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Design of new reversible and selective inhibitors of monoamine oxidase A and a comparison with drugs already approved



Monoamine oxidase (MAO) is an enzyme that has been targeted pharmacologically for the treatment of depression and neurodegenerative diseases such as Parkinson's disease. To avoid side effects, drugs currently in use must selectively target either of the enzyme's two isoforms, A or B. In this study, we designed molecules derived from chalcone as potential reversible and selective inhibitors of isoform A of the MAO enzyme.


Ten thousand one hundred compounds were designed and screened using molecular docking, considering the pharmacokinetic processes of chemical absorption, distribution, metabolism, and excretion. Density functional theory calculations were performed for the main ligands to evaluate their reactivity. Six drugs qualified as reversible and irreversible inhibitors of both isoform A and isoform B. Among these, molecule 356 was found to be a reversible inhibitor with the best performance in selectively targeting isoform A of the MAO enzyme. The interaction stability of ligand 356 in the isoform A binding site was confirmed by molecular dynamics. One hydrogen bond was found between the ligand and the cofactor, and up to six hydrogen bonds were formed between the ligand and the protein.


We selected a drug model (molecule 356) for its high affinity to isoform A over isoform B of the MAO enzyme. This proposal should decrease experimental costs in drug testing for neurodegenerative diseases. Therefore, our silico design of a reversible inhibitor of isoform A of enzyme monoamine oxidase can be used in further experimental designs of novel drugs with minimal side effects.

Graphical Abstract


Monoamine oxidase (MAO) has been a pharmacological target for treating depression and neurodegenerative diseases due to its role in regulating the levels of neurotransmitters in the brain. This enzyme (EC is a flavoenzyme that metabolizes a wide range of primary, secondary, and tertiary monoamines, but has a lower affinity for diamines. MAOis expressed in most body tissues, with the highest levels in the liver (Mardani Moghanaki et al. 2022; Mondovì and Finazzi Agrò 1982; Shih and Lan 1990). It is located on the outer membrane of the mitochondria inside the cell and forms dimers (Denney and Denney 1985; Edmondson et al. 2009; Yelekçi and Erdem 2023). There are two MAO isoforms, MAO-A and MAO-B, encoded by different genes but with 70% coincidence in their amino acid sequence (Finberg 2014; Khan et al. 2022). MAO-A and MAO-B are integral mitochondrial outer membrane proteins and, as isoenzymes, are distinguished from each other by differences in substrates and inhibitor specificities (Finberg 2014). MAO-A is inactivated by the irreversible inhibitor clorgyline, and it oxidizes serotonin, norepinephrine, and epinephrine (Aboutabl et al. 2021), whereas MAO-B is inactivated by the irreversible inhibitors pargyline and selegiline and it oxidizes phenylethylamine and benzylamine. Tyramine and tryptamine are oxidized by both MAO-A and MAO-B. They also have differences in their tissue distribution, e.g., placental tissue contains predominantly MAO-A, while platelets and lymphocytes express only MAO-B (Chen and Shih 1997; Edmondson et al. 2009; Hitge et al. 2022).

Both MAO-A and MAO-B play a critical role in the elimination of neurotransmitters such as norepinephrine, serotonin, and dopamine from the brain. Changes in the activity of these enzymes are linked to various psychiatric pathologies. The study of drugs successfully used for treating severe depression has led to the development of two theoretical models to explain their mechanisms of action, one of which is known as “cerebral monoamine deficiency”.

The “cerebral monoamine deficiency” model is shown in Fig. 1. The noradrenergic, serotonergic, and dopaminergic neuronal pathways start in the cerebral cortex expanding toward lower brain structures and modulating mental processes such as thoughts, feelings, and other cognitive activities. Certain antidepressant drugs are known to block the reuptake of monoamines (norepinephrine, serotonin, or dopamine) by presynaptic neurons. This is achieved by inhibiting MAO-A or MAO-B, which are responsible for the degradation of monoamines.

Fig. 1
figure 1

Simplified schematization of the mechanism of action of monoamine oxidase inhibitors

MAO-A inhibitors are effective in decreasing the metabolism of norepinephrine and 5-HT; for this reason they are used as antidepressants, while MAO-B inhibitors are used for the treatment of Parkinson's disease due to their role in dopamine metabolism (Finberg and Rabey 2016). MAO inhibitors can be irreversible or reversible in effect and can be selective for MAO-A or MAO-B. Nevertheless, the use of non-selective and irreversible inhibitors requires dietary restrictions due to serious cardiovascular effects that occur if during treatment, foods containing tyramine are consumed; interactions with those inhibitors have been called “cheese effect” and tyramine is found (in these cases) mainly in sympathetic neurons (Finberg and Gillman 2011; Rendić et al. 2022). It is also reported that the effects of these non-selective and irreversible drugs can result in the suicide of a patient (Finberg 2014). Hence, a selective and reversible inhibition of MAO-A is desirable to treat most depressive disorders.

