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Article

Pharmacophore-Based Virtual Screening of Alkaloids and Flavonoids for Designing Drugs with Inhibitory Activity on the Enzyme Monoamine Oxidase B

by
Ana Carolina de Jesus Silva
1,†,
Ana Beatriz Bezerra dos Santos
1,†,
Mariana Pegrucci Barcelos
2,†,
Carlos Henrique Tomich de Paula da Silva
2,† and
Lorane Izabel da Silva Hage-Melim
1,*,†
1
Laboratory of Pharmaceutical and Medicinal Chemistry, Federal University of Amapá, Macapá 68903-419, Brazil
2
Computational Laboratory of Pharmaceutical Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto 14040-903, Brazil
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2025, 15(18), 10162; https://doi.org/10.3390/app151810162
Submission received: 24 July 2025 / Revised: 21 August 2025 / Accepted: 27 August 2025 / Published: 18 September 2025
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Abstract

Parkinson’s disease is a neurodegenerative disorder characterized by the degeneration of dopaminergic neurons, resulting in multiple motor and cognitive impairments. Among the hypotheses proposed for its etiology, oxidative stress mediated by the enzyme monoamine oxidase B (MAO-B) stands out, as it is directly associated with dopamine metabolism. In this context, the search for molecules with potential antiparkinsonian activity and low toxicity, particularly those of natural origin, has been extensively investigated using computational approaches. In the present study, a pharmacophore-based virtual screening was carried out on molecules belonging to the alkaloid and flavonoid groups, followed by the evaluation of their pharmacokinetic, toxicological, and biological activity profiles, as well as ligand–receptor interaction analysis through molecular docking. The results indicated that palmatine, genistein, ZINC00597214, and ZINC72342127 exhibited superior performance compared to the other analyzed structures, considering all evaluated criteria. Therefore, this study, through in silico methodologies, demonstrated the antiparkinsonian potential of several chemical structures, attributable to their inhibitory activity on the MAO-B enzyme. Further experimental investigations, both in vitro and in vivo, are necessary to more comprehensively characterize the properties of these molecules, with the ultimate goal of developing new therapeutic strategies for the treatment of Parkinson’s disease.

1. Introduction

Considered the most frequent movement disorder and the second-most prevalent neurodegenerative disease [1], Parkinson’s disease (PD) is a chronic, neurodegenerative, progressive, and disabling disorder, characterized by the destruction of dopaminergic neurons, located in the compact portion of the substantia nigra [2,3,4]. It is currently estimated that the disease affects about 1% of the world population aged 65 years and older, with its prevalence expected to double by 2030 [5]. In industrialized countries, Parkinson’s disease is estimated to affect approximately 0.3% of the general population, with prevalence rates of 1% among individuals over 60 years of age and 3% in those over 80 [1]. While the incidence in people under 50 years old is considered rare, it increases progressively with age, reaching its highest peak in the 85 to 89 age group [3,6].
The reason why the neurodegeneration process is triggered has not yet been fully established; however, several hypotheses have been described in the literature. One such hypothesis concerns oxidative stress caused by the enzyme monoamine oxidase B (MAO-B), which is responsible for dopamine metabolism. During the degradation of dopamine, MAO-B generates the production and subsequent release of free radicals [7]. As the most prevalent isoform in the brain, MAO-B not only degrades dopamine but is also capable of metabolizing the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), which has been widely used for the induction of PD in experimental models. When oxidized by MAO-B, MPTP produces a neurotoxic byproduct, the MPP+ ion (1-methyl-4-phenylpyridine), which inhibits mitochondrial complex I activity, resulting in the release of free radicals and subsequent cellular damage, potentially leading to cell death [7,8]. Additionally, MAO-B is also involved in the accumulation of glutamate and together with free radicals they can cause molecular damage, which initiates an inflammatory process and excessive muscle contraction in addition to causing dysfunction in the mitochondrial autophagy mechanism, which is important for the elimination of toxic aggregates, further favoring the formation of inflammatory deposits [9].
As a result of dopaminergic dysfunction and the consequent reduction in dopamine levels, the symptoms most commonly associated with PD are the so-called motor symptoms or cardinal signs, which include akinesia, muscle rigidity, resting tremor, and postural instability [10]. However, non-motor symptoms are also characteristic of the disease, many of which manifest before the motor involvement itself, in what is referred to as the prodromal phase of the disease. These symptoms include, for example, behavioral sleep disturbances, olfactory loss (hyposmia), constipation, urinary dysfunction, orthostatic hypotension, excessive daytime sleepiness, and psychiatric problems such as anxiety and depression [6].
Given its prevalence and because it is a disabling disease with no cure to date, several pharmacological therapies are available which act on different molecular targets and through various mechanisms of action. Among these, the MAO-B enzyme inhibitors (MAO-B) cause a reduction in dopamine metabolism, thereby allowing greater dopaminergic stimulation in the synaptic cleft due to blocking the enzyme. They are generally used in early stages of the disease, as they improve motor symptoms and cause fewer side effects when compared to levodopa. In more advanced stages, they are often used in association with other classes to reduce or prevent motor fluctuations, in addition to the need to use levodopa [11]. However, the drugs available are of synthetic origin, such as selegiline, rasagiline, and safinamide, and have some limitations for use. Additionally, they cause side and adverse effects, which in a way, contribute to non-adherence to pharmacotherapy [12].
It is estimated that, even after approval for clinical trials, approximately 75% of drug candidates fail during one or more clinical phases, primarily due to challenges in elucidating pharmacokinetic and safety properties [13].
In this context, the search for substances capable of inhibiting the MAO-B enzyme represents a significant research focus, as such inhibition can increase dopamine concentration in the synaptic cleft, reduce enzymatic activity, and consequently decrease the production of free radicals. To achieve these objectives, compounds must exhibit a high capacity to cross the blood–brain barrier, appropriate lipophilicity, low molecular weight, and an adequate number of hydrogen bond donors and acceptors, thereby reaching the therapeutic target and effectively exerting their biological function, in addition to possessing low toxic potential. Among the various biomolecules, alkaloids and flavonoids are phytochemical groups with known pharmacological properties, including those with anti-inflammatory and antioxidant neuroprotective activity that can be studied for the treatment of different neuropathologies [14,15,16,17].
For this purpose, computational chemistry, using programs, specific software, and databases available on networks, enables the evaluation of different chemical and biological properties of various ligands. It also provides important data about a molecule and whether it can even be defined as a promising structure with pharmacological and therapeutic potential, all this on time and with less cost involved compared to experimental biological assays which are usually more expensive [18,19].
Given the limitations of current pharmacotherapy, the adverse effects associated with available medications, and the high prevalence of the disease, the investigation of new compounds, particularly those of natural origin, becomes essential for the development of drugs with potential antiparkinsonian activity. When combined with in silico studies employing tools capable of predicting and simulating various properties and profiles, the drug discovery process can be significantly optimized. Furthermore, virtual screening enables the identification of compounds with little-known properties that may emerge as promising candidates. In this context, the present study aims to evaluate, in silico, molecules belonging to the alkaloid and flavonoid classes as well as selected molecules from these groups based on a pharmacophore model in order to elucidate their potential inhibitory activity against the MAO-B enzyme and, consequently, their possible application as pharmacological therapies for Parkinson’s disease.

