Available online on 20.05.2022 at http://jddtonline.info

Journal of Drug Delivery and Therapeutics

Open Access to Pharmaceutical and Medical Research

Copyright  © 2011-2022 The  Author(s): This is an open-access article distributed under the terms of the CC BY-NC 4.0 which permits unrestricted use, distribution, and reproduction in any medium for non-commercial use provided the original author and source are credited

Open Access  Full Text Article                                                                                                                                                                    Research Article 

In silico Target Class Prediction and Probabilities for Plant Derived Omega 3 Fatty Acid from Ethyl Acetate Fraction of Moringa oleifera Leaf Extract

Murugan M.¹, Krishnaveni K.², Sabitha M.¹, Kandeepan C.¹, Senthilkumar N.³, Loganathan T.⁴, Grace Lydial Pushpalatha G.⁵, Pandiarajan G.⁶, Ramya S.⁷, Jayakumararaj R.⁸*

¹ PG & Research Department of Zoology, Arulmigu Palaniandavar College of Arts & Culture, Palani – 624601, Dindigul District, TN, India

² Department of Zoology, GTN Arts & Science College, Dindigul - 624005, TN, India

³ Institute of Forest Genetics & Tree Breeding (IFGTB), Indian Council of Forestry Research & Education (ICFRE), Coimbatore – 641002, TN, India 

⁴ Department of Plant Biology & Plant Biotechnology, LN Government College (A), Ponneri - 601204, TN, India

⁵ PG Department of Botany, Sri Meenakshi Government Arts College, Madurai-625002, TN, India

⁶ Department of Botany, Sri S Ramasamy Naidu Memorial College (A), Sattur - 626203 TN, India

⁷ PG Department of Zoology, Yadava College (Men), Thiruppalai - 625014, Madurai, TN, India

⁸ PG Department of Botany, Government Arts College, Melur – 625106, Madurai District, TN, India

INTRODUCTION

Plant Derived Omega 3 Fatty Acid - ALA is considered as an essential fatty acid because it is required for human health, but cannot be synthesized by humans. It is a type of natural fatty acid, commonly known as octadecatrienoic acids¹. Humans can synthesize omega-3 fatty acids from ALA, which includes eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). EPA is a precursor of the series-3 prostaglandins, the series-5 leukotrienes and the series-3 thromboxanes². Dietary omega-3 long chain polyunsaturated fatty acids (n-3 LC-PUFA) like eicosapentaenoic acid (EPA, 20:5n-3) and docosahexaenoic acid (DHA, 22:6n-3) are potent metabolic regulators with therapeutic and preventive effects³. These eicosanoids have anti-inflammatory and anti-atherogenic properties⁴. However, it must be pointed out that EPA and DHA are more potent than ALA and SDA (Steridonic acid) in reducing the risk of cardiovascular diseases⁵, metabolic inflammation⁶, cancer⁷ and nervous system (CNS) disorders⁸. ALA has been reported to inhibit the production of pro-inflammatory eicosanoids, prostaglandin E2 (PGE2)⁹ and leukotriene B4 (a pro-inflammatory lipid mediator and potent chemo-attractant acting via two G protein-coupled receptors (GPCRs))¹⁰, as well as pro-inflammatory cytokines, tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 beta (IL-1 beta)¹¹. 

PDO3FA like ALA and its by-products can modulate the expression of several genes, including those involved in fatty acid metabolism and inflammation^12,13. They regulate gene expression by affecting transcription factors including NF-kappa B and members of peroxisome proliferator-activated receptor (PPAR) family^14,15 or through inhibition of NLRP3 inflammasome activation^16,17. Incorporation of ALA and its metabolites in cell membranes can affect membrane fluidity¹⁸. ALA provide protection against lipopolysaccharide-induced acute lung injury through anti-inflammatory and anti-oxidative pathways¹⁹ besides inhibition of platelet aggregation and possibly in anti-proliferative actions of ALA by delta6 desaturease²⁰.

