Woo Youn Kim 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

Recently, deep neural network (DNN)-based drug-target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models' insufficient generalization remains a challenging problem in the practice of <i>in silico</i> drug discovery. We propose two key strategies to enhance generalization in the DTI model. The first is to predict the atom-atom pairwise interactions <i>via</i> physics-informed equations parameterized with neural networks and provi

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

Deep neural networks have been increasingly used in various chemical fields. In the nature of a data-driven approach, their performance strongly depends on data used in training. Therefore, models developed in data-deficient situations can cause highly uncertain predictions, leading to vulnerable decision making. Here, we show that Bayesian inference enables more reliable prediction with quantitative uncertainty analysis. Decomposition of the predictive uncertainty into model- and data-driven un

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

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

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

Theoretical understanding of metal nanowires and molecular devices is described towards the design of novel nanodevices. We focus our attention on structures, electronic, and spintronic properties of low dimensional metallic/molecular nanostructures based mostly on our recent works. The discussion includes (i) electric field induced molecular orbital control towards molecular electronic and spintronic devices, (ii) conductances of carbon nanotubes and graphene nanoribbons, (iii) low dimensional

Machine learning based on big data has emerged as a powerful solution in various chemical problems. We investigated the feasibility of machine learning models for the prediction of activation energies of gas-phase reactions. Six different models with three different types, including the artificial neural network, the support vector regression, and the tree boosting methods, were tested. We used the structural and thermodynamic properties of molecules and their differences as input features witho

A new quantification method of drug-likeness based on unsupervised learning. The method only uses drug molecules as training set without any non-drug-like molecules.

Promising applications of graphdiyne have often been initiated by theoretical predictions especially using DFT known as the most powerful first-principles electronic structure calculation method. However, there is no systematic study on the reliability of DFT for the prediction of the electronic properties of the graphdiyne. Here, we performed a study of Li adsorption on the graphdiyne using hybrid DFT with LC-ωPBE and compared the results with those of PBE, because accurate prediction of the Li