LiSENCE: A Hybrid Ligand and Sequence Encoder Network for Predicting CYP450 Inhibitors in Safe Multidrug Administration
Abstract
:1. Introduction
- (1)
- A novel LiSENCE hybrid AI model is proposed to predict the CYP450 inhibitors by combining and assembling features from two pipelines: the Ligand Encoder Network (LEN) and the Sequence Encoder Network (SEN). Both pipelines incorporated various attention modules to enhance the feature extraction of ligands and protein target sequences.
- (2)
- To extract important node information from the ligand graph, a novel two-stage attention mechanism is proposed utilizing Joint-localized Attention (JoLA) and Self-Attention modules.
- (3)
- The innovative Attentive-GIN (AGIN) module is designed to enhance the extraction of the compound features.
2. Related Work
3. Methods
3.1. Dataset
3.1.1. Drug/Ligand/Compound SMILEs String Dataset
PubChem Database
ChEMBL CYP450 BioAssay Data
3.1.2. CYP Target Protein Sequence Dataset
3.2. Preprocessing
3.3. The Proposed LiSENCE Framework for CYP450 Inhibitors Prediction
3.3.1. Ligand Encoder Network (LEN)
Joint-Localized Attention (JoLA)
Attentive Graph Isomorphism Network (AGIN)
Hybrid Pooling Layer
Multiple Convolution Layers
3.3.2. Sequence Encoder (SEN)
3.3.3. Classification Module
3.3.4. Explainable (XAI) Module
3.4. Evaluation Strategy
3.5. Training and Inference Settings
4. Experimental Results
4.1. Convergence of the Proposed AI Framework
4.2. LEN Evaluation Results
4.2.1. Examining JoLA
4.2.2. Examining AGIN
4.2.3. Examining LEN (End-to-End)
4.3. SEN Evaluation Results
4.4. Evaluation Comparison Results of LiSENCE to Other State-of-the-Art Methods
4.5. Visualizing Results with XAI Module and Re-Scoring LiSENCE
Re-Scoring LiSENCE After Explainability
4.6. Ablation Study Using ChEMBL External Dataset: Verification and Validation (V&V)
Statistical Testing of 10-Fold CV
4.7. Clinical Verification, Validation, and Application
5. Discussion
5.1. The Effectiveness of Various AI Configurations: Individual LEN Ablation Study
5.2. The Effectiveness of Various AI Configurations: Individual SEN Ablation Study
5.3. The Proposed LiSENCE Against Its Individual Sub-Models (LEN and SEN)
5.4. LiSENCE vs. the State-of-the-Art AI Models
5.5. Biological Deductions from XAI Module
5.6. Ablation Study: Biological Inference of Clinical Verification and Validation
6. Limitations
7. Future Works
8. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
XAI | Explainable Artificial Intelligence |
LEN | Ligand Encoder Network |
DDI | Drug–Drug Interactions |
SEN | Sequence Encoder Network |
LiSENCE | Ligand and Sequence Encoder Networks for Predicting CYP450 Inhibitors |
