Despite its potential to transform society, materials
research
suffers from a major drawback: its long research timeline. Recently,
machine-learning techniques have emerged as a viable solution to this
drawback and have shown accuracies comparable to other computational
techniques like density functional theory (DFT) at a fraction of the
computational time. One particular class of machine-learning models,
known as “generative models”, is of particular interest
owing to its ability to approximate high-dimensional probability distribution
functions, which in turn can be used to generate novel data such as
molecular structures by sampling these approximated probability distribution
functions. This review article aims to provide an in-depth understanding
of the underlying mathematical principles of popular generative models
such as recurrent neural networks, variational autoencoders, and generative
adversarial networks and discuss their state-of-the-art applications
in the domains of biomaterials and organic drug-like materials, energy
materials, and structural materials. Here, we discuss a broad range
of applications of these models spanning from the discovery of drugs
that treat cancer to finding the first room-temperature superconductor
and from the discovery and optimization of battery and photovoltaic
materials to the optimization of high-entropy alloys. We conclude
by presenting a brief outlook of the major challenges that lie ahead
for the mainstream usage of these models for materials research.
Owing to highly favorable properties such as enormous internal surface areas, high porosity and large flexibility when it comes to the choice of precursors, and high control over their structure...
The chemistry of DNA endows it with certain functional properties that facilitate the generation of self-assembled nanostructures, offering precise control over their geometry and morphology, that can be exploited for advanced biological applications. Despite the structural promise of these materials, their applications are limited owing to lack of functional capability to interact favourably with biological systems, which has been achieved by functional proteins or peptides. Herein, we outline a strategy for functionalizing DNA structures with short-peptides, leading to the formation of DNA-peptide hybrid materials. This proposition offers the opportunity to leverage the unique advantages of each of these bio-molecules, that have far reaching emergent properties in terms of better cellular interactions and uptake, better stability in biological media, an acceptable and programmable immune response and high bioactive molecule loading capacities. We discuss the synthetic strategies for the formation of these materials, namely, solid-phase functionalization and solution-coupling functionalization. We then proceed to highlight selected biological applications of these materials in the domains of cell instruction & molecular recognition, gene delivery, drug delivery and bone & tissue regeneration. We conclude with discussions shedding light on the challenges that these materials pose and offer our insights on future directions of peptide-DNA research for targeted biomedical applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.