2. Material and Methods

2.1. Elaboration of a Pharmacophoric Model of Molecules from the Groups of Alkaloids and Flavonoids

At first, molecules belonging to the alkaloid and flavonoid groups, described in three review articles, were identified as potential candidates with antiparkinsonian activity. The first article analyzed was by Zhang et al. [20], the second by Rabiei et al. [21], and the last by Rahman et al. [22]. The search was carried out using the following descriptors: Parkinson’s disease and natural products.
After identifying all the structures described in the review articles, each molecule was then searched for in the PubChem database (https://pubchem.ncbi.nlm.nih.gov/. Accessed on 10 August 2023) and downloaded in 2D in .sdf format. For the optimization of the structures, the HyperChem v. 8.0.8 program was used alongside the use of the semi-empirical method Recife Model 1 (RM1) [23], with subsequent addition and correction of partial charges in the Discovery Studio Visualizer (DSV) program to “clean up” the geometry and reduce the energy involved in the molecules.
Subsequently, the molecules of each phytochemical group were compacted into different folders to be then inserted into the PharmaGist webserver (http://bioinfo3d.cs.tau.ac.il/PharmaGist/. Accessed on 18 August 2023), which enables the alignment of different chemical structures and the subsequent identification of similar regions of the set through quantitative data through the score [24]. The feature weighting parameters were configured within the algorithm’s scoring function to accurately represent the anticipated interaction profile with the target. The weights assigned to each feature were: aromatic ring = 3.0; hydrogen bond donor/acceptor = 1.5; hydrophobic = 3.0; and charge (anion/cation) = 1.0. For the generation of each pharmacophore, a .mol2 file containing molecules aligned according to their respective phytochemical groups was utilized.

2.2. Pharmacophore-Based Virtual Screening

The visualization and description of the pharmacophoric groups in 3D were performed through the ZINCPharmer online platform (http://zincpharmer.csb.pitt.edu/. Accessed on 19 August 2023). The pharmacophore allows for the capturing of the nature and three-dimensional arrangement of the chemical groups that are important for interactions with molecular targets [24,25]. ZINCPharmer also makes it possible to search for similar molecules that are contained in databases through the pharmacophoric model, that is, from the alignment of a set of other molecules, thousands of conformational isomers of compounds are generated in less than 1 min. The results can be viewed instantly and the screened molecules can even be downloaded [26].
In the present research, the value of 1 was used as a limit parameter for “Max Hits per Conf” and “Max Hits per Mol”, RMSD 1.5, and molecular weight less than 400 g/mol−1, when considering the selection of molecules with the highest likelihood of activity at the central level. This screening made it possible to search for molecules with different conformations, non-isomers, from the pharmacophore in the ZINC Purchasable bank.

2.3. Prediction of Pharmacokinetic and Toxicological Properties (ADME/Tox)

The pharmacokinetic properties of the groups of molecules were determined using the QikProp software v. 4.4, which provides data related to ADME (absorption, distribution, metabolism, and excretion) [27]. The parameters evaluated in the program were: qualitative prediction of human oral absorption (HOA); prediction of the percentage of human oral absorption (%HOA); cell permeability in Caco-2 (pCaco-2) and MDCK (pMDCK) cells; penetration of the blood–brain barrier (logBB); prediction of activity in the Central Nervous System (CNSA); and prediction of blocking activity in HERG channels (logHERG).
As for the toxic potential of the molecules studied, the DEREK program (Deductive Estimate of Risk from Existing Knowedge) v. 6.0 was used, which allows for relating the molecular structure with toxicity through a series of information contained in the program’s database [28].

2.4. Molecular Docking Using the GOLD Program

To evaluate the interaction between ligands and the MAO-B protein, molecular docking was employed. This method allows structural recognition as well as prediction of bond thickness, in addition to determining the optimal binding pose of a molecule within the active site [29]. Based on a genetic algorithm (GA), GOLD (Genetic Optimization for Ligand Docking) is widely used to optimize docking times and increase the likelihood of identifying the best ligand fit for a target [30].
The predictive accuracy of the in silico model was assessed through a validation method known as redocking. Since crystallographic structures are typically complexed with other ligands, this validation step establishes whether the program can accurately reproduce the experimental spatial orientation of the ligand within the binding site. This phase allows for the identification of origin coordinates, the root mean square deviation (RMSD) value, and the validation radius. The target was considered validated when the RMSD value was below 2 Å.
The crystallographic structure of MAO-B used in this study (PDB ID: 2BYB) was obtained from the Protein Data Bank (PDB). It is complexed with deprenyl (selegiline) and has a resolution of 2.20 Å.
Following target validation in the PDB, docking of selegiline (reference molecule) and all triaged natural product molecules were performed using GOLD v. 2020.1. This approach aimed to determine the best results regarding intermolecular interactions and target analyses, comparing them to selegiline, a selective and irreversible MAO-B inhibitor.
For redocking and docking studies, the protein structure was prepared by removing water molecules to reduce computational load. In the GOLD program, hydrogen atoms were added to facilitate improved ligand-target docking and interaction analysis.

2.5. Activity Prediction of the Best Results: SEA and PASS

The last stage of the study consisted of predicting whether, after analysis, the best proposals had potential biological activity based on the application of two networked servers. With an average accuracy of 89%, the first refers to the PASS prediction (prediction of activity spectra for substances) which is based on a series of mathematical calculations, with the application of the NMA descriptor (multilevel neighbors of atoms) to predict possible biological effects in the most varied mechanisms of different substances from the chemical structure [31,32]. The second server is the SEA (Similarity ensemble approach) prediction (https://sea.bkslab.org/. Accessed on 15 September 2023), developed by Keiser and collaborators in 2007, and as the name implies it evaluates the ability of a substance to interact with a given target, based on its structure, when compared to others contained in the database [33]. In addition, to better substantiate the results from the different calculations applied and to determine whether other studies are already being carried out to evaluate the activity of interest for this study (MAO-B inhibition), a search was carried out in the Cortellis Drug database Discovery Intelligence (CDDI) (https://www.cortellis.com/drugdiscovery/. Accessed on 3 June 2025).