GIN | Graph Isomorphism Network |
AGIN | Attentive Graph Isomorphism Network |
LA | Local Attention |
JoLA | Joint-localized Attention |
ML | Machine Learning |
CV | Cross-validation |
DL | Deep Learning |
BR | Before Re-scoring |
AR | After Re-scoring |
SMILES | Simplified Molecular Input Line Entry System |
PDB | Protein Data Bank |
CYP450 | Cytochrome P450 |
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AI Category | Reference | Model /Platform | Dataset | Description | Limitation |
---|---|---|---|---|---|
Machine Learning | Li et al. [20], (2023) | RF, SVM, XGBoost | 500 compounds (CYP2B6 inhibitors vs. non-inhibitors) | Utilized molecular fingerprints, focusing on CYP2B6 | Single-isoform, single-feature type approach can be suboptimal for capturing isoform-specific patterns. |
Ouzounis et al. [21], (2023) | Majority -voting ensemble (CloudScreen platform) | PubChem and ChEMBL data | Used multiple molecular representations for drug repurposing | Relies on protein docking to generate features, adding complexity and requiring known structures. Data for some isoforms (CYP2A6/2B6) is limited, which may affect those models’ reliability. | |
Plonka et al. [25], (2021) | Random Forest models (web tool) | ChEMBL, PubChem, and a private ADME dataset | Random Forest classifiers for each of the 5 major CYP isoforms | Proprietary data inclusion means the model is not easily reproducible by others. | |
Banerjee et al. [27], (2021) | Random Forest (web server) | 17,143 compounds from PubChem | Random Forest deployed as a web server to classify inhibitors CYP1A2, 2C9, 2C19, 2D6, 3A4 | Uses simple molecular fingerprints, offering limited interpretability. | |
Deep Learning | Chen et al. [17] (2024) | Deep learning + Substructure Recognition | 85,715 compounds from PubChem BioAssay—high-quality curated set for 5 CYPs | Interpretable deep model highlighting key substructures | Requires a very large training set; performance on chemotypes outside the training distribution or on other CYP isoforms remains untested. |
Ai et al. [19] (2023)—DEEPCYPs | Multitask FP-GNN (web server) | 65,467 compounds (after curation) from PubChem BioAssay | Fuses GCN with fingerprint features to predict CYP inhibition for 5 major CYP isoforms | Offers limited interpretability—it can highlight influential fragments, but the reasons for predictions are not transparent. | |
Qiu et al. [23](2022) | GCNN + Attention | 17k compounds (PubChem inhibitor data for 5 CYPs, same dataset as iCYP-MFE) | Handles multiple CYP isoforms with end-to-end learning | Requires high-quality protein structure or sequence data for each isoform, limiting its application to well-characterized CYPs. | |
