Computer-aided nanodrug discovery: recent progress and future prospects

Area of Science:

• Biomedical Engineering
• Computational Chemistry
• Nanotechnology

Background:

• Nanodrugs offer advantages over conventional drugs, addressing limitations like poor targeting and high toxicity.
• Developing nanodrugs with specific biomedical functions requires precise optimization of complex nanomaterial properties.
• Current challenges include predicting nanomaterial behavior and designing personalized nanomedicines before experimental validation.

Purpose of the Study:

• To review computational advances in nanodrug discovery.
• To highlight the role of interfacial interactions in nanodrug efficacy.
• To discuss the potential of integrated computational and experimental strategies for accelerating precision nanodrug development.

Main Methods:

• Utilizing in silico methods to understand nanodrug functions based on physicochemical properties.
• Employing machine learning techniques to analyze bio-nano interactions.
• Summarizing computational approaches for studying key interfacial interactions: surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion.

Main Results:

• In silico methods and machine learning significantly accelerate nanodrug research and understanding of bio-nano interactions.
• Computational analysis provides insights into how interfacial interactions influence nanodrug therapeutic efficacy.
• The integrated "computation + machine learning + experimentation" strategy shows promise for rapid nanodrug discovery.

Conclusions:

• Computational tools are essential for overcoming challenges in precise nanodrug design and screening.
• Understanding interfacial interactions is crucial for optimizing nanodrug performance.
• An integrated "computation + machine learning + experimentation" approach is vital for advancing precision nanomedicine.