| Recommendation |
ID |
Data Information |
Model Information |
Application Information |
Reference |
| Rank |
Total_Score |
Model_ID |
Dataset_Name |
Element_type |
Data_features |
Model_name |
Algorithm_name |
Prediction_type |
Code_source |
Study_types |
Disease_name |
Target_name |
Drug_name |
Interaction_type |
Recommended_Number |
PMID |
Journal |
Publication_Year |
| 1 |
80.3 |
1892 |
Yamanishi_NR |
Drug, Target |
Drug: chemical structure information, Target: target sequence |
PPAEDTI |
Deep Autoencoder |
Drug Target Interaction (DTI) |
https://github.com/LiYuechao1998/PPAEDTI |
Case studies |
NA |
NA |
Nisoldipine |
NA |
Top 20 targets |
36301791 |
IEEE J Biomed Health Inform |
2023 |
| 1 |
80.3 |
1892 |
Yamanishi_NR |
Drug, Target |
Drug: chemical structure information, Target: target sequence |
PPAEDTI |
Deep Autoencoder |
Drug Target Interaction (DTI) |
https://github.com/LiYuechao1998/PPAEDTI |
Case studies |
NA |
hsa:775 (calcium voltage-gated channel subunit alpha1 C) |
NA |
NA |
Top 20 drugs |
36301791 |
IEEE J Biomed Health Inform |
2023 |
| 1 |
80.3 |
1892 |
Yamanishi_NR |
Drug, Target |
Drug: chemical structure information, Target: target sequence |
PPAEDTI |
Deep Autoencoder |
Drug Target Interaction (DTI) |
https://github.com/LiYuechao1998/PPAEDTI |
Case studies |
NA |
hsa:779 (calcium voltage-gated channel subunit alpha1 S) |
NA |
NA |
Top 20 drugs |
36301791 |
IEEE J Biomed Health Inform |
2023 |
| 2 |
70.4 |
150 |
Yamanishi_NR |
Drug, Target |
Drug: chemical sturcture, Target: target sequence |
NA |
Adaptive combination |
Drug Target Interaction (DTI) |
NA |
Case studies |
NA |
NA |
NA |
Drug target interaction |
Top 5 predictions |
26543857 |
Biomed Res Int |
2015 |
| 3 |
66.2 |
386 |
Yamanishi_NR |
Drug, Target |
Drug: molecule, Target: target sequence |
PUDTI |
Support Vector Machine (SVM) |
Drug Target Interaction (DTI) |
NA |
Case studies |
Alzheimer's Disease (AD) |
(D(1A), D(2) and D3 dopamine receptors (P21728, P14416 and P35462), alpha-1A and alpha-1B adrenergic receptor (P35348 and P35368), 5-hydroxytryptamine receptors (P28223) and potassium voltage-gated channel subfamily H member 2(Q12809) |
NA |
NA |
NA |
28808275 |
Sci Rep |
2017 |
| 3 |
66.2 |
386 |
Yamanishi_NR |
Drug, Target |
Drug: molecule, Target: target sequence |
PUDTI |
Support Vector Machine (SVM) |
Drug Target Interaction (DTI) |
NA |
Case studies |
NA |
NA |
Astemizole |
NA |
14 targets |
28808275 |
Sci Rep |
2017 |
| 3 |
66.2 |
386 |
Yamanishi_NR |
Drug, Target |
Drug: molecule, Target: target sequence |
PUDTI |
Support Vector Machine (SVM) |
Drug Target Interaction (DTI) |
NA |
Case studies |
NA |
DNA topoisomerase 2-alpha (P11388) |
NA |
NA |
31 drugs |
28808275 |
Sci Rep |
2017 |
| 4 |
63.2 |
356 |
Yamanishi_NR |
Drug, Target |
Drug: chemical sturcture, Target: target sequence |
Heter-LP |
Heterogeneous label propagation |
Drug Target Interaction (DTI),Drug Disease Association (DDA) |
NA |
Case studies |
Osteoarthritis with mild chondrodysplasia |
NA |
NA |
NA |
2 drugs |
28300647 |
J Biomed Inform |
2017 |
| 5 |
60.0875 |
2176 |
Yamanishi_NR |
Drug, Target |
Target: 20D Vector, Drug: SMILES |
geNnius (Graph Embedding Neural Network Interaction Uncovering System) |
Graph Neural Network (GNN) |
Drug Target Interaction (DTI) |
https://github.com/ubioinformat/GeNNius |
NA |
NA |
NA |
NA |
NA |
NA |
38134424 |
Bioinformatics |
2024 |
| 6 |
59.6 |
415 |
Yamanishi_NR |
Drug, Target |
Drug: chemical sturcture, Target: target sequence |
BRDTI |
Bayesian |
Drug Target Interaction (DTI) |
https://github.com/lpeska/BRDTI |
NA |
NA |
NA |
NA |
NA |
NA |
29054256 |
Comput Methods Programs Biomed |
2017 |
| 7 |
56.76 |
1695 |
Yamanishi_NR |
Drug, Target |
Drug: chemical substructures, Target: the primary sequences |
DT2Vec |
XGBoost |
Drug Target Interaction (DTI) |
https://github.com/elmira-amiri/DT2Vec |
NA |
NA |
NA |
NA |
NA |
NA |
35379165 |
BMC Bioinformatics |
2022 |
| 8 |
52.98571429 |
973 |
Yamanishi_NR |
Drug, Target |
Drug: chemical sturcture, Target: target sequence |
DTIP_MDHN |
Index kernel matrix |
Drug Target Interaction (DTI) |
NA |
NA |
NA |
NA |
NA |
NA |
NA |
32703151 |
BMC Bioinformatics |
2020 |
| 9 |
50.3 |
1787 |
Yamanishi_NR |
Drug, Target |
Drug: chemical structure, target: protein sequence |
Ro-DNILMF |
Integrated logistic matrix factorization |
Drug Target Interaction (DTI) |
https://github.com/LJX0326/Ro-DNILMF.git |
NA |
NA |
NA |
NA |
NA |
NA |
36014371 |
Molecules |
2022 |
| 10 |
47 |
723 |
Yamanishi_NR |
Drug, Target |
NA |
SPLCMF (Self-paced learning with collaborative matrix factorization) |
Matrix Factorization |
Drug Target Interaction (DTI) |
https://github.com/Macau-LYXia/SPLCMF-DTI |
NA |
NA |
NA |
NA |
NA |
NA |
31260620 |
J Chem Inf Model |
2019 |
| 11 |
46.56 |
1658 |
Yamanishi_NR |
Drug, Target |
NA |
IMCHGAN (Inductive Matrix Completion with Heterogeneous Graph Attention Network) |
Graph Attention Network (GAT),Inductive Matrix Completion (IMC) |
Drug Target Interaction (DTI) |
https://github.com/ljatynu/IMCHGAN/ |
NA |
NA |
NA |
NA |
NA |
NA |
34115592 |
IEEE/ACM Trans Comput Biol Bioinform |
2022 |
| 12 |
41.66 |
1473 |
Yamanishi_NR |
Drug, Target |
NA |
DTI-HeNE (DTI based on Heterogeneous Network Embedding) |
Random Forest (RF) |
Drug Target Interaction (DTI) |
https://github.com/arantir123/DTI-hene/ |
NA |
NA |
NA |
NA |
NA |
NA |
34479477 |
BMC Bioinformatics |
2021 |