NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Preparation
2.2. Principal Component Analysis
2.3. Model Building
2.4. Similarity Maps
3. Results
3.1. Compilation of Data Sets for Model Development
3.2. Analysis of the Physicochemical Properties of Natural Products and Synthetic Molecules
3.3. Model Development and Selection
3.4. Model Validation
3.5. Comparison of Model Performance with the NP-Likeness Calculator
3.6. Analysis of Class Probability Distributions for Different Data Sets
3.7. Analysis of Discriminative Features of Natural Products and Synthetic Molecules
3.8. Similarity Maps
3.9. NP-Scout Web Service
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Name 1 | Number of Molecules in SMILES Notation Successfully Parsed with RDKit | Number of Unique Molecules After Data Preprocessing | Scientific Literature and/or Online Presence |
---|---|---|---|
UNPD | 229,140 | 161,228 | [54,55] |
TCM Database@Taiwan | 56,325 | 45,422 | [56,57] |
NP Atlas | 20,018 | 18,358 | [53] |
TCMID | 13,188 | 10,918 | [58,59] |
TIPdb | 8838 | 7620 | [60,61,62] |
Ambinter and Greenpharma NPs | 7905 | 6680 | [63,64] |
AnalytiCon Discovery MEGx | 4315 | 4063 | [65] |
NANPDB | 6841 | 3734 | [66,67] |
StreptomeDB | 3990 | 3353 | [68,69] |
NPs of PubChem Substance Database | 3533 | 2638 | [70,71] |
NuBBE | 1856 | 1637 | [72,73] |
Pi Chemicals NPs | 1783 | 1511 | [74] |
NPCARE | 1613 | 1479 | [75,76] |
NPACT | 1516 | 1376 | [77,78] |
InterBioScreen NPs | 1359 | 1116 | [79] |
AfroDb | 954 | 865 | [80,81] |
TargetMol Natural Compound Library | 850 | 745 | [82] |
HIM | 1284 | 641 | [83,84] |
SANCDB | 623 | 588 | [85,86] |
UEFS Natural Products | 493 | 469 | via ZINC [40,87] |
p-ANAPL | 538 | 456 | [88] |
NCI/NIH DTP NP set IV | 419 | 394 | [89] |
HIT | 707 | 362 | [90,91] |
AfroCancer | 388 | 352 | [92,93] |
AfroMalariaDB | 265 | 250 | [94,95] |
AK Scientific NPs | 242 | 177 | [96] |
Selleck Chemicals NPs | 173 | 163 | [97] |
NP data set TOTAL | - | 201761 |
Test Method | Metric 1 | MOE Two-Dimensional Descriptors | Morgan2 Fingerprints (1024 Bits) | MACCS Keys | NP-Likeness Calculator |
---|---|---|---|---|---|
10-fold cross-validation | AUC | 0.997 | 0.997 | 0.996 | / |
MCC | 0.953 | 0.958 | 0.950 | / | |
Independent test set | AUC | 0.997 | 0.997 | 0.997 | 0.997 |
MCC | 0.954 | 0.960 | 0.960 | 0.959 |
Identifier Used by MOE | Feature Importance 1 | Description |
---|---|---|
a_nN | 0.103 | Number of nitrogen atoms. |
a_ICM | 0.051 | Entropy of the element distribution in the molecule. |
chiral_u | 0.045 | Number of unconstrained chiral centers. |
GCUT_SLOGP_0 | 0.045 | Descriptor derived from graph distance adjacency matrices utilizing atomic contribution to log P. |
SlogP_VSA0 | 0.044 | Surface area descriptor taking into account the contributions of individual atoms to log P. |
chiral | 0.042 | Number of chiral centers. |
GCUT_SLOGP_3 | 0.036 | Descriptor derived from graph distance adjacency matrices utilizing atomic contribution to log P. |
a_nO | 0.025 | The number of oxygen atoms. |
GCUT_PEOE_0 | 0.025 | Descriptor derived from graph distance adjacency matrices utilizing partial equalization of orbital electronegativities charges. |
SlogP_VSA1 | 0.024 | Surface area descriptor taking into account the contributions of individual atoms to log P. |
Similarity Map 1 | Name | Source 2 | NP Class Probability | Disease Indication | Year Introduced |
---|---|---|---|---|---|
| arglabin | N | 1.0 | anticancer | 1999 |
| cefonicid sodium | ND | 0.34 | antibacterial | 1984 |
| dutaseride | ND | 0.18 | benign prostatic hypertrophy | 2001 |
| vorapaxar | ND | 0.30 | coronary artery disease | 2014 |
| empagliflozin | S*/NM | 0.67 | antidiabetic (diabetes 2) | 2014 |
| belinostat | S*/NM | 0.09 | anticancer | 2014 |
| febuxostat | S/NM | 0.19 | hyperuricemia | 2009 |
| zalcitabine | S* | 0.46 | antiviral | 1992 |
| bilastine | S | 0.17 | antihistamine | 2011 |
| perampanel | S | 0.16 | antiepileptic | 2012 |
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Chen, Y.; Stork, C.; Hirte, S.; Kirchmair, J. NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules. Biomolecules 2019, 9, 43. https://doi.org/10.3390/biom9020043
Chen Y, Stork C, Hirte S, Kirchmair J. NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules. Biomolecules. 2019; 9(2):43. https://doi.org/10.3390/biom9020043
Chicago/Turabian StyleChen, Ya, Conrad Stork, Steffen Hirte, and Johannes Kirchmair. 2019. "NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules" Biomolecules 9, no. 2: 43. https://doi.org/10.3390/biom9020043