Woo Dae Jang

dblp:341/1945 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0003-1649-0174ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
drug discovery
0.912025
ChemBounce: a computational framework for scaffold hopping in drug discovery · Bioinform. 2025
Bioinformatics and computational biology › molecular informatics › cheminformatics
molecular similarity
0.312025
ChemBounce: a computational framework for scaffold hopping in drug discovery · Bioinform. 2025

Methods — techniques the papers use, named apart from their topics

tanimoto similarity · 0.9shape similarity · 0.9fragment replacement · 0.9
YearPublicationVenuePosition
2025 ChemBounce: a computational framework for scaffold hopping in drug discovery
abstract
SUMMARY: Scaffold hopping is a critical strategy in medicinal chemistry for generating novel and patentable drug candidates. Here, we present ChemBounce, a computational framework designed to facilitate scaffold hopping by generating structurally diverse scaffolds with high synthetic accessibility. Given a user-supplied molecule in SMILES format, ChemBounce identifies the core scaffolds and replaces them using a curated in-house library of over 3 million fragments derived from the ChEMBL database. The generated compounds are evaluated based on Tanimoto and electron shape similarities to ensure retention of pharmacophores and potential biological activity. By enabling systematic exploration of unexplored chemical space, ChemBounce represents a valuable tool for hit expansion and lead optimization in modern drug discovery. AVAILABILITY AND IMPLEMENTATION: The source code for ChemBounce is available at https://github.com/jyryu3161/chembounce. In addition, a cloud-based implementation of ChemBounce is available as a Google Colaboratory notebook.
Woo Dae Jang, Changdai Gu, Yumi Noh, Kwang-Seok Oh, Jae Yong Ryu
Bioinform.1
2023 PredAOT: a computational framework for prediction of acute oral toxicity based on multiple random forest models
abstract
BACKGROUND: Acute oral toxicity of drug candidates can lead to drug development failure; thus, predicting the acute oral toxicity of small compounds is important for successful drug development. However, evaluation of the acute oral toxicity of small compounds considered in the early stages of drug discovery is limited because of cost and time. Here, we developed a computational framework, PredAOT, that predicts the acute oral toxicity of small compounds in mice and rats. METHODS: PredAOT is based on multiple random forest models for the accurate prediction of acute oral toxicity. A total of 6226 and 6238 compounds evaluated in mice and rats, respectively, were used to train the models. RESULTS: PredAOT has the advantage of predicting acute oral toxicity in mice and rats simultaneously, and its prediction performance is similar to or better than that of existing tools. CONCLUSION: PredAOT will be a useful tool for the quick and accurate prediction of the acute oral toxicity of small compounds in mice and rats during drug development.
Jae Yong Ryu, Woo Dae Jang, Jidon Jang, Kwang-Seok Oh
BMC Bioinform.2