ALA is a long chain polyunsaturated fatty acid (LC-PUFA) precursor to longer n−6 fatty acids commonly known as omega-6 fatty acids²¹. Omega-6 fatty acids are characterized by a carbon-carbon double bond at the sixth carbon from the methyl group²². Similarly, PUFA alpha-linoleic acid (ALA) is the precursor to n-3 fatty acids known as PDO3FA is characterized by a carbon-carbon double bond at the 3^rd carbon from the methyl group²³. ALA undergoes a series of conversions to reach their final fatty acid form²⁴. ALA enters the cell and is catalyzed to gamma-linolenic acid (GLA) by acyl-CoA 6-desaturase (delta-6-desaturase/fatty acid desaturase 2). GLA is then converted to dihomo-gammalinolenic acid (DGLA) by elongation of very long chain fatty acids protein 5 (ELOVL5). DGLA is then converted to arachidonic acid (AA) by acyl-CoA (8-3)-desaturase (delta-5-desaturase/fatty acid desaturase). Arachidonic acid is then converted to a series of short lived metabolites called eicosanoids before reaching its final form^25,26.

ALA chemically (18:3n-3 or 3n-6) is a carboxylic acid with 18 carbons and three cis double bonds. ALA is present in either cis ('Z') or trans ('E') conformation in the plants. Intake of ALA decrease the risk of cardiovascular diseases by 1) prevent recurrent ventricular arrhythmias that can lead to sudden cardiac death, 2) decrease risk of thrombosis (blood clot formation) that can lead to heart attack or stroke, 3) decrease serum triglyceride levels, 4) slow growth of atherosclerotic plaque, 5) improve vascular endothelial function, 6) lower blood pressure and 7) decrease inflammation^27,28. ALA deficiency may lead to visual impairment and sensory neuropathy, scaly and hemorrhagic skin or scalp inflammations.²⁷ Therefore PDO3FA has to be complemented through food or supplements to maintain the balance between omega-3 and omega-6. Clinical studies have shown that PDO3FA has a preventive and therapeutic effect for multiple sclerosis, anxiety, depression, dyslipidemia, coronary heart disease, metabolic syndrome, diabetes and cancer related complications.^23,28

Moringa oleifera is a traditional medicinal plant used in treating myriads of ailments and diseases including body pain, weakness, fever, asthma, cough, blood pressure, arthritis, diabetes, epilepsy, wound, and skin infection. Recently, its phytocompounds have been used to treat diseases like HIV/ AIDs, chronic anemia, cancer, malaria and hemorrhage. Of its several species, M. oleifera is widely cultivated species due to its multifarious uses in the management of health and disease²⁹. M. oleifera is native to India, however, cultivated all over the world. The plant is a deciduous tree with brittle stem, whitish-gray corky bark with branches; leaves pale green, bipinnate/ tri-pinnate with opposite, ovate leaflets^30-33. All parts of the plant including leaves, roots, pods, seeds, and flowers have been explored for their nutraceutical and pharmaceutical properties. Pharmacological studies indicate that extracts obtained from the plant have significant medicinal properties^34-51. Aim of this study is to bioprospect ALA from the leaves of MO for its molecular and biological properties. There is a growing interest in using ALA for a number of purposes related to health and disease, but there isn't enough reliable information to whether it might be practically helpful. This study provides the baseline ADMETox information to develop PDO3FA – ALA as a novel lead in drug discovery.

MATERIALS AND METHODS

In silico Drug-Likeliness and Bioactivity Prediction

The drug likeliness and bioactivity of selected molecule was analyzed using the Molinspiration server (http://www.molinspiration.com). Molinspiration tool is cheminformatics software that provides molecular properties as well as bioactivity prediction of compounds. In Molinspiration-based drug-likeness analysis, there are two important factors, including the lipophilicity level (log P) and polar surface area (PSA) directly associated with the pharmacokinetic properties (PK) of the compounds.⁵² In Molinspiration-based bioactivity analysis, the calculation of the bioactivity score of compounds toward GPCR ligands, ion channel modulators, kinase inhibitors, nuclear receptor ligands, protease inhibitors, and other enzyme targets were analyzed by Bayesian statistics.⁵³ This was carried out for G protein-coupled receptors (GPCR), ion channels, kinases, nuclear hormone receptors, proteases, and other enzymes (RdRp), are the major drug targets of most of the drugs.⁵⁴