3. Results and Discussion

3.1. Literature Search: Alkaloids and Flavonoids

In full, eleven alkaloids were described in the three review articles: berberine, coptisine, epiberberine, groenlandicine, isorhynchophylline, jatrorrhizine, leorunine, ligustrazine, L-stefolidine, nicotine, and palmatine (Figure 1). Additionally, eight flavonoids were described in full: apigenin, baicalein, daidzein, genistein, hyperoside, naringin, puerarin, and quercetin (Figure 2). These all have potential antiparkinsonian activity, due to their neuroprotective, antioxidant, and anti-inflammatory characteristics, derived from literature studies, demonstrating potential for experimental application.
Alkaloids are low-molecular-weight compounds containing a nitrogen atom within a heterocyclic ring, classified as primary, secondary, tertiary, or quaternary amines. In plants, they occur as salts, esters, or in association with tannins or sugars, and are generally water-soluble [34,35]. Their structural diversity, wide distribution, and ability to cross the blood–brain barrier make them promising candidates for the treatment of various diseases, particularly CNS disorders, even at low doses [14,36].
Flavonoids are secondary metabolites consisting of a benzopyrone ring with phenolic or polyphenolic groups, abundantly found in fruits, vegetables, cereals, stems, flowers, and seeds [16,17]. Their core structure comprises a diphenylpropane backbone (C15), in which rings A and B are linked via a heterocyclic pyran ring containing oxygen [15,37]. Classification is based on chemical structure, degree of unsaturation, and oxidation state [16,17]. Flavonoids exhibit diverse pharmacological activities—anti-inflammatory, antioxidant, antimicrobial, antitumor, neuroprotective, among others—partly due to their ability to modulate enzymatic activity and interact with CNS receptors. Their antioxidant properties protect cells from ROS-induced oxidative stress, implicated in cardiovascular, oncological, metabolic, and neurodegenerative diseases [16,37].

3.2. Pharmacophoric Model

The molecules were optimized for the pharmacophore formation, and their partial charges were corrected. These procedures are important to avoid possible errors in subsequent predictive calculations. As each molecule atom has a partial charge, whether negative or positive, and an electronic “cloud” is created around it which can influence the distribution of these charges and thus interfere with the results [38].
A pharmacophore is essentially defined as the spatial arrangement of molecular features that enables the identification of regions necessary for a ligand to interact with its target and elicit a biological response, serving as a powerful tool in drug design studies. The concept was first introduced by Erlich in 1909, who described it as “a molecular structure that carries (phoros) the essential characteristics responsible for the biological activity of a drug (pharmacon)” [39]. Nearly a century later, in 1998, the International Union of Pure and Applied Chemistry (IUPAC) expanded the definition, describing a pharmacophore as “a set of steric and electronic features necessary to ensure optimal supramolecular interactions with a specific target structure to trigger (or block) its biological response” [39,40].
Given the diversity and complexity of molecular structures, various programs and algorithms have been developed to rapidly elucidate pharmacophoric models [40]. One such tool is PharmaGist, a free online server that generates 3D pharmacophores by aligning multiple molecules and provides scoring for up to 32 molecules within minutes [24]. The formation of a pharmacophore involves identifying the set of functionally important groups responsible for intermolecular interactions, thereby defining the chemical features required for a ligand to be active, even if in a reduced but representative number, enabling the identification of regions with key characteristics. These features typically include aromatic groups, hydrogen-bond donors, hydrogen-bond acceptors, and positively or negatively charged ions—regions that are crucial for activity [40,41].
Figure 3 shows the pharmacophoric model generated by the server in a 3D model of the groups of alkaloids (A) and flavonoids (B). The regions marked in purple represent the aromatic regions, the one in blue represents the presence of a positive ion, and the orange represents the hydrogen acceptors, confirming the basic structures of alkaloids and flavonoids.

3.3. Virtual Screening Results

As there are many molecules available in the most varied databases, virtual screening is an important step for the study and development of drugs as it allows us to refine the search more quickly [42]. The ZINCPharmer server allows the search of more than 176 million conformations in a few minutes due to being an easy-to-use and fast search tool, and through the application of filters it allows us to refine and reduce even more the number of molecules generated [26]. In the alkaloids group, after applying the filters, a total of eight molecules were obtained, while in the flavonoids group, a total of sixteen molecules were obtained. Among these, the ZINC00597214 and ZINC72342127 molecules stand out, screened from alkaloids and flavonoids, respectively, which will be discussed further in the following sections (Figure 4).
This stage of the study involved the application of a screening process aimed at identifying new molecular candidates that could serve as a basis for further investigations. Specific filters were applied with the objective of maximizing the likelihood of permeation through the blood–brain barrier, taking into account the parameters and influences of physicochemical properties, in order to select compounds with greater potential for biological activity.

