In Silico ADME Methods Used in the Evaluation of Natural Products
Abstract
1. Introduction
2. In Silico Methods Available for ADME Predictions
2.1. Quantum Mechanics (QM) and Molecular Mechanics (MM) Methods
2.2. Molecular Docking
2.2.1. Docking for Transporter Proteins
- (a)
- Emodin was involved in four Pi interactions with the Phe974 and Phe728 residues and formed a hydrogen bond through its 3-hydroxyl with Ser975, likely contributing to its strong inhibitory effect.
- (b)
- 18β-GA interacted with Gln191 through two hydrogen bonds, while its isomer, 18α-GA, which had a much lower effect on P-gp in vitro, only had Van der Waals interactions.
- (c)
- DAG formed a hydrogen bond with Gln721, but it was involved in no other remarkable interaction with P-gp.
- (d)
- 20(S)-GF1 formed three hydrogen bonds with Gln721 and Gln191, distinguishing by this from ginsenoside Rh1, which had a very low inhibitory effect on P-gp and binds to Tyr949 via a hydrogen bond [49].
2.2.2. Docking for Proteins Involved in Drug Distribution
2.2.3. Docking for Proteins Involved in Drug Metabolism
2.3. Pharmacophore Modeling
2.4. Quantitative Structure–Activity Relationship (QSAR) Models
2.5. Molecular Dynamics (MD) Simulations
2.5.1. Applications for Natural Products
2.5.2. Refinement and Integration with Other Methods
2.6. Physiologically Based Pharmacokinetics (PBPK) Modeling
- At the clinical trial design stage, they can predict how drug formulation and food intake will influence pharmacokinetics, guiding initial human studies and forecast drug–drug interactions mediated by enzymes or transporters, informing inclusion/exclusion criteria, dose selection, and potentially waiving unnecessary clinical interaction studies or studies where enrolling subjects is anticipated to be difficult.
- They can predict appropriate dosing regimens for different pediatric subsets, from newborns to adolescents, by enabling informed selection of sampling timepoints and proposing suitable doses.
- They can predict exposure to the drug in patients with impaired renal or hepatic function, guiding organ impairment studies or supporting decisions to waive such studies.
- They can estimate the drug disposition in the mother and fetus, aiding in optimizing the therapeutic benefit-risk ratio during pregnancy.
- They can predict pH-mediated drug–drug interactions in patients receiving proton pump inhibitors or antacids, guiding formulation development and efforts to minimize food–drug interactions [221].
3. Limitations of in Silico Methods
3.1. General Limitations
- (a)
- Dependence on data quality and quantity. Most in silico models (including those used for PK purposes) are heavily dependent on the quality of the initial data, as well as their volume. This is due to the nature of those models, but research confirmation investigating specifically the influence of data on model results are available in the literature [232]. For instance, QM/MM methods, which are otherwise physics-based, require accurate initial structures of the protein–ligand complex (generated experimentally with X-ray crystallography, cryo-EM, or NMR) [233], mechanistic studies to understand a reaction steps, transition states, reaction intermediates, etc. [234,235] Often QM/MM methods are applied to data generated in MD simulations, and in such a case, if the MD simulation has been impacted by a structure error or missing atom, it will get propagated further to the QM/MM predictions [236]. Ligand–protein docking results are also impacted not only by computation algorithms but, to a good extent, by initial experimental data, as follows: the protein structure (as confirmed by X-ray crystallography, cryo-EM, or NMR) and the known binding site (or predicted on the basis of other experimental data for other proteins) are dependent on the quality of the experimental data. Even the scoring functions used in molecular docking are often empirical, being built by training on experimental data sets or knowledge-based functions, derived from 3D-structures of protein data sets, although “pure”, physics-based scoring functions are also available [237,238]. QSAR models are by their nature directly dependent on the quality and quantity of the training and testing data sets [239], and the situation is very similar for pharmacophore [98] and PBPK models [240].
