DrugVLAB™

DrugVLABTM은 chemical space, genetic space, disease space를 정확하고 효율적으로 탐색하여 표적단백질 및 후보물질을 선별합니다.
수많은 논문으로 검증된 standalone tool들과 다양한 needs를 모아서 만든 pre-built packages를 통해 약물 개발 단계를 최적화합니다.

Standalone Tools

Target Discovery

Biomarker Discovery using multi-omics data

  • SupervisedSAS. Scientific Reports, 2021.
  • Risk Stratification. Cancers. 2022.
  • GOAT. Bioinformatics, 2023

EHR data analysis or Text mining

  • Document Classification. AAAI , 2022.
  • TM-HGNN. ACL, 2023

Lead Identification

Lead identification

  • ChemNP. CSBJ, 2023.

Drug Target Interaction

  • EnsDTI. BioRxiv, 2023
  • Diffusion-based docking

Lead Optimization

Lead optimization with graph deep learning

  • SPGP.NeurIPS GLFrontiers.2022
  • ChemGen.

Drug combination 

  • MADC. BIB,2023

Pre-Clinical

Toxicity

  • SSM.iScience,2023
  • SIDER. IEEE JBHI , 2023 (revision)
  • MDTR. Scientific Reports. (Under review)

ADME

  • Multi-Task Aware Learnable Prototypes on Few Shot learning

Animal model

  • In-silico virtual knockout system
  • In-silico experimental system. Methods,2018

Clinical

Drug response

  • DRPreter.Int J Mol Sci .2022
  • NetGP.BIB.2023

Drug repurposing

  • DREAMwalk. Nature Comms. 2023

Drug approval/withdrawal

  • Drug withdrawal pred. IEEE Big Comp, 2022.

ChemMining™

데이터 마이닝 기반 화합물 데이터 베이스 탐색으로 선도물질 발굴 가속화

ChemMining™ efficiently explores the chemical space, identifying high-efficacy compounds and providing priority rankings utilizing Deep Learning, Network Exploration, and Cheminformatics. Our innovative approach allows for the discovery of compounds with unique scaffolds, enhancing patentability and diversity.

  • Used Cases Discovery of hit compounds for small molecule kinase inhibitors. Identification of hit compounds for pharmacological chaperones.

ChemGen™

화합물의 functional fragments 발굴을 통한 3차원 단백질 구조 기반 선도물질 생성 가속화

ChemGen™ innovates compound design using protein 3D structures and experimental data. Our Generative AI models create molecules binding to protein pockets. With Network Propagation technology, we efficiently select high-efficacy, drug-like compounds with prioritized precisions.

  • Used Cases Optimization of pharmacological chaperone lead compounds. Development of optimal compound formulations.

ChemResponse™

다양한 레벨에서의 작용기전 해석을 통한 "약물 반응성" 예측

ChemResponse™ predicts drug responses by analyzing the mechanism of action from various perspectives. Our innovative approach identifies crucial gene interactions and pathways. Also, we simulate the effects of compound perturbations starting from target genes, pinpointing driver genes crucial to the biological mechanisms of the compound.

  • Used Cases Interpreting mode of action of compounds. Stratification of samples based on drug responsiveness.

ChemTox™

생체 내 데이터 기반 인체 내 독성 및 부작용 예측

ChemTox™ analyzes in vivo toxicity and side effects based on transcriptome data and compounds structures. We identify crucial toxicophores, predict toxicity via CYP and hERG analysis, and offer insights into six hepatotoxicity mechanisms. Also, we forecast compound side effect frequencies based on molecular structure, similarity, and target protein data.

  • Used Cases Prediction of kinase off-target effects.

ChemTune™

생물의학적 지식 네트워크 기반 약물 재창출 및 복합제 예측

ChemTune™ utilizes biomedical knowledge networks for knowledge drug repurposing and synergy analysis. Our biomedical knowledge graph connects drug-disease spaces with Protein-Protein Interaction (PPI) networks. Skip-Gram encoder models analyze relationships, and ‘Teleportation’ in random walks ensures unbiased exploration.

  • Used Cases Prediction of small molecule inhibitor combinations.

ChemProtac™

3차원 구조정보 기반 PROTAC 링커 설계 및 ADME 예측

ChemProtac™ elevates the potential of candidate inhibitors by expertly designing tailored PROTAC solutions. By integrating existing assay data with advanced protein structure prediction and docking simulation models, our model refines the search for optimal linkers in the development and evaluation of de novo PROTAC leads.

  • Used Cases Explore innovative PROTAC linker designs. Predicting protein inhibition efficacy for existing PROTACs.

Customized Packages

Fully customized AI service tailored specifically for your needs