Park et al. [26] (2022) | Bio-selectivity multitask DNN (stereochemical descriptors) | 150,000 compounds from PubChem (5 CYP isoforms) | A multitask deep neural network model for CYP inhibition that introduced 3D stereochemical descriptors to account for bio-selectivity differences among isoforms | Moreover, like other data-driven models, it does not explicitly model protein–ligand interactions, so mechanistic insight remains limited to correlations in the training data. | |
Hybrid | Nguyen-Vo et al. [24] (2022)—iCYP-MFE | Multitask learning + Fingerprint Embedding | 17,143 compounds from PubChem (5 CYP isoforms)—curated and split for multitask learning | Graph-based learning with attention to relevant subgraphs | Uses a single type of input feature (chemical fingerprints) for all tasks, which can limit its ability to capture isoform-specific nuances. |
Weiser et al. [22] (2023) | ML- Augmented Docking | Combined known inhibitor data for major CYPs | Incorporates selectivity profiles into model training | The workflow is computationally intensive (each query requires docking simulation). | |
Kim et al. [18] (2023) | PubChem Resource | Millions of screening results (incl. CYP inhibition assays) | Learns cross-task CYP inhibition representations | Despite curation efforts, the translational relevance of high-throughput inhibition data (often single-concentration or in vitro) to clinical DDI risk can be limited. |
Isoforms | Inhibitors | Non-Inhibitors | Ratio (Pos/Neg) | Total | ||||
---|---|---|---|---|---|---|---|---|
Training/ Validation | Testing | Training/ Validation | Testing | Training/ Validation | Testing | Training/ Validation | Testing | |
1A2 | 3885 | 158 | 6223 | 1120 | 0.14:1 | 0.62:1 | 10,108 | 1278 |
2C9 | 2808 | 7252 | 7089 | 27,502 | 0.26:1 | 0.40:1 | 9897 | 34,754 |
2C19 | 4599 | 8755 | 6101 | 22,758 | 0.38:1 | 0.75:1 | 10,700 | 31,513 |
2D6 | 1392 | 182 | 9698 | 1291 | 0.14:1 | 0.14:1 | 11,090 | 1473 |
3A4 | 3083 | 916 | 6668 | 2680 | 0.34:1 | 0.46:1 | 9751 | 3596 |
Total | 15,767 | 17,263 | 35,779 | 55,351 | 0.31:1 | 0.44:1 | 51,546 | 72,614 |
Isoforms | Inhibitors | Non-Inhibitors | Total |
---|---|---|---|
1A2 | 578 | 1042 | 1620 |
2C9 | 1278 | 1434 | 2712 |
2C19 | 609 | 952 | 1561 |
2D6 | 1386 | 1740 | 3126 |
3A4 | 2572 | 2075 | 4647 |
Isoforms | Acc | AUC | Sen | Spe | MCC | F1-Score | Pre |
---|---|---|---|---|---|---|---|
CYP1A2 | 30 | 65 | 28 | 45 | 18 | 25 | 26 |
CI | (25.0, 35.0) | (59.5, 70.5) | (25.6, 30.4) | (40.1, 49.9) | (14.4, 21.6) | (21.4, 28.6) | (22.2, 29.8) |
CYP2C9 | 34 | 67 | 30 | 47 | 19 | 27 | 28 |