3.4. Pharmacokinetic Predictions

In order to compare the results obtained in QikProp v. 4.4 with the study groups, the pharmacokinetic prediction of selegiline was performed as a reference molecule. From this session onwards, the results of the molecules palmatine, genistein, ZINC00597214, and ZINC72342127 will be presented and further explored since they show more favorable results when compared to the others (Table 1). The #stars parameter indicates the number of descriptors computed for a molecule in QikProp that are outside the range of 95% values [43,44]. This implies that the farther from 0, the less reliable the result, indicating that when comparing with other molecules available in the collection the program could not measure the parameters with such accuracy as there is little similarity. Therefore, the program establishes a confidence interval of 95%. If it exceeds this then it must be understood that the calculation was not accurately validated [44].
The parameters of HOA and %HOA are important for defining the behavior of the drug by the organism and how it manages to reach the bloodstream to be distributed in ideal concentrations, which influences, therefore, its bioavailability. Popularized from the studies developed by Hidalgo and collaborators in 1989, Caco-2 cells are derived from the human colon adenocarcinoma lineage, in which they spontaneously differentiate into enterocytes under normal conditions and form layers of columnar epithelial cells, tight intercellular junctions, and apical microvilli, that is, they are structurally very similar to the human epithelium and are able to be used as a model [45,46]. QikProp allows for predicting the ability of blood–gut penetration, considering non-active transport through these cells [44].
Another widely used model is that of MDCK cells, which are isolated from canine distal renal tissue. Like Caco-2 cells, they differentiate into columnar epithelium with brush borders with the formation of tight junctions when cultured in vitro. However, the cultivation and growth time is faster, in addition to having lower TEER values (an indicator of the integrity of cell barriers) [46,47]. Not only that, but these cells are also used to understand distal tubular physiology and serve as a good mimic of the blood–brain barrier [44,46,48].
For planning a drug that acts on the central nervous system, it is essential to evaluate two parameters: the ability to penetrate the BBB through the logBB value, and whether it manages to activate or block some response in the central nervous system (CNS). The cerebral capillary endothelium is protected by tightly bound cells through the interaction of several transmembrane proteins, which allows for the blocking of access of substances to the cerebral fluid. In this sense, the BBB is a protective layer that prevents more polar and high-molecular-weight molecules from penetrating [49]. According to the software, values between −3 and 1.2 indicate good permeability through the barrier [44].
Finally, the last parameter analyzed was the inhibitory potential under the HERG/K+ channel. This criterion is important to assess whether a given substance can change or cause disturbances in the heart’s normal rhythm, which can cause heart failure and even death. This is due, in turn, to the fact that the blockade in the channel causes a disturbance in cardiac repolarization (increased QT interval) [50,51,52].

3.5. Toxicology Prediction

The DEREK program (Deductive Estimation of Risk from Existing Knowledge) v. 6.0, developed by Schering Agrochemitures and marketed by Lhasa Limited, predicts the toxicity of different molecules through structural correlation with other compounds already tested experimentally [53]. The program applies structure–activity relationships (SAR) and provides qualitative information on the toxic action of different compounds [54,55] through alert signaling, which indicates which group of the molecule is responsible for providing such signaling and identifies the toxic effect that can be caused in the body. To compare with the groups of interest, the prediction of selegiline, which is the reference molecule, was performed. The molecule presented an alert for the condition of renal disorder in mammals due to the presence of the benzphetamine structure, this alert being of the equivocal type. Although the program only signaled a warning, even more so of the equivocal type, it is known that selegiline can cause several adverse effects, such as a hypertensive crisis which is associated with the toxic levels of the drug’s adrenergic metabolites and can also cause an increase in high blood pressure if the individual consumes it with food, supplements, and drinks rich in tyramine. Sudden sleep episodes, orthostatic hypotension, arrhythmias, mental status changes, and hallucinations may occur in individuals using the drug [56].
As shown Table 2, the flavonoid genistein presented a greater number of alerts when compared to the other structures. However, this was a characteristic presented by the majority of flavonoids and so probably indicates that they are more toxic in terms of skin sensitization, as well as presenting potential modulation of estrogen receptors and potential teratogenicity. It is important to emphasize that this parameter was taken into consideration when considering the synthesis process of these substances and the risk associated with it.

3.6. Molecular Docking

In the method validation or redocking step (Figure 5), the RMSD value obtained was 0.75 Å. This parameter evaluates the quality of interaction between the ligand and the protein, in which values below 2 Å are the most accepted since they indicate high accuracy in identifying the ligand at the site, with a correct fit occurring in a favorable spatial orientation, at least in the program [57,58]. The coordinates of origin in the identification of the site were x = 52.40, y = 156.72, and z = 25.78, and the validation radius obtained was 9.200 Å.
For molecular docking, water and ligands were removed from the extracted PDB molecule in the DSV, keeping only the A chain to be run in the GOLD program. Furthermore, as MAO-B is a FAD-dependent enzyme, for the calculation the cofactor remained in the protein [59].
In this study, a docking simulation was performed with selegiline as a ligand to compare with the other molecules of interest for this study regarding the types of interactions. The gold score presented was in the amount of 64.07. This value is important to define the degree of affinity with the target, and it consequently influences the selectivity of the molecule. In this sense, the higher the value, the greater the selectivity indicator. Selegiline presented fourteen interactions, of which two were hydrogen bonds involving the amino acids Gln206 and Tyr435 of the carbon type with the atoms H29 and H30. These interactions require a closer approximation of the ligand to occur, mainly due to the size of the hydrogen atom. According to IUPAC, hydrogen bonding is an attractive interaction between a hydrogen atom of a molecule or residue with another atom that is more electronegative than hydrogen [60]. With the amino acid Cys172, due to the presence of the sulfur atom it interacted with the aromatic group of selegiline (π-Sulfur), with a distance of 4.95 Å.
The rest of the interactions were all hydrophobic, including one π–π T-shaped, one alkyl, and nine of the π-alkyl type. T-shaped π–π bonds (Tyr326) are interactions that occur in cyclic systems involving double bonds (sp2 order) in which specific angles of each aromatic ring are formed which look like the letter T, hence the name [61]. Regarding the alkyl interaction, this occurred between C1 and Leu171 of MAO-B. Finally, the rest of the interactions with the amino acids Tyr326, Phe343, Tyr398, Tyr435, Ile199, and FAD were of the π–alkyl type, in which the alkyl portion of one molecule interacts with the aromatic ring of the other.
According to the study carried out by Colibus et al. (2005) [62], the amino acids present in the active site of MAO-B are Tyr60, Gln65, Val82, Glu84, Leu88, Leu171, Cys172, Ile198, Ile199, Ser200, Thr201, Glu207 Thr-314, Ile316, Tyr326, Leu328, Met341, Phe343, Tyr398, and Tyr435.
Viña and collaborators (2012) [63] noticed when performing molecular docking of coumarin derivatives, to assess potential inhibitory activity on the enzyme, the participation of the residue Cys172, which interacted through hydrogen bonding with the carbonyl of coumarin. Tyr326, on the other hand, interacted through a hydrophobic bond of the π–π type with the 3-aryl coumarin rings. Van der Waals and electrostatic interactions were also observed with the following residues: Phe168, Leu171, Ile198, Ile199, Ile316, Phe343, Tyr398, and Thy435.
In the publication of Taylor et al. (2013) [64], when they analyzed the interactions between different ligands (rasagiline, proposal 1, 2, and 4) they detected and compared which residues appeared to establish the more important ones. According to the authors, the amino acids Tyr326, Tyr398, Tyr435, Phe168, and Trp119 are present at the site. With rasagiline, there was interaction between Tyr326, Tyr398, Tyr435, and Phe168. With the proposals, the interactions occurred via Tyr326, Tyr435, and Tyr398. And two of the proposals performed hydrophobic bonds, of the π-stacking type, while the rest interacted through hydrophobic and hydrophilic bonds.
In the work performed by Mellado et al. (2019) [65], molecular docking between prenylated chalcones and MAO-B was performed. Through this study, one of the most active compounds when tested in vitro showed hydrogen interactions with Cys172 and Tyr435, and hydrophobic interactions with residues Tyr60, Trp119, Leu164, Leu167, Phe168, Leu171, Cys172, Ile199, Gln206, Ile316, Tyr326, Met341, Phe343, Tyr398, and FAD600.
With this, it is possible to infer that the docking was validated and allowed for the identification of the active site of the MAO-B enzyme with essential amino acids according to findings in the literature.