- (b)
- Actual performance in laboratory or living systems of a drug active ingredient may often be influenced by additional factors that in silico models have not taken into account [238]. For instance, an oral absorption prediction model might predict high absorption based on passive diffusion and membrane permeability, but if the model ignores an active efflux mechanism (involving the Pgp, for instance), it might wrongly overpredict oral bioavailability. Likewise, a model might undervalue the importance of uptake transporters such as OATPs, which are often involved in hepatic drug uptake [241], or overlook differences in CYP enzyme activity caused by genetic variations between individuals [242]—both of which can result in incorrect estimates of how drugs are absorbed or eliminated. About 20% of the compounds in a data set of 117 substances showed over 5-fold lab variability in the measurement of the fraction unbound to human plasma (with a maximum 185-fold); if a prediction is based on a low value of the range, for instance, it could result in important errors in prediction, not because the input data was wrong in itself, but because the input data were incomplete and ignored large values that could equally be found in the real world [243].
- (c)
- All in silico tools need experimental validation to be reliable and applicable in real-world contexts. “Dry data” generated by computers should be confirmed by “wet data” generated in wet lab experiments [244]. It is not always easy to have the validation by the same scientists (often the computational researchers do not have the tools or expertise required to perform the wet lab investigations), but non-validated data generated with computational means should always be regarded as only hypothetical until confirmed (or not) experimentally. In the case of QM/MM models, it has been argued that seemingly accurate predictions against experimental data can sometimes be the result of merely reciprocal cancelation of errors, for instance, when limitations in the QM approach and a small QM region offset each other [245] (for instance, if the QM component overestimates binding energy by 5 kcal/mol due to an imperfect approximation, while the MM component underestimates it by 5 kcal/mol due to an oversimplification, the errors cancel out—producing a result coherent with experimental data; however, the model could fail in other systems where errors do not happily balance as in this hypothetical case).
3.2. Specific Limitations
4. Is the Performance of in Silico Models Confirmed by Experimental Data?
4.1. QM and QM/MM Methods
4.2. Molecular Docking
4.3. Pharmacophore Models
4.4. QSAR Models
4.5. Molecular Dynamics
4.6. PBPK Models
5. Comprehensive ADME Tools
6. Conclusions and Future Perspectives
- Pre-2020: Fewer than 100 publications containing the phrase “in silico” in the title or abstract.
- 2021: Approximately 200 such publications.
- 2023–2024: Over 270 such publications annually.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ADMET | Absorption, distribution, metabolism, excretion, and toxicity |
ADME | Absorption, distribution, metabolism, and excretion |
QSAR | Quantitative structure–activity relationship |
PBPK | Physiologically based pharmacokinetics |
PAINS | Pan-assay interference compounds |
cLogP | Calculated partition coefficient |
LLE | Lipophilicity ligand efficiency |
SFI | Solubility forecast index |
PFI | Property forecast index |
QM | Quantum mechanics |
MNDO | Modified neglect of diatomic overlap |
AM1 | Austin model 1 |
PMn | Parametric method n |
OMn | Orthogonalization-corrected method n |
DFTB | Density-functional tight-binding |
HF | Hartree–Fock |
SCF | Self-consistent field |
MPPT | Møller–Plesset perturbation theory |
MP | Møller–Plesset perturbation theory |
CI | Configuration interaction theory |
CC | Coupled cluster |
CASSCF | Complete active space self-consistent field |
CASPT2 | Complete active space perturbation theory |
MCSCF | Multi-configurational self-consistent field |
DMRG | Density matrix renormalization group method |
DFT | Density functional theory |
DFT-D | Dispersion-corrected DFT |
