Method to Construct Volcano Relations by Multiscale Modeling: Building Bridges between the Catalysis and Biosimulation Communities

Computer-aided nanodrug discovery: recent progress and future prospects

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

A doxorubicin-peptide-gold nanoparticle conjugate as a functionalized drug delivery system: exploring the limits

Efficient transport of pharmaceuticals to malignant cells in the human body often requires the application of drug-delivery systems (DDSs) consisting of several building blocks, each of them bearing a specific function. While nanoparticles are promising as potential carrier moieties, biomolecules may add to the efficient delivery by binding several drug molecules simultaneously. In this contribution, we apply a combination of atomistic molecular dynamics simulations and density functional theory calculations to characterize a multi-component DDS for the transport of the anthracycline antibiotic doxorubicin (DOX), comprising a gold nanoparticle (NP) and a drug-binding peptide (DBP) grafted on the NP surface. We have shown previously that the DDS can stabilize one DOX per DBP. However, by increasing the drug load to a 2 : 1 DOX : DBP ratio the two drug molecules compete for the available adsorption sites, which may cause spontaneous dissociation of one DOX molecule. We identify the chain length of the DBP as a limiting factor for the drug-loading capacity and provide important guidelines for further optimization of multi-component functionalized DDSs.