3.7. Molecular Docking of the Alkaloid Group: Natural Products and Screened Molecules

The palmatine molecule showed a total of 21 interactions, with a degree of affinity from the score of 84.76, with hydrogen interactions involving the residues Cys172, Ile199, Tyr435, as well as the FAD cofactor. There was one π–sulfur interaction with Cys172, and the other bonds were all hydrophobic, one T-shaped π–π, six of the alkyl type, and seven of the π-alkyl type. In the docking analysis of the group of molecules selected, it was observed that there was interaction with important amino acids and many of them also showed behavior similar to or superior to that of selegiline for this parameter. About the screened molecule, the number of interactions and potential affinity with the target was slightly lower when compared to palmatine (19 interactions/score: 78.62), but it still showed interactions with important amino acids, as shown in Figure 6 and Table 3.
In the work carried out by Al-Baghadi et al. (2012) [66], when analyzing the potential of the alkaloid piperine and its derivatives, extracted from the plant species Piper ningrum, to inhibit the MAO-B enzyme they found through docking study that the methylenedioxyphenyl ring of piperine formed an aqueous bridge with the residues Tyr188 and Cys172, considering an aqueous environment, and interacted through hydrogen bonding between the aromatic ring and the hydroxyl hydrogen of Tyr398, which was highlighted by the author as being a commonly found interaction. In addition, interaction was observed with the residue Ile199. This is usually found in docking studies with MAO-B inhibitors since it is considered an essential amino acid, and it is considered a “gatekeeper” of the target.
Zhi et al. (2014) [67] evaluated the potential of alkaloids extracted from Desmodium elegans leaves to inhibit both MAO isoforms, both MAO-A and MAO-B. The results presented to the last-mentioned isoform demonstrated that the ligands interacted with residues Phe168, Cys172, Ile198, Ile199, Gln206, and Tyr326. The author pointed out that due to the presence of the 3-hydroxy-β-ionone group of the new alkaloid desmodeleganine and the fact that it has a larger molecular volume due to the group it allowed an increase in the surface area and hydrophobicity, which allowed extra interaction in the hydrophobic cavity of MAO-B, thus contributing to greater strength of interaction. Even in the enzymatic assay, this substance showed the highest inhibitory potential when compared to the other structures tested.
In another study, focusing on another alkaloid, crinamine, Naidoo et al. (2020) [68] performed docking with the aforementioned ligand and compared it to other alkaloids, epibuphanisine, hemantamine, haemanthidine, and also with selegiline as a standard molecule. It was observed through the study that crinamine presented, in all, six hydrophobic interactions involving residues Leu171, Ile19, Tyr326, Tyr398, and Tyr435. The epibuphanisine molecule interacted with five residues (Phe168, Leu171, Ile199, Ile316, and Tyr326) through hydrophobic interactions. Still for this type of interaction, hemanthamine and haemanthiding showed four interactions, with the amino acids Tyr60, Gln206, Tyr326, and Tyr435, in addition to two hydrogen interactions with Tyr398 and Tyr435. With that, when comparing with other studies the author reinforced that MAO-B is formed by a small entrance site and that it expands to a larger cavity, where the FAD cofactor is connected. In addition to Ile199, Tyr326 is also considered a “gatekeeper” as it is important to maintaining the conformation of the active site of the protein in addition to interacting with different ligands, including those with inhibitory potential on MAO-B when tested in vitro.
Othman and collaborators (2022) [69], when investigating the neuroprotective effect of seven amide alkaloids obtained from Bassia indica and Agathophora alopecuroids, found in a docking study with MAO-B as a therapeutic target that each structure performed hydrogen bonds with Ile198 and Tyr432, in addition to bonds hydrophobic with Leu171, Ile198, Ile199, Ile316, and Tyr398. With that, they established that these structures have potential activity since they interacted with amino acids considered essential, which are present at the site, through stable bonds.
In this sense, it can be observed that the results obtained in the present study demonstrated that the analyzed structures present potential activity with action on the MAO-B enzyme, since the great majority presented key interactions with important residues, as described in the literature.