GGA | Generalized gradient approximation |
MM | Molecular mechanics |
OTC | Organic cation transporter |
IUPAC | International Union of Pure and Applied Chemistry |
SBP | Structure-based pharmacophore |
PDB | Protein Data Bank |
OATn | Organic anion transporter n |
URAT1 | Urate transporter 1 |
CoMFA | Comparative Molecular Field Analysis |
HQSAR | Hologram QSAR |
PAMPA | Parallel artificial membrane permeation assay |
QSPR | Quantitative structure-property relationship |
RMS | Root mean square |
HIA | Human intestinal absorption |
CV | Cross-validation |
CCR | Correct classification rate |
MCC | Matthews correlation coefficient |
AAE | Average Absolute Error |
RMSE | Root mean square error |
AME | Absolute mean error |
BBB | Blood—brain barrier |
DMPC | Dimyristoylphosphatidylcholine |
EGCG | Epigallocatechin gallate |
MD | Molecular dynamics |
MM- PBSA | Molecular mechanics Poisson–Boltzmann surface area |
MM- GBSA | Molecular mechanics generalized born surface area |
CADD | Computer—aided drug design |
Kp | Skin permeation coefficient |
PK | Pharmacokinetics |
Smol | Solvent—accessible molecular surface |
SASA | Solvent—accessible molecular surface |
Vmol, hfob | Total volume of molecules enclosed by solvent-accessible molecular surface |
log Swat | Logarithm of aqueous solubility |
QPlogPo/w | Predicted octanol/water partition coefficient |
logKhsa | Logarithm of predicted binding constant to human serum albumin |
log B/B | Logarithm of predicted blood/brain barrier partition coefficient |
BIP caco2 | Predicted apparent Caco–2 cell membrane permeability |
MDCK | Madin—Darby Canine Kidney |
QPMDCK | Apparent MDCK cell permeability |
Indcoh | Index of cohesion interaction in solids |
Glob | Globularity descriptor |
QPpolrz | Predicted polarizability |
VDss | Volume of distribution at steady state |
HLM | Human liver microsomal stability |
RLM | Rat liver microsomal stability |
CLp | Plasma clearance |
CLr | Renal clearance |
MRT | Mean retention time |
AUC | Area under the curve |
DMPNN | Deep message passing neural networks |
nHA | Number of hydrogen acceptors |
nHD | Number of hydrogen donors |
nRot | Number of rotatable bonds |
nRing | Number of rings |
MaxRing | Number of atoms in the largest ring |
nHet | Number of heteroatoms |
fChar | Formal charge |
nRig | Number of rigid bonds |
FLuc | Firefly luciferase |
PPB | Plasma protein binding |
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Chemical Compound | Stability/Reactivity * | QM Method Used | Reference |
---|---|---|---|
Coriandrin | High molecular stability | PM6 | [26] |
Alternamide A | Highly reactive | PM3 | [25] |
γ-elemene | Least stable among the six terpene compounds evaluated | MNDO | [27] |
Adenine | Moderately reactive | PM6 | [26] |
Adenosine | Moderately reactive (more reactive than adenine) | PM6 | [26] |
Coriandrone B | Moderately stable | PM6 | [26] |
Menthone | Most stable among the six terpene compounds evaluated | MNDO | [27] |
Dihydrocoriandrin | Relatively high stability | PM6 | [26] |
Tryptophan | Relatively high stability | PM6 | [26] |
β-caryophyllene | Somewhat instable | MNDO | [27] |
β-caryophyllene oxide | Somewhat stable | MNDO | [27] |
Eucalyptol | Very stable | MNDO | [27] |
Pulegone | Very stable | MNDO | [27] |
Type of Model | Data Set Size (Training, Test Sets) | Performance (Best Model) | Outcome Variable | Reference |
---|---|---|---|---|
Regression QSAR | 86 (67, 9, 10 *) | RMS—9.4% HIA units (training), 19.7% HIA units (CV), 16.0% HIA units (external set) | Human intestinal absorption (%) | [136] |
Hologram QSAR, regression | 638 (50, 128) | R2—0.79, Q2—0.63 | Human intestinal absorption (%) | [137] |
Classification | 272 (232, 40) | Accuracy (train set)—71%, accuracy (test set): 60%. | Bioavailability data in healthy human subjects (4 classes of bioavailability: class 1 (<20%), class 2 (20–49%), class 3 (50–79%), class 4 (80–100%). | [138] |
Regression and classification models | 458 | Regression: R2—0.60 Classification: CCR—0.88, MCC—0.75 (10-fold cross-validation) | Human intestinal absorption (%). Three ordinal classes of absorption (class 1—>80%, class 2—30–80%, class 3—<30%). | [139] |