In silico ADMET Analysis

SwissADME: is a Web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, druglikeness and medicinal chemistry friendliness, among which in-house proficient methods such as iLOGP (a physics-based model for lipophilicity) or the BOILED-Egg. It is the first online tool that enables ADME-related calculation for multiple molecules, allowing chemical library analysis and efficient lead optimization.⁵⁵ PK properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), of fatty acids were predicted using admerSAR v2.0 server (http://lmmd.ecust.edu.cn/admetsar2/) and admerSAR server is an open-source computational tool for prediction of ADMET properties of compounds, which makes it a practical platform for drug discovery and other pharmacological research. In ADMET analysis, absorption (A) of good drugs depends on factors such as membrane permeability [designated by colon cancer cell line (Caco-2)]⁵⁶, human intestinal absorption (HIA)⁵⁷, and status of either P-glycoprotein substrate or inhibitor⁵⁸. The distribution (D) of drugs mainly depends on the ability to cross blood-brain barrier (BBB)⁵⁹. The metabolism (M) of drugs is calculated by the CYP, MATE1, and OATP1B1-OATP1B3 models⁶⁰. Excretion (E) of drugs is estimated based on the renal OCT substrate. Toxicity (T) of drugs is predicted on Human Ether-A-Go-Go related gene inhibition, carcinogenic status, mutagenic status, and acute oral toxicity^61,62.

vNN model building and analysis

vNN method was used to calculate the similarity distance between molecules in terms of their structure, and uses a distance threshold to define a domain of applicability to ensures that the predictions generated are reliable. vNN models can be built keeping quantitative structure–activity relationship (QSAR) models up-to-date to maintain their performance levels. Performance characteristics of the models are comparable, and often superior to those of other more elaborate model.⁶³ One of the most widely used measures of similarity distance between two small molecules is Tanimoto distance, d, which is defined as:

where n(P∩Q) is number of features common to molecules p and q, and n(P) and n(Q) are the total numbers of features for molecules p and q, respectively. The predicted biological activity y is given by a weighted across structurally similar neighbours:

where d_i denotes Tanimoto distance between a query molecule for which a prediction is made and a molecule i of the training set; d₀ is a Tanimoto-distance threshold, beyond which two molecules are no longer considered to be sufficiently similar to be included in the average; y_i is the experimentally measured activity of molecule i; v denotes the total number of molecules in the training set that satisfies the condition d_i≤d₀; and h is a smoothing factor, which dampens the distance penalty. Values of h and d₀ are determined from cross-validation studies. To identify structurally similar compounds, Accelrys extended-connectivity fingerprints with a diameter of four chemical bonds (ECFP4) was used which have previously been reported to show good overall performance.^64,65

Model Validation

A 10-fold cross-validation (CV) procedure was used to validate new models and to determine the values of smoothing factor h and Tanimoto distance d₀. In this procedure, data was randomly divided into 10 sets, and used 9 to develop the model and 10^th to validate it, this process was repeated 10 times, leaving each set of molecules out once.

Performance Measures

Following metrics were used to assess model performance. (1) sensitivity measures a model’s ability to correctly detect true positives, (2) specificity measures a model’s ability to detect true negatives, (3) accuracy measures a model’s ability to make correct predictions and (4) kappa compares the probability of correct predictions to the probability of correct predictions by chance (its value ranges from +1 (perfect agreement between model prediction and experiment) to –1 (complete disagreement), with 0 indicating no agreement beyond that expected by chance).

where TP, TN, FP, and FN denote the numbers of true positives, true negatives, false positives, and false negatives, respectively. Kappa is a metric for assessing the quality of binary classifiers. Pr (e) is an estimate of the probability of a correct prediction by chance. It is calculated as:

The coverage is the proportion of test molecules with at least one nearest neighbour that meets the similarity criterion. The coverage is a measure of how many test compounds are within the applicability domain of a prediction model.

RESULTS AND DISCUSSION

3D structure, molecular and biological properties of PDO3FA-ALA in M. oleifera, its physicochemical properties, lipophilicity properties, water solubility properties, pharmacokinetic properties, druglikeness, medicinal chemistry properties⁶⁶, ADMET properties of ALA from M. oleifera is provided in Table 1-3 respectively.   Performance measures of vNN models in 10-fold cross validation using a restricted/ unrestricted applicability domain for ALA from M. oleifera (Table 4).