CI | (29.0, 39.0) | (61.5, 72.5) | (27.6, 32.4) | (42.1, 51.9) | (15.4, 22.6) | (23.4, 30.6) | (24.2, 31.8) |
CYP2C19 | 36 | 69 | 31 | 50 | 20 | 29 | 30 |
CI | (31.0, 41.0) | (63.5, 74.5) | (28.6, 33.4) | (45.1, 54.9) | (16.4, 23.6) | (25.4, 32.6) | (26.2, 33.8) |
CYP2D6 | 39 | 71 | 32 | 52 | 21 | 30 | 31 |
CI | (34.0, 44.0) | (65.5, 76.5) | (29.6, 34.4) | (47.1, 56.9) | (17.4, 24.6) | (26.4, 33.6) | (27.2, 34.8) |
CYP3A4 | 44.5 | 76.5 | 33 | 55 | 25.5 | 32.5 | 34 |
CI | (35.0, 45.0) | (71.0, 82.0) | (30.6, 35.4) | (50.1, 59.9) | (21.9,29.1) | (28.9, 36.1) | (30.2, 37.8) |
Average | 35.8 | 69.7 | 30.8 | 49.8 | 20.7 | 28.7 | 29.8 |
Isoforms | Acc | AUC | Sen | Spe | MCC | F1-Score | Pre |
---|---|---|---|---|---|---|---|
CYP1A2 | 35 | 68 | 35 | 53 | 22 | 25 | 30 |
CI | (30.3, 39.7) | (63.3, 72.7) | (31.3, 38.7) | (48.3, 57.7) | (18.2, 25.8) | (20.7, 29.3) | (27.0, 33.0) |
CYP2C9 | 37 | 70 | 37 | 55 | 24 | 27 | 31 |
CI | (32.3, 41.7) | (65.3, 74.7) | (33.3, 40.7) | (50.3, 59.7) | (20.2, 27.8) | (22.7, 31.3) | (28.0, 34.0) |
CYP2C19 | 40 | 72 | 39 | 57 | 26 | 30 | 33 |
CI | (35.3, 44.7) | (67.3, 76.7) | (35.3, 42.7) | (52.3, 61.7) | (22.2, 29.8) | (25.7, 34.3) | (30.0, 36.0) |
CYP2D6 | 42 | 75 | 41 | 60 | 27 | 32 | 34 |
CI | (37.3, 46.7) | (70.3, 79.7) | (37.3, 44.7) | (55.3, 64.7) | (23.2, 30.8) | (27.7, 36.3) | (31.0, 37.0) |
CYP3A4 | 44.5 | 77.5 | 42.5 | 62.5 | 30 | 33.5 | 36 |
CI | (39.8, 49.2) | (72.8, 82.2) | (38.8, 46.2) | (57.8, 67.2) | (26.2, 33.8) | (29.2, 37.8) | (33.0, 39.0) |
Average | 39.7 | 72.5 | 38.9 | 57.5 | 25.8 | 29.5 | 32.8 |
Isoforms | Acc | AUC | Sen | Spe | MCC | F1-Score | Pre |
---|---|---|---|---|---|---|---|
CYP1A2 | 42 | 85 | 40 | 60 | 30 | 28 | 42 |
CI | (38.9, 45.1) | (79.7, 90.3) | (35.7, 44.3) | (54.3, 65.7) | (24.0, 36.0) | (23.9, 32.1) | (38.3, 45.7) |
CYP2C9 | 44 | 90 | 42 | 63 | 32 | 30 | 44 |
CI | (40.9, 47.1) | (84.7, 95.3) | (37.7, 46.3) | (57.3, 68.7) | (26.0, 38.0) | (25.9, 34.1) | (40.3, 47.7) |
CYP2C19 | 46 | 92 | 45 | 66 | 34 | 32 | 46 |
CI | (42.9, 49.1) | (86.7, 97.3) | (40.7, 49.3) | (60.3, 71.7) | (28.0, 40.0) | (27.9, 36.1) | (42.3, 49.7) |
CYP2D6 | 48 | 95 | 47 | 68 | 38 | 34 | 48 |
CI | (44.9, 51.1) | (89.7, 98.3) | (42.7, 51.3) | (62.3, 73.7) | (32.0, 44.0) | (29.9, 38.1) | (44.3, 51.7) |
CYP3A4 | 47.5 | 95.5 | 48.5 | 72 | 42 | 36.5 | 49.5 |
CI | (44.4, 50.6) | (90.2, 97.8) | (44.2, 52.8) | (66.3, 77.7) | (36.0, 48.0) | (32.4, 40.6) | (45.8, 53.2) |
Average | 45.5 | 91.5 | 44.5 | 65.8 | 35.2 | 32.1 | 45.9 |
Isoforms | Acc | AUC | Sen | Spe | MCC | F1-Score | Pre |
---|---|---|---|---|---|---|---|
CYP1A2 | 34 | 85 | 50 | 65 | 20 | 34 | 74 |