3.8. Molecular Docking of the Flavonoid Group: Natural Products and Screened Molecules

Of the flavonoids, genistein had a total of 15 interactions with important amino acids with a score of 76.56, involving the residues Tyr326, Ile199, Phe168, Leu171, Cys172, Ile198, Ile199, Ile316, with a total of five hydrogen interactions and the rest all being hydrophobic. The ZINC723127 molecule also showed interactions with important amino acids, with the highest score and the greatest number of interactions (19 interactions and score: 85.35), all of which were hydrophobic since interactions only occurred in the aromatic regions, as illustrated in Figure 7 and Table 4.
Turkmenoglu et al. (2015) [70], when analyzing flavonoids extracted from species of the genus Sideristis, intended to establish the potential inhibitory activity on the two isoforms of MAO, both MAO-A and MAO-B. In the docking study with the flavonoid xanthomicrol it was observed that two carbonyl and two hydroxyl groups interacted with Cys172 and Tyr435 residues. Furthermore, this flavonoid also interacted with residues Ile198, Ile199, Cys172, Leu171, Tyr435, Tyr398, Tyr326, and Phe343. Another substance analyzed in the study was salvigenin, in which an interaction between the carbonyl group of the coumarin ring and Cys172 was visualized. In the hydrophobic pocket, it interacted with residues Phe168, Tyr60, Ie199, Tyr326, Cys172, Ile198, Phe343, Gln206, and Tyr398.
In another study, carried out by Monteiro et al. [71], the potential activity of different flavonoids as possible therapeutic agents for Alzheimer’s and Parkinson’s disease with action on different receptors was analyzed. About the inhibitory action on MAO-B, the authors established that all the substances analyzed (3-O-methylquercetin, 8-prenylnaringenin, aspalathin, capensinidine, europidine, epicatechin gallate, hisperidine, homoeroidctyl, rosinidine, and sterubin) interacted with residues Cys172, Tyr435, Leu171, Tyr326, Tyr60, and Gln206.
Chaurasiya et al. (2020) [72] evaluated the ability of O-methylated flavonoids derived from natural products to interact with the two isoforms of MAO. These flavonoids came from five plant species (Senecio roseiflorus, Polygonum senegalense, Bhaphia macrocaliyx, Gardenia ternifolia, and Psiadia punculata). Among all the compounds analyzed, two showed the greatest potential to selectively inhibit the MAO-B isoform in the experimental assay, based on the Ki determination (inhibition constant), which were the substances named 8-demethylsideroxylin and 5,7-diydroxy-2,3,4,5-tetramethoxyflavone. For both molecules, in the docking study a hydrogen interaction was observed between the C-4 carbonyl and Cys172, while the C-5 hydroxyl showed interaction, mediated by water, with Tyr188 and Gln206. Ring A was positioned in the hydrophobic pocket of the protein, surrounded by several residues, such as Tyr60, Phe343, Tyr398, and Tyr435, and ring B was surrounded by residues Leu164, Leu167, Phe168, Ile199, Ile316, and Tyr326.
Corroborating the results obtained in this research with data from the literature, it was possible to establish that most molecules interacted satisfactorily with the target, deferring potential activity with action on the MAO-B enzyme.

3.9. Activity Prediction of the Best Results: SEA and PASS

Therefore, after defining the best results, the prediction of biological activities was carried out through online servers for PASS prediction and SEA prediction. The PASS prediction web server, through a series of mathematical and structural data, assigns various activities to different compounds and quantifies them in probable values, which are defined by Pa (probability of activity) and Pi (probability of inactivity). If the value of Pa is >0.7, the probability that the substance exhibits experimental activity is high, with a high chance that the substance is an analog of an already known pharmaceutical agent. Pa values between 0.5 and 0.7 indicate a lower probability, though there remains the possibility that the substance is different from known structures. In situations where the Pa value is <0.5, it is less likely to exhibit experimental activity [30].
The server also provides a list of activities that can be predicted and informs one about the number of active compounds detected, in addition to providing the prediction of the invariant accuracy of the calculation. For the activity “MAO-B inhibitor”, the server contains a total of 672 compounds, with a precision forecast of 0.98. As for the “antiparkinsonian” activity, there are a total of 2768 compounds, with a precision of 0.89.
In this study, only one of the molecules studied showed a result for biological activity, which was ZINC72342127 screened from the group of flavonoids. It is important to emphasize that this one presented a Pa value higher than selegiline, indicating that it has a greater probability of presenting inhibitory activity on MAO-B than the reference molecule. It is noteworthy that even if the other molecules did not show any activity of interest they should be discarded for future studies, since the non-appearance of results by the server may be due to the lack of data on these structures in its database against the desired activity, which is the inhibition of MAO-B, as show in Table 5.
As for the SEA prediction server, this is based on the idea that compounds with similar chemical structures will have similar behavior and properties, including interacting with certain receptors or groups of proteins [33]. The program employs the maximum Tanimoto coefficient, where values range from 0 to 1 and in which the closer to or equal to 1 a value is, the greater the probability of a molecule binding to the target. A comparison is then made between different molecules available in the database of the server itself, and when a structure of interest is inserted the calculation is applied to evaluate the potential for interaction through chemical and structural similarity, functioning as a molecular fingerprint [33,73].
It is important to emphasize that the absence of activity predictions for some molecules in the PASS and SEA servers does not necessarily imply that these compounds are inactive against the target of interest, namely the MAO-B enzyme. This limitation primarily arises from the lack of structurally similar data in the reference databases used by the models, which diminishes the algorithms capacity to associate such molecules with known biological activities. Additionally, low or absent probability of activity (Pa) values reflect low chemical similarity to previously documented active compounds, thereby hindering reliable prediction. Even if certain structures did not present any results for this parameter, this does not exclude their potential to interact with MAO-B experimentally as the servers may fail to provide results due to insufficient data on molecules similar to those tested for the specific target. Therefore, it should be highlighted that these results do not preclude the experimental potential of these molecules, especially for novel or poorly studied structures. Complementary experimental investigations are thus essential to accurately assess the true inhibitory effects of these molecules on MAO-B.
Given this, therefore, to verify whether other studies are being developed to analyze these molecules with the purpose of inhibitory action on MAO-B for the treatment of PD, a search was carried out in the CDDI database, which is a platform that integrates several areas of knowledge regarding different molecules, including those with similar or analogous structures. According to the aforementioned bank, palmatine is being studied as a potential acetylcholinesterase inhibitory agent, aiming to treat Alzheimer’s disease. In relation to the flavonoid genistein, several research groups are investigating this substance for various purposes, such as an anti-HIV agent, its use for the treatment of bladder and prostate cancer, bone diseases, endometriosis, erectile dysfunction, cystic fibrosis, endometriosis, gynecological disorders, as an immunomodulatory agent, used for melanoma therapy, as an ophthalmic drug, in cases of poisoning, radioprotective and/or radiomitigating, and finally, for cases of renal failure.
From the group of screened molecules, the one called ZINC00597214 is being studied as a candidate drug to be used in cases of erectile dysfunction, with an action similar to Viagra®. Regarding ZINC72342127, according to the CDDI no studies regarding the molecule and analogs are currently under development.

4. Conclusions

Due to the high prevalence of Parkinson’s disease (PD) among neurodegenerative disorders and the absence of a cure, the development of effective treatments is of utmost importance to improve patients’ quality of life. The application of in silico studies has significantly contributed to the discovery and design of new drugs by enabling the analysis of a large number of compounds within a short timeframe.
Following this approach, analyses of the molecules revealed that many exhibited results surpassing those obtained with selegiline. This indicates that the natural products studied, particularly palmatine and genistein, are promising computational candidates that warrant experimental validation due to their predicted inhibitory potential against the MAO-B enzyme, primarily through interactions with key amino acid residues. Moreover, virtual screening facilitated the identification of molecules with properties comparable or even superior to those of known metabolites, notably ZINC00597214 and ZINC72342127.
Consequently, this study broadens the pool of promising candidates for the pharmacological development of antiparkinsonian therapies. Further investigations aim to deepen the understanding of these molecules through in silico simulations, such as molecular dynamics studies, alongside in vitro enzymatic assays and in vivo tests using the zebrafish model for behavioral and toxicity evaluations with the goal of more precisely characterizing their properties.