ADMET Predictions 

The implemented Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) prediction models, including their performance measures, have been reported previously.^62,63,67 The 15 models cover a diverse set of ADMET endpoints. Some of the models have already been published, including those for Maximum Recommended Therapeutic Dose (MRTD)⁶⁸, chemical mutagenicity⁶⁹, human liver microsomal (HLM)⁷⁰, Pgp inhibitor/substrates⁷¹.

The implemented Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) prediction models, including their performance measures, are available online. The 15 models cover a diverse set of ADMET endpoints. Models have included for Maximum Recommended Therapeutic Dose (MRTD), chemical mutagenicity, human liver microsomal (HLM), Pgp inhibitor/substrates.

Liver Toxicity - DILI: Drug-induced liver injury (DILI) has been one of the most commonly cited reasons for drug withdrawals from the market. This application predicts whether a compound could cause DILI. The dataset of 1,431 compounds was obtained from four sources. This dataset contains both pharmaceuticals and non-pharmaceuticals; a compound was classified as causing DILI if it was associated with a high risk of DILI and not if there was no such risk.

Cytotoxicity (HepG2): Cytotoxicity is the degree to which a chemical causes damage to cells. A cytotoxicity prediction model was developed using in vitro data on toxicity against HepG2 cells for 6,000 structurally diverse compounds, which was collected from ChEMBL. In developing the model, the compounds with an IC₅₀ ≤ 10 μM were considered in the in vitro assay as cytotoxic.

Metabolism HLM: The human liver microsomal (HLM) stability assay is commonly used to identify and exclude compounds that are too rapidly metabolized. For a drug to achieve effective therapeutic concentrations in the body, it cannot be metabolized too rapidly by the liver. Compounds with a half-life of 30 min or longer in an HLM assay are considered as stable; otherwise they are considered unstable. HLM data was retrieved from the ChEMBL database, manually curated the data, and classified compounds as stable or unstable based on the reported half-life (T_1/2 > 30 min was considered stable, and T_1/2 < 30 min unstable. Final dataset contained 3,654 compounds. Of these, as much as 2,313 were classified as stable; 1,341 as unstable.

Cytochrome P450 enzyme (CYP) inhibition: CYPs constitute a superfamily of proteins that play an important role in the metabolism and detoxification of xenobiotics. In vitro data derived from five main drug-metabolizing CYPs - 1A2, 3A4, 2D6, 2C9, and 2C19 was used to develop CYP inhibition models. CYP inhibitors were retrieved from PubChem and classified a compound with an IC₅₀ ≤ 10 μM for an enzyme as an inhibitor of the enzyme. Predictions for the following enzymes: CYP1A2, CYP3A4, CYP2D6, CYP2C9, and CYP2C19 has been provided. 

Membrane Transporters - BBB: The blood-brain barrier (BBB) is a highly selective barrier that separates the circulating blood from the central nervous system. VNN-based BBB model has been developed, using 352 compounds whose BBB permeability values (log-BB) were obtained from the literature respectively^72,73. Compounds with logBB values of less than –0.3 and greater than +0.3 were classified as BBB non-permeable and permeable.

Pgp Substrates and Inhibitors: P-glycoprotein (Pgp) is an essential cell membrane protein that extracts many foreign substances from the cell. Cancer cells often overexpress Pgp, which increases the efflux of chemotherapeutic agents from the cell and prevents treatment by reducing the effective intracellular concentrations of such agents—a phenomenon known as multidrug resistance. For this reason, identifying compounds that can either be transported out of the cell by Pgp (substrates) or impair Pgp function (inhibitors) is of great interest. Models to predict both Pgp substrates and Pgp inhibitors were developed as mentioned by Xu et al.⁷⁴, and Schyman et al^75-77. This dataset consists of measurements of 422 substrates and 400 non-substrates. To generate a large Pgp inhibitor dataset and both the datasets were combined⁷⁸ and removed duplicates to form a combined dataset consisting of 1,319 inhibitors/ 937 non-inhibitors⁷⁹.

hERG (Cardiotoxicity): The human ether-à-go-go-related gene (hERG) codes for a potassium ion channel involved in the normal cardiac repolarization activity of the heart. Drug-induced blockade of hERG function can cause long QT syndrome, which may result in arrhythmia and death. As much as 282 known hERG blockers from the literature were retrieved known hERG blockers from the literature and classified compounds with an IC₅₀ cut-off value of 10 μM or less as blockers, A set of 404 compounds with IC₅₀ values greater than 10 μM were collected from ChEMBL and classified them as non-blockers⁷⁶.