Regression models based on Abraham descriptors | 169 (38 + 131; 31 + 138) | 0.85 (train set); 0.78 (cross-validation) | Human intestinal absorption (%) | [140] |
Regression and classification | 96 (67 + 9 + 12 *) | RMS—6.5 (train set), 27.7 (test set), 22.8 (external prediction set). For classification, sensitivity 100%, specificity 50%. | Human intestinal absorption (%). For classification purposes, a 50% HIA threshold was used to define two classes. | [139,141] |
Classification QSAR, using structural descriptors | 1262 (899 + 362) | AAE—0.12 (12%); Accuracy: 79–86% | Human intestinal absorption (%, divided in six classes of about 16% per class) | [142] |
Regression models using five classes of descriptors | 169 (113 + 56) | R2—0.86 (training set), 0.73 (test set) | Human intestinal absorption (%) | [143] |
Regression model using descriptors computed based on DFT | 241 (38 + 203) | RMSE—12.8 (% HIA) (15 on the entire test set) | Human intestinal absorption (%) | [144] |
Classification QSAR using a variety of descriptor classes | 141 (+ an external data set of 27 compounds) | Accuracy: 88.9% (external data set), 65.71% (10-fold CV) | Human intestinal absorption (%) (5 classes) | [145] |
MI-QSAR (QSAR based on “descriptors explicitly derived from simulations of solutes [drugs] interacting with phospholipid membrane models”) | 188 (164 + 24) | R2 = 0.68 (train set), 0.65 (test set). | Human intestinal absorption (%) | [146] |
Regression and classification models (using a variety of descriptor classes) | 553 (455 +98) | R2—0.76 ** (train set), R2—0.79 ** (test set), AME a—7.3% (test set), Accuracy > 96.8%. | Human intestinal absorption (%) | [147] |
Multiple regression models using a variety of descriptors | 552 (380 + 172) | R2—0.64 ** (train set), R2—0.79 ** (test set) | Human intestinal absorption (%) | [148] |
Regression models using descriptors computed with two commercial products and predicted pKa | 567 (+25 + 22 ***) | R2 for log Peff b—0.72–0.84; RMSE—0.35–0.45 log units (equivalent to 2.24–2.82%) | Human intestinal absorption (%) | [149] |
Classification QSAR using multiple classification algorithms and 166 descriptors | 225 (158 + 67) | Accuracy—94% (training set), 91% (test set) c. κ statistic—0.58 | Human intestinal absorption (%). Two classes: high (>30%) and low (<30%). | [150] |
Classification QSAR using FP4 and MACCS fingerprints | 578 (480 + 98, (+634 ***) | Accuracy—98.5% (training set), 98.8% (test set), 94% (validation set) | Human intestinal absorption (%). Two classes: high (>30%) and low (<30%). | [151] |
Regression and classification QSAR using topological descriptors (computed with the CODES program) | 367 (202 + 165) d | R2 = 0.93 (train set), Q2 = 0.92 (LOO cross-validation). Global accuracy: 74%. | Human intestinal absorption (%). Three classes (cut-offs: 30%, 50% and 70%). | [152] |
Classification and regression QSAR models build with different descriptors and algorithms | 577 (78 + 489) | Accuracy: 99.37%, 99.58% (train set), 95.92%, 94.90% (test set). RMSE—6.39 (train set), 5.71 (test), R2—0.972 (train set), 0.953 (test set) | Human intestinal absorption (%). Two classes, using a 30% threshold. | [153] |
Regression QSPR models using 2D and 3D descriptors | 1272 (1017 + 255) | R2 = 0.97, Q2 = 0.83, RMSE CV = 0.31 (training test), R2 = 0.81, RMSE T = 0.31 (test set) | Caco-2 cell permeability (permeability coefficient of Caco-2 monolayer cell—Papp) | [154] |
Classification and regression QSAR/QSPR models | 141 (98, +43) | Accuracy: 0.77 (10-fold CV), 0.70 (external data set) R2: 0.38 (training set), 0.05 (external data set) | Human intestinal absorption (%). Two classes, using an 85% threshold. | [155] |
Regression QSAR using a variety of descriptors computed with the Dragon software | 160 (90 + 30 + 40) | R2—0.771 (training set), 0.716 (test set). RMSE—0.182 (training set), 0.189 (test set) | Human intestinal absorption (%)—more precisely, log10 (HIA% + 10). | [156] |
Regression QSAR using artificial neural networks | 86 (67 + 9 + 10) | R2—0.802 (test set); RMS—0.59 (train set), RMS—0.42 (test set). | Human intestinal absorption (%). | [157] |
