김우연 교수

Abstract Polydopamine is the first adhesive polymer that can functionalize surfaces made of virtually all material chemistries. The material‐independent surface modification properties of polydopamine allow the functionalization of various types of medical and energy devices. However, the mechanism of dopamine polymerization has not yet been clearly demonstrated. Covalent oxidative polymerization via 5,6‐dihydroxyindole (DHI), which is similar to the mechanism for synthetic melanin synthesis, ha

We propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. We introduce a distance-aware graph attention algorithm to differentiate various types of intermolecular interactions. Furthermore, we extract the graph feature of intermolecular interactions directly from the 3D structural information on the protein-ligand binding pose. Thus, the model can learn key features for accurate predictions of drug-target interaction rather than just memorize

physics-informed equations parameterized with neural networks and provides the total binding affinity of a protein-ligand complex as their sum. We further improved the model generalization by augmenting a broader range of binding poses and ligands to training data. We validated our model, PIGNet, in the comparative assessment of scoring functions (CASF) 2016, demonstrating the outperforming docking and screening powers than previous methods. Our physics-informing strategy also enables the interp

With the advance of nanotechnology, a variety of molecules, from single atoms to large-scale structures such as graphene or carbon nanotubes, have been investigated for possible use as molecular devices. Molecular orbitals (MOs) are a key ingredient in determining the transport properties of molecules, because they contain all the quantum mechanical information of molecular electronic structures and offer spatial conduction channels for electron transport. Therefore, the delicate modulation of t

prediction illustrates that data noise affects the data-driven uncertainty more significantly than the model-driven one. Based on this finding, we could identify artefacts that arose from quantum mechanical calculations in the Harvard Clean Energy Project dataset. Consequently, the Bayesian GCN is critical for molecular applications under data-deficient conditions.

Abstract Covalent organic frameworks (COFs) have been considered a potentially versatile electrode structure if they are made highly conductive and flexible to stabilize the redox functionality. Although conceptually plausible, COF‐based electrodes have rarely satisfied high capacity, cyclability, and rate capability thus far. Incorporating thiazole moieties into the organic scaffold, it is able to fabricate π‐conjugated and crystalline organic electrodes and demonstrate the fast two‐electron tr

Despite remarkable advances in computational chemistry, prediction of reaction mechanisms is still challenging, because investigating all possible reaction pathways is computationally prohibitive due to the high complexity of chemical space. A feasible strategy for efficient prediction is to utilize chemical heuristics. Here, we propose a novel approach to rapidly search reaction paths in a fully automated fashion by combining chemical theory and heuristics. A key idea of our method is to extrac

Rapid progress of nanotechnology requires developing novel theoretical methods to explain complicated experimental results and predict new functions of nanodevices. Thus, for the last decade, one of the challenging works of quantum chemistry is to understand the electron and spin transport phenomena in molecular devices. This critical review provides an extensive survey of on-going research and its current status in molecular electronics with the focus on theoretical applications to diverse type

Modification of the electronic structure and lattice contraction of Pt alloy nanocatalysts through control over their morphology and composition has been a crucial issue for improving their electrocatalytic oxygen reduction reaction (ORR) activity. In the present work, we synthesized PtCo alloy nanocubes with controlled compositions (Pt(x)Co NCs, x = 2, 3, 5, 7, and 9) by regulating the ratio of surfactants and the amount of Co precursor to elucidate the effect of the composition of nanocatalyst

Based on density functional theory, we have developed a program code to investigate the electron transport characteristics for a variety of nanometer scaled devices in the presence of an external bias voltage. We employed basis sets comprised of linear combinations of numerical type atomic orbitals, particularly focusing on k-point sampling for the realistic modeling of the bulk electrode. The scheme coupled with the matrix version of the nonequilibrium Green's function method enables calculatio

We present a powerful method for the conversion of molecular structures from atomic connectivity to bond orders to three‐dimensional ( 3D ) geometries. There are a number of bond orders and 3D geometries corresponding to a given atomic connectivity. To uniquely determine an energetically more favorable one among them, we use general chemical rules without invoking any empirical parameter, which makes our method valid for any organic molecule. Specifically, we first assign a proper bond order to

Deep generative modeling has a strong potential to accelerate drug design. However, existing generative models often face challenges in generalization due to limited data, leading to less innovative designs with often unfavorable interactions for unseen target proteins. To address these issues, we propose an interaction-aware 3D molecular generative framework that enables interaction-guided drug design inside target binding pockets. By leveraging universal patterns of protein-ligand interactions

An intricate problem in molecular electronics is to control the molecule-electrodes contacts. Asymmetric couplings between both contacts are important in driving novel nonlinear transport characteristics like negative differential resistance (NDR). We find that in the presence of an applied field, metallic carbon nanotubes (CNTs) can form asymmetric couplings even if symmetric structures are employed. This origin is due to the CNT itself, while the NDR phenomenon can be obtained by tuning the th

Basin-hopping sampling has been widely used for searching local minima on a potential energy surface. Reaction intermediates including reactants and products are also local minima composed of a reaction path, but their brute-force sampling is too demanding because of large degrees of freedom. We developed an efficient Monte Carlo basin-hopping method to sample reaction intermediates through the fragmentation of molecules and a postanalysis scheme using the graph theory with a matrix representati