CI | (29.7, 38.3) | (80.5, 89.5) | (47.2, 52.8) | (60.8, 69.2) | (17.2, 22.8) | (30.3, 37.7) | (68.7, 79.3) |
CYP2C9 | 36 | 88 | 52 | 68 | 21 | 36 | 76 |
CI | (31.7, 40.3) | (83.5, 92.5) | (49.2, 54.8) | (63.8, 72.2) | (18.2, 23.8) | (32.3, 39.7) | (70.7, 81.3) |
CYP2C19 | 38 | 90 | 54 | 70 | 22 | 38 | 78 |
CI | (33.7, 42.3) | (85.5, 94.5) | (51.2, 56.8) | (65.8, 74.2) | (19.2, 24.8) | (34.3, 41.7) | (72.7, 83.3) |
CYP2D6 | 40 | 92 | 56 | 72 | 24 | 40 | 82 |
CI | (35.7, 44.3) | (87.5, 96.5) | (53.2, 58.8) | (67.8, 76.2) | (21.2, 26.8) | (36.3, 43.7) | (76.7, 87.3) |
CYP3A4 | 43 | 94.5 | 52.5 | 73.5 | 25.5 | 41.5 | 84.5 |
CI | (38.7, 47.3) | (90.0, 99.0) | (49.7, 55.3) | (69.3, 77.7) | (22.7, 28.3) | (37.8, 45.2) | (79.2, 89.8) |
Average | 38.2 | 89.9 | 52.9 | 69.7 | 22.5 | 37.9 | 78.9 |
Isoforms | Acc | AUC | Sen | Spe | MCC | F1-Score | Pre |
---|---|---|---|---|---|---|---|
CYP1A2 | 82 | 89 | 70 | 85 | 68.1 | 74 | 85 |
CI | (79.2, 84.8) | (87.4, 90.6) | (66.6, 73.4) | (82.0, 88.0) | (66.3, 69.9) | (67.1, 80.9) | (80.8, 89.2) |
CYP2C9 | 84 | 92 | 74 | 88 | 71.5 | 78 | 87 |
CI | (81.2, 86.8) | (90.4, 93.6) | (70.6, 77.4) | (85.0, 91.0) | (69.7, 73.3) | (71.1, 84.9) | (82.8, 91.2) |
CYP2C19 | 85 | 91 | 72 | 90 | 70.3 | 64 | 80 |
CI | (82.2, 87.8) | (89.4, 92.6) | (68.6, 75.4) | (87.0, 93.0) | (68.5, 72.1) | (57.1, 70.9) | (75.8, 84.2) |
CYP2D6 | 80 | 90 | 76 | 84 | 69.7 | 75 | 89 |
CI | (77.2, 82.8) | (88.4, 91.6) | (72.6, 79.4) | (81.0, 87.0) | (67.9, 71.5) | (68.1, 81.9) | (84.8, 93.2 |
CYP3A4 | 85.5 | 92 | 76.5 | 86.5 | 71.5 | 76.5 | 85.5 |
CI | (82.7, 88.3) | (90.4, 93.6) | 73.1,79.9) | (83.5, 89.5) | (69.7, 73.3) | (69.6, 83.4) | (81.3, 89.7) |
Average | 70.1 | 95.8 | 81.7 | 92.2 | 70.3 | 76.7 | 86.9 |
Isoforms | Acc | AUC | Sen | Spe | MCC | F1-Score | Pre |
---|---|---|---|---|---|---|---|
CYP1A2 | 65 | 88 | 74 | 88 | 60 | 70 | 77 |
CI | (62.4, 67.6) | (84.1, 91.9) | (69.9, 78.1) | (84.2, 91.8) | (54.3, 65.7) | (67.0, 73.0) | (73.9, 80.1) |
CYP2C9 | 67 | 89 | 76 | 90 | 63 | 72 | 79 |
CI | (64.4, 69.6) | (85.1, 92.9) | (71.9, 80.1) | (86.2, 93.8) | (57.3, 68.7) | (69.0, 75.0) | (75.9, 82.1) |
CYP2C19 | 66 | 91 | 78 | 92 | 66 | 74 | 81 |
CI | (63.4, 68.6) | (87.1, 94.9) | (73.9, 82.1) | (88.2, 95.8) | (60.3, 71.7) | (71.0, 77.0) | (77.9, 84.1) |
CYP2D6 | 64 | 92 | 80 | 93 | 68 | 75 | 83 |
CI | (61.4, 66.6) | (88.1, 95.9) | (75.9, 84.1) | (89.2, 96.8) | (62.3, 73.7) | (72.0, 78.0) | (79.9, 86.1) |
CYP3A4 | 69.5 | 96 | 82.5 | 96 | 72 | 76 | 82.5 |
CI | (66.9, 72.1) | (92.1, 99.9) | (78.4, 86.6 | 92.2, 99.8) | (66.3, 77.7) | (73.0, 79.0) | (79.4, 85.6) |
Average | 66.3 | 91.2 | 78.1 | 91.8 | 65.8 | 73.4 | 80.5 |
