
• quantitative structure activity relationship (QSAR)
• computer-aided drug design (CADD)
• materials modeling
• chemical databases