Author Contributions

Study concept or Design, A.C.d.J.S.; Writing—Reviewing and Editing, A.B.B.d.S.; Data Analysis or Interpretation, M.P.B.; Visualization, C.H.T.d.P.d.S.; Data Curation, L.I.d.S.H.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), through PDPG Call No. 18/2020, by Universidade Federal do Amapá (UNIFAP) and Pró-Reitoria de Pesquisa e Pós-Graduação (PROPESPG), through Call No. 01/2024.

Informed Consent Statement

Not applicable.

Data Availability Statement

The authors confirm that the data supporting the findings of this research are available within the article.

Acknowledgments

To the Laboratory of Pharmaceutical and Medicinal Chemistry (PharMedChem) at UNIFAP. To the Computational Laboratory of Pharmaceutical Chemistry, of the School of Pharmaceutical Sciences of Ribeirão Preto (USP). To the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Fundação de Amparo à Pesquisa do Amapá (FAPEAP).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

%HOAHuman Oral Absorption Percentage
HOAHuman Oral Absorption
MAO-BMonoamine Oxidase B
MPP+1-methyl-4-phenylpyridine
MPTP1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine
PDParkinson Disease
RMSDRoot Mean Square Deviation
SARStructure–activity relationship

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Figure 1. Molecular structure of alkaloids in their original structures.
Figure 1. Molecular structure of alkaloids in their original structures.
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Figure 2. Molecular structure of flavonoids in their original structures.
Figure 2. Molecular structure of flavonoids in their original structures.
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Figure 3. Pharmacophoric groups of alkaloids (A) and flavonoids (B).
Figure 3. Pharmacophoric groups of alkaloids (A) and flavonoids (B).
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Figure 4. Screened molecules, from alkaloids and flavonoids, respectively.
Figure 4. Screened molecules, from alkaloids and flavonoids, respectively.
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Figure 5. Representation of the best ligand pose (orange) compared to the structure obtained in the PDB (2BYB), in the validation step.
Figure 5. Representation of the best ligand pose (orange) compared to the structure obtained in the PDB (2BYB), in the validation step.
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Figure 6. Two-dimensional representation of palmatine and ZINC00597214 of the simulation of docking with MAO-B enzyme. Interactions are represented by colors: carbon–hydrogen bonds (blue), π–sigma (purple), π–sulfur (yellow), π–π stacked (gray), π–π T-shaped (dark pink), alkyl (orange), and π–alkyl (light pink).
Figure 6. Two-dimensional representation of palmatine and ZINC00597214 of the simulation of docking with MAO-B enzyme. Interactions are represented by colors: carbon–hydrogen bonds (blue), π–sigma (purple), π–sulfur (yellow), π–π stacked (gray), π–π T-shaped (dark pink), alkyl (orange), and π–alkyl (light pink).
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Figure 7. Two-dimensional representation of genistein and ZINC72342127 from simulation of docking with MAO-B protein. Interactions are represented by colors: conventional hydrogen bonds (green), carbon–hydrogen bonds (blue); π–sulfur (yellow), π–sigma (purple), π–π stacked, π–π T-shaped (dark pink), alkyl (orange), and π–alkyl (light pink).
Figure 7. Two-dimensional representation of genistein and ZINC72342127 from simulation of docking with MAO-B protein. Interactions are represented by colors: conventional hydrogen bonds (green), carbon–hydrogen bonds (blue); π–sulfur (yellow), π–sigma (purple), π–π stacked, π–π T-shaped (dark pink), alkyl (orange), and π–alkyl (light pink).
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Table 1. Pharmacokinetic properties, calculated with the QikProp software.
Table 1. Pharmacokinetic properties, calculated with the QikProp software.
MoleculesSimilarityAbsorptionDistribution
StarsHOA%HOApCaco-2pMDCKCNSAlogBBlogHERG
Selegiline231002.234331.3050220.621−5.35
Palmatine131006602.973805.3210.134−5.467
Genistein0376.738187.31780.927−2−4.393−6.74
ZINC00597214231009831.485851.3110.319−4.799
ZINC72342127031003428.011873.560−0.095−5.803
Table 2. Prediction of toxicological properties of molecules by the DEREK program.
Table 2. Prediction of toxicological properties of molecules by the DEREK program.
CompoundTotal of AlertsConditionAlert
Selegiline1Renal disorders in mammals (equivocal)Benzphetamine-like
Palmatine1Skin sensitization (plausible)Vinylic or allylic anisole
Genistein5Skin sensitization (plausible)
Estrogen receptor modulation (plausible)
Teratogenicity (equivocal)		Enol ether
Substituted phenol
Resorcinol or precursor
Hydroxynaphthalene or derivative
4′,7-Dihydroxyflavone or derivative
ZINC005972141Skin sensitization (plausible)Vinylic or allylic anisole
ZINC723421271Skin sensitization (equivocal)Hydrazine or precursor
Table 3. Molecular docking simulation analysis of the interactions of the alkaloid palmatine and the screened compound ZINC00597214 with the MAO-B enzyme.
Table 3. Molecular docking simulation analysis of the interactions of the alkaloid palmatine and the screened compound ZINC00597214 with the MAO-B enzyme.
CompoundAAAtomInteractionTypeDistanceScore
PalmatineCys172O25Hydrogen BondCarbon–Hydrogen Bond2.7084.76
Ile199H44Hydrogen BondCarbon–Hydrogen Bond2.37
Tyr435H47Hydrogen BondCarbon–Hydrogen Bond2.59
Cys172H48Hydrogen BondCarbon–Hydrogen Bond2.81
Cys172LigandOtherπ–Sulfur5.06
Tyr326LigandHydrophobicπ–π T-shaped4.91
Leu171LigandHydrophobicAlkyl4.47
Cys172LigandHydrophobicAlkyl5.13
Ile199LigandHydrophobicAlkyl4.55
Leu164C22HydrophobicAlkyl4.54
Ile199C22HydrophobicAlkyl4.58
Ile316C24HydrophobicAlkyl5.17
Trp119C22Hydrophobicπ–Alkyl5.43
Trp119C22Hydrophobicπ–Alkyl4.37
Phe168LigandHydrophobicπ–Alkyl5.22
Tyr326C24Hydrophobicπ–Alkyl4.83
Leu171LigandHydrophobicπ–Alkyl4.66
Leu171LigandHydrophobicπ–Alkyl3.88
Ile199LigandHydrophobicπ–Alkyl3.86
FAD600H27Hydrogen BondCarbon–Hydrogen Bond2.66
FAD600H29Hydrogen BondCarbon–Hydrogen Bond3.00
ZINC00597214Ile199H51Hydrogen BondCarbon–Hydrogen Bond2.9278.62
Ile199LigandHydrophobicπ–Sigma3.65
Tyr398LigandHydrophobicπ–π Stacked5.64
Tyr326LigandHydrophobicπ–π T-shaped4.85
Leu171LigandHydrophobicAlkyl4.28
Ile198C21HydrophobicAlkyl3.73
Leu171C23HydrophobicAlkyl4.93
Ile198C23HydrophobicAlkyl5.16
Phe168C23Hydrophobicπ–Alkyl4.17
Tyr188C21Hydrophobicπ–Alkyl4.88
Tyr326LigandHydrophobicπ–Alkyl4.87
Tyr398C22Hydrophobicπ–Alkyl4.54
Tyr435C21Hydrophobicπ–Alkyl5.26
Tyr435C22Hydrophobicπ–Alkyl4.94
Leu171LigandHydrophobicπ–Alkyl4.34
Ile199LigandHydrophobicπ–Alkyl4.73
Leu171LigandHydrophobicπ–Alkyl5.29
FAD600C22Hydrophobicπ–Alkyl4.11
FAD600C22Hydrophobicπ–Alkyl4.31
Table 4. Molecular docking simulation analysis of the interactions of the flavonoid genistein and the screened compound ZINC72342127 with the MAO-B enzyme.
Table 4. Molecular docking simulation analysis of the interactions of the flavonoid genistein and the screened compound ZINC72342127 with the MAO-B enzyme.
CompoundAAAtomInteractionTypeDistanceScore
GenisteinTyr326O17Hydrogen BondConventional Hydrogen Bond3.0576.56
Tyr326O18Hydrogen BondConventional Hydrogen Bond2.05
Ile199H28Hydrogen BondConventional Hydrogen Bond1.75
Phe168O9Hydrogen BondCarbon–Hydrogen Bond2.23
Phe168H25Hydrogen BondCarbon–Hydrogen Bond2.59
Leu171LigandHydrophobicπ–Alkyl4.99
Cys172LigandHydrophobicπ–Alkyl4.30
Ile198LigandHydrophobicπ–Alkyl5.40
Leu171LigandHydrophobicπ–Alkyl4.03
Cys172LigandHydrophobicπ–Alkyl5.23
Ile199LigandHydrophobicπ–Alkyl4.94
Leu171LigandHydrophobicπ–Alkyl5.29
Ile199LigandHydrophobicπ–Alkyl4.37
Ile316LigandHydrophobicπ–Alkyl5.21
ZINC72342127Leu171LigandHydrophobicπ–Sigma2.5185.35
Cys172LigandOtherπ–Sulfur5.33
Cys172LigandOtherπ–Sulfur4.61
Tyr398LigandHydrophobicπ–π Stacked5.15
Tyr398LigandHydrophobicπ–π Stacked3.90
Tyr435LigandHydrophobicπ–π Stacked4.51
Tyr326LigandHydrophobicπ–π T-shaped5.32
Leu164C1HydrophobicAlkyl5.09
Leu167C1HydrophobicAlkyl4.49
Ile316C1HydrophobicAlkyl3.95
Leu328LigandHydrophobicAlkyl5.48
Tyr60LigandHydrophobicπ–Alkyl5.01
Tyr326LigandHydrophobicπ–Alkyl4.93
Phe343LigandHydrophobicπ–Alkyl4.73
Ile199LigandHydrophobicπ–Alkyl4.51
Leu171LigandHydrophobicπ–Alkyl5.11
Ile198LigandHydrophobicπ–Alkyl5.43
FAD600LigandHydrophobicπ–π T-shaped4.94
FAD600LigandHydrophobicπ–π T-shaped4.91
Table 5. Active and inactive probability of the best results by the PASS prediction program.
Table 5. Active and inactive probability of the best results by the PASS prediction program.
CompoundPa aPi bActivity
Selegiline 0.366 0.004 MAO-B inhibitor
0.580 0.008Antiparkinsonian
Genistein 0.615 0.03 MAO-B inhibitor
0.188 0.180Antiparkinsonian
Palmatine---
ZINC00597214---
ZINC723421270.6510.005MAO-B inhibitor
Note: a Pa = potential activity; b Pi = potential inhibition.
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MDPI and ACS Style