CYP | Reference, (Year) | Method | Acc | AUC | Sen | Spe | MCC | F1-Score | Pre |
---|---|---|---|---|---|---|---|---|---|
1A2 | Njimbouom et al. [15] (2024) | MumCypNet | 82 | 90 | 82 | 85 | 64 | 82 | 82 |
Ai et al. [19], (2023) | DEEPCYPs | 84 | 93 | 82 | 88 | 70 | 82 | 91 | |
Qiu et al. [23], (2022) | GCNN | 84 | 92 | 82 | 86 | 68 | 84 | 92 | |
Ngyuyen et al. [24] (2021) | iCYP-MFE | 81 | 91 | 81 | 81 | 62 | 83 | 83 | |
Ours, (2025) | LiSENCE | 87 | 95 | 85 | 92 | 68 | 86 | 81 | |
2C9 | Njimbouom et al. [15] (2024) | MumCypNet | 80 | 89 | 69 | 91 | 59 | 78 | 88 |
Ai et al. [19] (2023) | DEEPCYPs | 84 | 91 | 72 | 93 | 66 | 76 | 85 | |
Qiu et al. [23] (2022) | GCNN | 76 | 89 | 63 | 92 | 58 | 70 | 79 | |
Ngyuyen et al. [24] (2021) | iCYP-MFE | 86 | 92 | 72 | 91 | 67 | 80 | 90 | |
Ours, (2025) | LiSENCE | 87 | 93 | 79 | 80 | 66 | 78 | 85 | |
2C19 | Njimbouom et al. [15] (2024) | MumCypNet | 80 | 89 | 69 | 91 | 59 | 78 | 88 |
Ai et al. [19] (2023) | DEEPCYPs | 78 | 86 | 71 | 84 | 57 | 76 | 81 | |
Qiu et al. [23] (2022) | GCNN | 81 | 88 | 77 | 82 | 60 | 76 | 89 | |
Ngyuyen et al. [24] (2021) | iCYP-MFE | 90 | 90 | 67 | 89 | 68 | 62 | 80 | |
Ours, (2025) | LiSENCE | 92 | 91 | 80 | 92 | 67 | 77 | 90 | |
2D6 | Njimbouom et al. [15] (2024) | MumCypNet | 78 | 86 | 71 | 85 | 57 | 76 | 81 |
Ai et al. [19] (2023) | DEEPCYPs | 91 | 88 | 64 | 91 | 60 | 65 | 78 | |
Qiu et al. [23] (2022) | GCNN | 85 | 92 | 77 | 85 | 66 | 76 | 87 | |
Ngyuyen et al. [24] (2021) | iCYP-MFE | 82 | 91 | 78 | 84 | 64 | 77 | 86 | |
Ours, (2025) | LiSENCE | 92 | 95 | 79 | 81 | 65 | 76 | 87 | |
3A4 | Njimbouom et al. [15] (2024) | MumCypNet | 87 | 93 | 74 | 86 | 64 | 79 | 89 |
Ai et al. [19] (2023) | DEEPCYPs | 82 | 90 | 82 | 85 | 64 | 82 | 82 | |
Qiu et al. [23] (2022) | GCNN | 84 | 93 | 82 | 88 | 70 | 82 | 91 | |
Ngyuyen et al. [24] (2021) | iCYP-MFE | 84 | 92 | 82 | 86 | 68 | 84 | 92 | |
Ours, (2025) | LiSENCE | 88 | 95 | 83 | 87 | 68 | 85 | 93 | |
Average Results of LiSENCE for all 5 isoforms | 89.2 | 97 | 92.2 | 97.3 | 87.8 | 83.3 | 93.8 |
Isoforms | Re-Scoring | Acc | AUC | Sen | Spe | MCC | F1-Score | Pre |
---|---|---|---|---|---|---|---|---|
CYP1A2 | before | 87 | 95 | 85 | 92 | 68 | 86 | 81 |
(84.0, 90.0) | (92.9, 97.1) | (80.6, 89.4) | (85.7, 98.3) | (65.3, 70.7) | (80.0, 92.0) | (75.5, 86.5) | ||
after | 88 | 97 | 87 | 93 | 70 | 87 | 83 | |
(85.0, 91.0) | (94.9, 99.1) | (82.6, 91.4) | (86.7, 99.3) | (67.3, 72.7) | (81.0, 93.0) | (77.5, 88.5) | ||
CYP2C9 | before | 87 | 93 | 79 | 80 | 66 | 78 | 85 |
(84.0, 90.0) | (90.9, 95.1) | (74.6, 83.4) | (73.7, 86.3) | (63.3, 68.7) | (72.0, 84.0) | (79.5, 90.5) | ||