Regression QSAR using mainly structural descriptors | 467 (417 + 50) | R2—0.79 (train set), 0.79 (test set), RMSE—12.3% HIA | Human intestinal absorption (%). | [158] |
Method | Key Limitations | Impact on PK Predictions | Potential Mitigation |
---|---|---|---|
QM | High computational costs; limited to small systems; reliance on approximations; short timescales. | Inaccurate modeling or unreasonably long times for large systems (e.g., membranes) or dynamic processes (e.g., drug transformations). | Advanced computational resources, including GPU acceleration; empirical methods for particular properties; QM/MM were pure QM methods are unrealistic. |
QM/MM | High computational costs; QM/MM boundary artifacts; poor charge transfer with small QM regions; limited sampling. | Unreasonably long times for large systems, errors in binding energies or reaction kinetics due to error cancelation or inadequate sampling. | Larger QM regions; improved boundary treatments; enhanced sampling methods, including coarse graining. |
Ligand Docking | Limited protein flexibility, ignorance of indued fit; simplified scoring functions; poor solvent treatment; often poor reporting; the docking score is not sufficient to assert the direction of effect (agonist vs. antagonist). | Errors in binding affinity or pose prediction; misidentification of agonists/antagonists. | Flexible or induced-fit docking, better scoring functions, experimental validation. |
Pharmacophore | No general scoring method; dependence on a set of pre-generated conformations; static models; dependence on good quality ligand–protein crystal structures; no canonic way of building models. | False positives or missed hits due to oversimplified models or incorrect tautomers. | Improved conformation databases, dynamic modeling. |
QSAR | Overfitting; limited applicability domain; activity cliffs. | Poor generalizability to new compounds; prediction errors for structurally similar compounds. | High quality, high size training data; use of appropriate techniques to control overfitting; use tools to control for activity cliffs, use local models, use cliff-aware descriptors (e.g., 3D, conformational, quantum). |
MD Simulations | High computational costs; imperfect force fields; short timescales; not well-suited for systems where quantum effects play a prominent role | Errors in predictions; inadequate sampling of conformational states; poor modeling of large systems. | Polarizable force fields, coarse-grained MD, enhanced sampling, use of QM/MM when quantum effects are important. |
PBPK Models | Overestimation of CYP3A4 TDI; limited non-CYP enzyme data; software customization needs; limitations in the case of proteins; areas with limited experience; limitations in tissue-specific distribution. | Errors in PK predictions; uncertainties in tissue distribution or special populations. | Imaging-based validation (e.g., PET), customized software for non-CYP enzymes. |
Method | Example | Experimental Validation | Performance Summary |
---|---|---|---|
Quantum Mechanics (QM) | Stereoselectivity of nicotine hydroxylation by CYP2A6 [277] | Yes (retrospective), computed (~97%) vs. wet lab (89–94%) | High agreement |
LogP estimation for BBB permeability (e.g., caffeine) [278] | Yes (clinical data, retrospective) | Good match with known BBB-crossing compounds | |
DFT used for C-H bond energy at the main metabolic site (e.g., acetic acid) [279] | Yes (vs. experimentally derived bond energy) | Lower bond dissociation energy at main metabolic site confirmed by experimental data as compared with other C-H bond | |
Global reactivity of 4-hydroxyisoleucine [280,281] | Indirect (predicted stability vs. independent plasma stability study) | Supported by external experimental data | |
Molecular Docking | Docking of drugs with CYP2D6 variants [280] | Yes, retrospective correlation (R2 = 0.81–0.92) | High agreement |
Flavonoids binding to Pgp [281] | Very weak; correlation r = –0.27 to 0.079 | Poor correlation despite otherwise claims | |
