# Modeling Structure–Activity Relationship of AMPK Activation

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## Abstract

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## 1. Introduction

## 2. Results

#### 2.1. Similarity of Groups

#### 2.2. Statistical Comparison of Datasets

#### 2.3. Random Forest Classification (RFC)

#### 2.4. Support Vector Machine Classification (SVM-C)

#### 2.5. Stochastic Gradient Boosting (SGB) Analysis

#### 2.6. Logistic Regression Classification (LRC)

_{2}-norm and as optimizer the Newton-cg was applied. The class weight was “balanced” to include automatic calculation of weights to account for different class sizes.

#### 2.7. Deep Neural Network (DNN) Analysis

#### 2.8. Test Performance

## 3. Discussion

## 4. Materials and Methods

#### 4.1. Data

- “AMPK AND activation”
- “AMPK AND inhibition”

_{50}was ≤0.1 μM to identify proven activators. In addition, compounds were included that were confirmed activators by at least one PubMed-listed publication. On the other hand, tested compounds shown to be inactive for AMPK activation or showing inhibitory function or compounds described in the literature as inhibitors of AMPK formed the control group for this analysis.

#### 4.2. Data Preprocessing

- with their names, smiles codes, PubChem IDs, and PubMed IDs (Compounds.csv); and
- with all calculated PaDel descriptors (Data.csv) in https://github.com/cptbern/QSAR_AMPK, accessed on 27 October 2021.

#### 4.3. Validation

#### 4.4. Similarity

_{i}and x

_{j}denote the i-th and j-th instance and each descriptor values of the respective group, respectively (i, j = 0, …, N, the number of elements of the group, i ≠ j), S denotes the number of pairwise differences.

#### 4.5. Machine Learning Models

#### 4.6. Random Forest Classification (RFC)

#### 4.7. Stochastic Gradient Boosting Classification (SGB)

#### 4.8. Support Vector Machine Classification (SVM-C)

#### 4.9. Logistic Regression Classification (LRC)

_{1}-norm, l

_{2}-norm, and elastic net) were evaluated.

#### 4.10. Deep Learning Neural Network (DNN)

#### 4.11. Model Evaluation

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Sample Availability

## References

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**Figure 1.**t-distributed stochastic neighbor embedding (tSNE) analysis: AMPK activators and controls.

**Figure 2.**tSNE analysis of chemically classified activators and controls separated by chemical structure. (

**A**) AMPK activators (N = 904); (

**B**) AMPK control (N = 799).

**Figure 3.**Feature importance (standard deviation) of the first 10 features for random forest classification; nAcid = number of acidic groups; ALogP = Ghose-Crippen LogKow; ALogP2 = square of ALogP; AMR = molar refractivity; apol = sum of the atomic polarizabilities (including implicit hydrogens); naAromAtom = number of aromatic atoms; nAromBond = number of aromatic bonds; nAtom = number of atoms; nHeavyAtom = number of heavy atoms; nH = number of hydrogen atoms.

**Figure 4.**Receiver operating characteristic (ROC) of the investigated methods. (

**a**) Random Forest classifier, (

**b**) Support Vector Machine classifier, (

**c**) Stochastic Gradient Boosting classifier, (

**d**) Logistic Regression classifier, and (

**e**) Deep Neural Network classifier.

Method | Training Accuracy (%) | Test Accuracy (%) | Y-Randomization ** (%) | Test Precision (%) | Sensitivity (%) | Specificity (%) | AUC * |
---|---|---|---|---|---|---|---|

RFC | 91.6 | 92.6 | 52.7 ± 2.3 | 90.3 | 91.2 | 94.0 | 0.968 ± 0.013 |

SVM-C | 91.0 | 93.0 | 53.2 ± 2.2 | 90.1 | 93.5 | 92.4 | 0.962 ± 0.009 |

SGB | 91.3 | 93.0 | 52.8 ± 2.2 | 90.7 | 92.0 | 94.0 | 0.968 ± 0.012 |

LRC | 90.8 | 91.0 | 52.6 ± 2.1 | 89.2 | 97.4 | 94.8 | 0.948 ± 0.014 |

DNN | 91.6 | 90.6 | 53.0 ± 1.8 | 87.6 | 90.2 | 91.1 | 0.970 ± 0.002 |

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**MDPI and ACS Style**

Drewe, J.; Küsters, E.; Hammann, F.; Kreuter, M.; Boss, P.; Schöning, V.
Modeling Structure–Activity Relationship of AMPK Activation. *Molecules* **2021**, *26*, 6508.
https://doi.org/10.3390/molecules26216508

**AMA Style**

Drewe J, Küsters E, Hammann F, Kreuter M, Boss P, Schöning V.
Modeling Structure–Activity Relationship of AMPK Activation. *Molecules*. 2021; 26(21):6508.
https://doi.org/10.3390/molecules26216508

**Chicago/Turabian Style**

Drewe, Jürgen, Ernst Küsters, Felix Hammann, Matthias Kreuter, Philipp Boss, and Verena Schöning.
2021. "Modeling Structure–Activity Relationship of AMPK Activation" *Molecules* 26, no. 21: 6508.
https://doi.org/10.3390/molecules26216508