Artificial intelligence to bring nanomedicine to life

The technology of drug delivery systems (DDSs) has demonstrated an outstanding performance and effectiveness in production of pharmaceuticals, as it is proved by many FDA-approved nanomedicines that have an enhanced selectivity, manageable drug release kinetics and synergistic therapeutic actions. Nonetheless, to date, the rational design and high-throughput development of nanomaterial-based DDSs for specific purposes is far from a routine practice and is still in its infancy, mainly due to the limitations in scientists' capabilities to effectively acquire, analyze, manage, and comprehend complex and ever-growing sets of experimental data, which is vital to develop DDSs with a set of desired functionalities. At the same time, this task is feasible for the data-driven approaches, high throughput experimentation techniques, process automatization, artificial intelligence (AI) technology, and machine learning (ML) approaches, which is referred to as The Fourth Paradigm of scientific research. Therefore, an integration of these approaches with nanomedicine and nanotechnology can potentially accelerate the rational design and high-throughput development of highly efficient nanoformulated drugs and smart materials with pre-defined functionalities. In this Review, we survey the important results and milestones achieved to date in the application of data science, high throughput, as well as automatization approaches, combined with AI and ML to design and optimize DDSs and related nanomaterials. This manuscript mission is not only to reflect the state-of-art in data-driven nanomedicine, but also show how recent findings in the related fields can transform the nanomedicine's image. We discuss how all these results can be used to boost nanomedicine translation to the clinic, as well as highlight the future directions for the development, data-driven, high throughput experimentation-, and AI-assisted design, as well as the production of nanoformulated drugs and smart materials with pre-defined properties and behavior. This Review will be of high interest to the chemists involved in materials science, nanotechnology, and DDSs development for biomedical applications, although the general nature of the presented approaches enables knowledge translation to many other fields of science.

Interplay of Conformational and Chemical Transformations of Ortho-Substituted Aromatic Nitroso Oxides: Experimental and Theoretical Study

The mechanism of the photooxidation of aromatic azides containing a substituent at one of the ortho positions (2,4-dimethoxyphenyl azide (1a) and 2-methyl-4-[(2E)-1-methylbut-2-en-1-yl]phenyl azide (1b)) was studied in acetonitrile. The electronic spectra and the kinetic regularities of the consumption of corresponding nitroso oxides, which are the reaction intermediates, were investigated by flash photolysis. Owing to the one-and-a-half order of the C-N and N-O bonds and asymmetric molecule structure these nitroso oxides exist as four conformers (cis/syn, cis/anti, trans/syn, and trans/anti). The conformers differ in the spectral properties and in the reactivity in various irreversible transformations. The only product, (2Z,4E)-4-methoxy-6-oxohepta-2,4-dienenitrile oxide (7a), was observed during photooxidation of 1a, whereas transformations of the nitroso oxide isomers derived from 1b led to a set of stable products: the cis/anti isomer was transformed into (3,4,7-trimethyl-3a,4-dihydro-2,1-benzisoxazol-5(3H)-ylidene)ethanal (10), the trans isomers recombined forming the corresponding nitro and nitroso compounds, and the most reactive cis/syn isomer was transformed into ortho-nitrosobenzyl alcohol 11. The last was oxidized slowly to the corresponding benzaldehyde 12. Interaction of 11 and 12 led to the formation of (Z)-1,2-bis(2-formyl-4-((2E)-1-methylbut-2-en-1-yl)phenyl)diazene-1-oxide (13). The DFT simulation and kinetic modeling of the nitroso oxide transformations as well as the product analysis allowed revealing the fine details of the mechanism of decay for these species.

Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics

We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD). Existing approaches for identifying species and reactions from molecular dynamics typically use bond length and duration criteria, where bond duration is a fixed parameter motivated by an understanding of bond vibrational frequencies. In contrast, we show that for highly reactive systems, bond duration should be a model parameter that is chosen to maximize the predictive power of the resulting statistical model. We demonstrate our method on a high temperature, high pressure system of reacting liquid methane, and show that the learned KMC model is able to extrapolate more than an order of magnitude in time for key molecules. Additionally, our KMC model of elementary reactions enables us to isolate the most important set of reactions governing the behavior of key molecules found in the MD simulation. We develop a new data-driven algorithm to reduce the chemical reaction network which can be solved either as an integer program or efficiently using L1 regularization, and compare our results with simple count-based reduction. For our liquid methane system, we discover that rare reactions do not play a significant role in the system, and find that less than 7% of the approximately 2000 reactions observed from molecular dynamics are necessary to reproduce the molecular concentration over time of methane. The framework described in this work paves the way towards a genomic approach to studying complex chemical systems, where expensive MD simulation data can be reused to contribute to an increasingly large and accurate genome of elementary reactions and rates.