after | 88 | 92 | 80 | 84 | 67 | 74 | 82 | |
(85.0, 91.0) | (89.9, 94.1) | (75.6, 84.4) | (77.7, 90.3) | (64.3, 69.7) | (68.0, 80.0) | (76.5, 87.5 | ||
CYP2C19 | before | 92 | 91 | 80 | 92 | 67 | 77 | 90 |
(63.4, 68.6) | (87.1, 94.9) | (73.9, 82.1) | (88.2, 95.8) | (60.3, 71.7) | (71.0, 77.0) | (77.9, 84.1) | ||
after | 92 | 94 | 82 | 93 | 69 | 79 | 93 | |
(89.0, 95.0) | (91.9, 96.1) | (77.6, 86.4) | (86.7, 99.3) | (66.3, 71.7) | (73.0, 85.0) | (87.5, 98.5) | ||
CYP2D6 | before | 92 | 95 | 88 | 81 | 65 | 76 | 87 |
(89.0, 95.0) | (92.9, 97.1) | (83.6, 92.4) | (74.7, 87.3) | (62.3, 67.7) | (70.0, 82.0) | (81.5, 92.5) | ||
after | 93 | 93 | 89 | 83 | 63 | 78 | 88 | |
(90.0, 96.0) | (90.9, 95.1) | (84.6, 93.4) | (76.7, 89.3) | (60.3, 65.7) | (72.0, 84.0) | (82.5, 93.5) | ||
CYP3A4 | before | 88 | 95 | 83 | 87 | 68 | 85 | 93 |
(85.0, 91.0) | (78.6, 87.4) | (78.6, 87.4) | (80.7, 93.3) | (65.3, 70.7) | (79.0, 91.0 | (87.5, 98.5) | ||
after | 88 | 94 | 85 | 89 | 70 | 86 | 91 | |
(85.0, 91.0) | (91.9, 96.1) | (80.6, 89.4) | (82.7, 95.3) | (67.3, 72.7) | (80.0, 92.0) | (85.5, 96.5) |
Drugs | CYP450 Inhibits |
---|---|
Fluconazole | 2C9 and 3A4 |
Clarithromycin | 3A4 |
Cimetidine | 1A2, 2C19, 2D6, 3A4 |
Ritonavir | 3A4 and 2D6 |
Fluvoxamine | 1A2, 2C19 |
Ketoconazole | 3A4 |
Valproic Acid | 2C9, 2C19 |
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Atwereboannah, A.A.; Wu, W.-P.; Yussif, S.B.; Abdullah, M.A.; Tenagyei, E.K.; Ukuoma, C.C.; Gu, Y.H.; Al-antari, M.A. LiSENCE: A Hybrid Ligand and Sequence Encoder Network for Predicting CYP450 Inhibitors in Safe Multidrug Administration. Mathematics 2025, 13, 1376. https://doi.org/10.3390/math13091376
Atwereboannah AA, Wu W-P, Yussif SB, Abdullah MA, Tenagyei EK, Ukuoma CC, Gu YH, Al-antari MA. LiSENCE: A Hybrid Ligand and Sequence Encoder Network for Predicting CYP450 Inhibitors in Safe Multidrug Administration. Mathematics. 2025; 13(9):1376. https://doi.org/10.3390/math13091376
Chicago/Turabian StyleAtwereboannah, Abena Achiaa, Wei-Ping Wu, Sophyani B. Yussif, Muhammed Amin Abdullah, Edwin K. Tenagyei, Chiagoziem C. Ukuoma, Yeong Hyeon Gu, and Mugahed A. Al-antari. 2025. "LiSENCE: A Hybrid Ligand and Sequence Encoder Network for Predicting CYP450 Inhibitors in Safe Multidrug Administration" Mathematics 13, no. 9: 1376. https://doi.org/10.3390/math13091376
APA StyleAtwereboannah, A. A., Wu, W.-P., Yussif, S. B., Abdullah, M. A., Tenagyei, E. K., Ukuoma, C. C., Gu, Y. H., & Al-antari, M. A. (2025). LiSENCE: A Hybrid Ligand and Sequence Encoder Network for Predicting CYP450 Inhibitors in Safe Multidrug Administration. Mathematics, 13(9), 1376. https://doi.org/10.3390/math13091376