MMP (Mitochondrial Toxicity): Given the fundamental role of mitochondria in cellular energetics and oxidative stress, mitochondrial dysfunction has been implicated in cancer, diabetes, neurodegenerative disorders, and cardiovascular diseases. A largest dataset of chemical-induced changes in mitochondrial membrane potential (MMP), was used based on the assumption that a compound that causes mitochondrial dysfunction is also likely to reduce the MMP. A vNN- model was developed based MMP prediction model, using 6,261 compounds collected from a previous study that screened a library of 10,000 compounds (~8,300 unique chemicals) at 15 concentrations, each in triplicate, to measure changes in the MMP in HepG2 cells. The study found that 913 compounds decreased the MMP, whereas 5,395 compounds had no effect as pointed out by Li et al.,⁷⁷.

Mutagenicity (Ames test): Mutagens are chemicals that cause abnormal genetic mutations leading to cancer. A common way to assess a chemical’s mutagenicity is the Ames test. A prediction model was developed using a literature dataset of 6,512 compounds, of which 3,503 were Ames-positive as indicated in pervious study.³¹ 

MRTD: Maximum Recommended Therapeutic Dose (MRTD) is an estimated upper daily dose that is safe. A prediction model was developed based on a dataset of MRTD values publically disclosed by the FDA, mostly of single-day oral doses for an average adult with a body weight of 60 kg, for 1,220 compounds (most of which are small organic drugs). Organometallics, high-molecular weight polymers were excluded  (>5,000 Da), nonorganic chemicals, mixtures of chemicals, and very small molecules (<100 Da). An external test set of 160 compounds collected by the FDA was used for validation. The total dataset for the model contained 1,185 compounds⁶⁸. The predicted MRTD value for O3FA (mg/day unit) was calculated based upon an average adult weighing 60 kg.