de Jesus Silva, A.C.; dos Santos, A.B.B.; Barcelos, M.P.; de Paula da Silva, C.H.T.; da Silva Hage-Melim, L.I. Pharmacophore-Based Virtual Screening of Alkaloids and Flavonoids for Designing Drugs with Inhibitory Activity on the Enzyme Monoamine Oxidase B. Appl. Sci. 2025, 15, 10162. https://doi.org/10.3390/app151810162

AMA Style

de Jesus Silva AC, dos Santos ABB, Barcelos MP, de Paula da Silva CHT, da Silva Hage-Melim LI. Pharmacophore-Based Virtual Screening of Alkaloids and Flavonoids for Designing Drugs with Inhibitory Activity on the Enzyme Monoamine Oxidase B. Applied Sciences. 2025; 15(18):10162. https://doi.org/10.3390/app151810162

Chicago/Turabian Style

de Jesus Silva, Ana Carolina, Ana Beatriz Bezerra dos Santos, Mariana Pegrucci Barcelos, Carlos Henrique Tomich de Paula da Silva, and Lorane Izabel da Silva Hage-Melim. 2025. "Pharmacophore-Based Virtual Screening of Alkaloids and Flavonoids for Designing Drugs with Inhibitory Activity on the Enzyme Monoamine Oxidase B" Applied Sciences 15, no. 18: 10162. https://doi.org/10.3390/app151810162

APA Style

de Jesus Silva, A. C., dos Santos, A. B. B., Barcelos, M. P., de Paula da Silva, C. H. T., & da Silva Hage-Melim, L. I. (2025). Pharmacophore-Based Virtual Screening of Alkaloids and Flavonoids for Designing Drugs with Inhibitory Activity on the Enzyme Monoamine Oxidase B. Applied Sciences, 15(18), 10162. https://doi.org/10.3390/app151810162

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