Lignans and flavonoids binding to Pgp [51] | Partial; 2 of 10 flavonoids experimentally confirmed | Partial success (at least 20%) | |
Abietane diterpenes binding to Pgp [282] | Yes, for 2 hemisynthesis compounds | Good performance for two tested compounds | |
Pharmacophore Models | URAT1 inhibitors [105] | Yes, 3 flavonoids of 25 hits were active (relatively low potency) | Modest performance |
CYP2D6 inhibitors [110] | Yes; 42% strong, 33% moderate inhibition | High agreement (75% activity in vitro) | |
DDIs via CYP1A2, 2C9, and 3A4 enzymes [283] | Yes (vs in vitro results obtained with fluorescence-based P450 microarrays) | 32.1–65.5% depending on model and enzyme | |
CYP3A4 inhibitors from Tripterygium wilfordii [284] | Ye (vs. in vitro enzyme inhibition assays); 3 of 5 predicted were confirmed | Good agreement | |
CYP1A2 inhibitors from herbal compounds [285] | Yes; 7 of 12 compounds active | ~58% accuracy for a combined approach (docking + pharmacophore models) | |
QSAR Models | COMFA/COMSIA for natural phenolics [163] | Yes; retrospective (r2pred = 0.78, 0.70) | Very good agreement |
Intestinal absorption prediction [286] | Yes; 83% predictions within 2-fold of observed values | Comparable to in vitro method | |
Drug absorption in rats [287] | Reliability comparable to the Caco-2 and 2/4/A1 cell lines | Very good agreement | |
Molecular Dynamics (MD) | Withaferin-A and withanone membrane permeability [288] | Yes; imaging based on antibody detection confirmed MD predictions | Excellent agreement |
Curcumin and quercetin binding to CYP3A4 and displacing CDK inhibitors [289] | Yes; docking, MD, and IC50 (in vitro) | Excellent agreement in several validation approaches | |
PBPK Models | Oxymatrine dose prediction [290] | Yes; compared to clinical dose | Predicted dose (367 mg TID) aligned with clinical recommendation |
Prediction of DDIs for hyperforin with sedative-hypnotics in human patients [291] | Yes—model predictions compared with known clinical interactions | Close agreement, all predictions within acceptable margin of error | |
PK of hydrastine and berberine [292] | Yes—validated against observed clinical data | Close fit to human PK data | |
PK of single dose and multiple dose administration of piperine [293] | Yes—validated against actual clinical data | All error values below the two-fold acceptance criterion |
Features | SwissADME | pkCSM | ADMETlab 3.0 | admetSAR 3.0 |
---|---|---|---|---|
Physicochemical properties | 12 | 7 * | 21 | 10 |
Medicinal chemistry endpoints | 10 ** | 0 | 20 | 4 ** |
Absorption *** endpoints | 3 (C) | 3 (N) | 9 (2N, 7C) | 14 (6N, 6C) |
Distribution endpoints | 1 (C) | 4 (N) | 9 (3N, 6C) | 11 (1N, 12C) |
Metabolism endpoints | 5 (C) | 7 (C) | 14 (C) | 15 (C) |
Excretion endpoints | 0 | 2 (1N, 1C) | 2 (N) | 4 (2N, 2C) |
PAINS included | Yes | No | Yes | No |
Batch evaluation/API support | Multiple smiles allowed | Limit to 100 smiles | Input limited to one smile, but API available | Batch prediction allowed for 1000 molecules. |
Interpretation help | ++ | ++ | +++ | ++ |
Uncertainty estimation | No | No | Yes (prediction probabilities for categorical predictions converted into six symbols) | Yes (prediction probabilities for categorical predictions) |
Availability | Free | Free | Free | Free |
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Ancuceanu, R.; Lascu, B.E.; Drăgănescu, D.; Dinu, M. In Silico ADME Methods Used in the Evaluation of Natural Products. Pharmaceutics 2025, 17, 1002. https://doi.org/10.3390/pharmaceutics17081002
Ancuceanu R, Lascu BE, Drăgănescu D, Dinu M. In Silico ADME Methods Used in the Evaluation of Natural Products. Pharmaceutics. 2025; 17(8):1002. https://doi.org/10.3390/pharmaceutics17081002
Chicago/Turabian StyleAncuceanu, Robert, Beatrice Elena Lascu, Doina Drăgănescu, and Mihaela Dinu. 2025. "In Silico ADME Methods Used in the Evaluation of Natural Products" Pharmaceutics 17, no. 8: 1002. https://doi.org/10.3390/pharmaceutics17081002
APA StyleAncuceanu, R., Lascu, B. E., Drăgănescu, D., & Dinu, M. (2025). In Silico ADME Methods Used in the Evaluation of Natural Products. Pharmaceutics, 17(8), 1002. https://doi.org/10.3390/pharmaceutics17081002