Target - Probability Prediction: Cyclooxygenase-1 (PTGS1) - 0.859; Peroxisome proliferator-activated receptor gamma (PPARG) - 0.555; Peroxisome proliferator-activated receptor alpha (PPARA) - 0.555; Fatty acid binding protein muscle (FABP3) - 0.538; Free fatty acid receptor 1 (FFAR1) - 0.520; Fatty acid binding protein adipocyte (FABP4) - 0.520; Peroxisome proliferator-activated receptor delta (PPARD) - 0.520; Fatty acid binding protein epidermal (FABP5) - 0.182; Anandamide amidohydrolase (FAAH) - 0.164; Telomerase reverse transcriptase (TERT) - 0.147; Fatty acid-binding protein, liver (FABP1) - 0.112; Cannabinoid receptor 1 (CNR1) - 0.104; Acyl-CoA desaturase (SCD) - 0.095; Protein-tyrosine phosphatase 1B (PTPN1) - 0.095; DNA polymerase beta (POLB) - 0.095; Arachidonate 5-lipoxygenase (ALOX5) - 0.095; T-cell protein-tyrosine phosphatase (PTPN2) - 0.095; Prostaglandin E synthase (PTGES) - 0.095; Leukotriene B4 receptor 1 (LTB4R) - 0.095; Carboxylesterase 2 (CES2) - 0.095; Estrogen receptor beta (ESR2) - 0.086; Protein-tyrosine phosphatase 1C (PTPN6) - 0.086; Protein farnesyltransferase (FNTA FNTB) - 0.086; Nuclear receptor ROR-gamma (RORC) - 0.078; Arachidonate 12-lipoxygenase (ALOX12) - 0.078; 11-beta-hydroxysteroid dehydrogenase 1 (HSD11B1) - 0.078; DNA topoisomerase I (TOP1) - 0.078; Prostanoid IP receptor (PTGIR) - 0.078; Phosphodiesterase 4D (PDE4D) - 0.069; Nitric oxide synthase, inducible (NOS2) - 0.069; Prostanoid EP2 receptor (by homology) (PTGER2) - 0.069; Dual specificity phosphatase Cdc25A (CDC25A) - 0.069; Cytochrome P450 19A1 (CYP19A1) - 0.060; Glucocorticoid receptor (NR3C1) - 0.060; Vanilloid receptor (TRPV1) - 0.060; CD81 antigen (CD81) - 0.060; Protein kinase C eta (PRKCH) - 0.060; Receptor-type tyrosine-protein phosphatase F (LAR) (PTPRF) - 0.060; LXR-alpha (NR1H3) - 0.060; Corticosteroid binding globulin (SERPINA6) - 0.060; Testis-specific androgen-binding protein (SHBG) - 0.060; Glucose-6-phosphate 1-dehydrogenase (G6PD) - 0.060; Cytochrome P450 51 (by homology) (CYP51A1) - 0.060. G protein-coupled receptor 44 (PTGDR2) - 0.060; Protein-tyrosine phosphatase 2C (PTPN11) - 0.060; Low molecular weight phosphotyrosine protein (ACP1) - 0.060; Phospholipase A2 group 1B (PLA2G1B) - 0.060; Steroid 5-alpha-reductase 2 (SRD5A2) - 0.060; Prostanoid EP1 receptor (PTGER1) - 0.060; Estrogen receptor alpha (ESR1) - 0.060; G-protein coupled receptor 120 (FFAR4) - 0.060; HMG-CoA reductase (HMGCR) - 0.060; Niemann-Pick C1-like protein 1 (NPC1L1) - 0.060; Sigma opioid receptor (SIGMAR1) - 0.060; Cytochrome P450 17A1 (CYP17A1) - 0.060; Prostanoid EP4 receptor (PTGER4) - 0.060; Dual specificity phosphatase Cdc25B (CDC25B) - 0.060; Monocarboxylate transporter 1 (by homology) (SLC16A1) - 0.060; Interleukin-6 (IL6) - 0.060; Glutamine synthetase (GLUL) - 0.060; 11-beta-hydroxysteroid dehydrogenase 2 (HSD11B2) - 0.060; Mineralocorticoid receptor (NR3C2) - 0.060; Adenosine A3 receptor (ADORA3) - 0.060; Cyclooxygenase-2 (PTGS2) - 0.060; G-protein coupled bile acid receptor 1 (GPBAR1) - 0.060; Androgen Receptor (AR) - 0.060; MAP kinase ERK1 (MAPK3) - 0.060; Progesterone receptor (PGR) - 0.060; Prolyl endopeptidase (PREP) - 0.060; Autotaxin (ENPP2) - 0.060; Endothelin receptor ET-A (by homology) (EDNRA) - 0.060; Matrix metalloproteinase 13 (MMP13) - 0.060; Matrix metalloproteinase 3 (MMP3) - 0.060; Matrix metalloproteinase 8 (MMP8) - 0.060; Butyrylcholinesterase (BCHE) - 0.060; Cytosolic phospholipase A2 (PLA2G4A) - 0.060; Cytochrome P450 26B1 (CYP26B1) - 0.060; Cytochrome P450 26A1 (CYP26A1) - 0.060 respectively (Table 5; Figure 1). 

CONCLUSION 

In the present study ALA from M. oleifera was ADMET predicted for functional target oriented properties. It has been well established that in the human system, ALA is converted to EPA/ DHA. EPA/ DHA endowed with cardioprotective potentials lowers blood cholesterol level and reduces the risk of heart disease. With limited data, it is not obvious to conclude that ALA of MO is safe as a dietary ingredient as evidence on risks associated with ALA remains inadequate as of now. In-silico ADMET prediction data presented in the paper is hopefully expected to assist the process of drug discovery by rapid design, evaluation, and prioritization of ALA owing to its remarkable biomedical applications. 

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Figure 1: Percentage Target Probability of ALA from Moringa oleifera 

Table 1 Physicochemical, Lipophilicity, Solubility, Pharmacokinetics and Drug likeness properties of LA

Table 2: Summary of Physicochemical, Druggability & ADMET properties of LA

Table 3: In silico Drug likeliness, Biomolecular Properties and Bioactivity of ALA 

Table 4: Performance measures of vNN models in 10-fold cross validation using a restricted or unrestricted applicability domain

Table 5: Predicted Targets/ Target Class, Common Name and Probability of LA