Search Results for author: SeongHwan Kim

Found 13 papers, 5 papers with code

FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching

no code implementations19 Feb 2025 Joongwon Lee, SeongHwan Kim, Seokhyun Moon, Hyunwoo Kim, Woo Youn Kim

We introduce FragFM, a novel hierarchical framework via fragment-level discrete flow matching for efficient molecular graph generation.

Diversity Drug Discovery +3

Riemannian Denoising Score Matching for Molecular Structure Optimization with Accurate Energy

no code implementations29 Nov 2024 Jeheon Woo, SeongHwan Kim, Jun Hyeong Kim, Woo Youn Kim

Our method has been evaluated by refining several types of starting structures on the QM9 and GEOM datasets, demonstrating that the proposed Riemannian score matching method significantly improves the accuracy of the generated molecular structures, attaining chemical accuracy.

Computational chemistry Denoising

Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation

no code implementations2 Oct 2024 Jun Hyeong Kim, SeongHwan Kim, Seokhyun Moon, Hyeongwoo Kim, Jeheon Woo, Woo Youn Kim

Our approach extends Iterative Markovian Fitting to discrete domains, and we have proved its convergence to the SB.

Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers

1 code implementation30 May 2024 Kiyoung Seong, Seonghyun Park, SeongHwan Kim, Woo Youn Kim, Sungsoo Ahn

Understanding transition pathways between two meta-stable states of a molecular system is crucial to advance drug discovery and material design.

Drug Discovery

Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models

no code implementations3 Apr 2024 Hyungjoo Chae, Yeonghyeon Kim, Seungone Kim, Kai Tzu-iunn Ong, Beong-woo Kwak, Moohyeon Kim, SeongHwan Kim, Taeyoon Kwon, Jiwan Chung, Youngjae Yu, Jinyoung Yeo

Also, we show that compared to natural language, pseudocode can better guide the reasoning of LMs, even though they are trained to follow natural language instructions.

Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs

1 code implementation20 Apr 2023 SeongHwan Kim, Jeheon Woo, Woo Youn Kim

The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics.

GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising

no code implementations28 Mar 2023 Hyeonsu Kim, Jeheon Woo, SeongHwan Kim, Seokhyun Moon, Jun Hyeong Kim, Woo Youn Kim

Hence, to incorporate information of the correct, GeoTMI aims to maximize mutual information between three variables: the correct and the corrupted geometries and the property.

Denoising

The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions

1 code implementation5 Jul 2022 Mingyu Kim, Jihwan Oh, Yongsik Lee, Joonkee Kim, SeongHwan Kim, Song Chong, Se-Young Yun

This challenge, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control.

SMAC+

Ambiguity Adaptive Inference and Single-shot based Channel Pruning for Satellite Processing Environments

no code implementations29 Sep 2021 Minsu Jeon, Kyungno Joo, Changha Lee, Taewoo Kim, SeongHwan Kim, Chan-Hyun Youn

In a restricted computing environment like satellite on-board systems, running DL models has limitation on high-speed processing due to the problems such as restriction of available power to consume compared to the relatively high computational complexity.

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