On the other hand, Computer-aided drug discovery (CADD) is being widely used in the development of new drugs, from pharmaceutical industries to research groups (Medina-Franco 2021). CADD includes various theoretical disciplines such as cheminformatics, bioinformatics, data mining and more recently artificial intelligence (López-López et al. 2021). Molecular docking is a tool that searches for the most feasible binding geometry of a ligand in the 3D space of the receptor active site, positions are scored resulting in interaction strength (Kitchen et al. 2004; Madanagopal et al. 2022). Density functional theory (DFT) reformulates the problem to be able to obtain, for example, the energy and electron distribution of the ground state, working with the electron density functional instead of the wave function (Hoffmann and Rychlewski 2002). With the theory of density functionals, certain molecular descriptors such as chemical potential, hardness, and softness can be rigorously defined and quantified because DFT works with electronic density (LaPointe and Weaver 2007; García-Toral et al. 2022; Zhao et al. 2022).

The use of computational tools in research for new drugs has proven to be highly efficient in terms of cost and benefit, providing new opportunities to increase the precision and effectiveness of the drug development process through in silico methods. This paper presents the results of our research and development efforts focused on designing a new selective and reversible MAO inhibitor drug with fewer side effects using CADD tools. In this regard, an in silico design of novel reversible MAO inhibitor drugs was proposed, which could de-absorbed from the substrate by competition effects with serotonin at high concentrations and could prevent the serotonergic syndrome.


A total of 10,100 chalcone-derived molecules were obtained (Additional files 1, 2, and 3); then different screening techniques were used for physicochemical properties, molecular docking, molecular dynamics, and DFT calculations to select the best candidates. Drugs used as MAO-A inhibitors were evaluated as a reference comparison.

Molecular docking and dynamics

The resulting molecules of the chalcone derivatives were selected based on the docking results. The highest affinity energy observed between chalcone derivatives and MAO-A was 13 kcal/mol (Table 1); however, this molecule (335) has also high affinity for MAO-B, which would not allow selectivity. In consequence, the molecule selected was ligand 356 due to its high affinity for MAO-A and low affinity energy for MAO-B, which suggests selective binding properties.

Table 1 Docking molecular score of chalcone derivatives with MAO-A and MAO-B enzymes

Compound 356 (Fig. 2a) occupies the active site of the MAO-A protein, making it a potential competitive inhibitor (Fig. 2b). Two key interactions were presented as hydrogen bonding between the protein and the ligand; Arg206 and Gly110 (Fig. 2c). The interaction with these two amino acids has been previously reported for other molecules that have been proposed as MAO-A inhibitors (Badavath et al. 2016; Esfahani and Mirzaei 2019; Yusufzai et al. 2018).

Fig. 2
figure 2

a Molecule 356 and SMILE code; b Interaction resulting from molecular docking assays between the 356 ligand and the MAO-A protein; c 2D diagram of amino acid interaction

The stability of the interaction was corroborated by molecular dynamics (Fig. 3), where it is observed that the root mean square deviation (RMSD) between the protein, the ligand and the cofactor is less than 0.6 nm (Fig. 3a). There is a hydrogen bond between the ligand and the cofactor. Still, it is not stable over time, and up to 6 hydrogen bonds are formed with the ligand and the protein, giving stability to the molecule at the binding site of the protein (Fig. 3b). The root mean square fluctuation (RMSF; Fig. 3c) is a measure of the movement of the atoms of the protein chain; the region with the greatest movement of the MAO-A is the beginning between 1 and 200 atoms. Radius of gyration (Rg) was measured in the entire trajectory, the results show a variation of 0.05 nm (2.32–2.37 nm), which verifies the stability of the tertiary structure of the protein with respect to its center of mass (Fig. 3d). MD results indicate that the interaction between compound 356 and MAO-A is stable and will also have selectivity with respect to MAO-B.

Fig. 3
figure 3

Molecular dynamics results of the MAO-A receptor ligand-356 complex. a RMSD results of each component of the complex; b Hydrogen bonds formed between the components of the system; c RMSF of the MAO-A; d Radius of gyration

On the other hand, molecular docking studies of the inhibitors show that the affinity energy varies between − 6.10 and − 8.87 kcal/mol (Table 2). Irreversible inhibitors would be expected to have higher affinity energy than reversible inhibitors. However, the results do not correlate; since moclobemide, a reversible inhibitor has higher affinity energy than irreversible inhibitors. Therefore, the affinity energy result cannot be considered as a measure of the potential reversibility of the proposed compounds. Hence, new studies were proposed using molecular dynamics and DFT calculations.

Table 2 Molecular docking results of the main commercial drugs used to inhibit MAO-A

Density functional theory (DFT) calculations

DFT results were used to evaluate the reactivity of the molecules, in order to select the best candidates that could be reversible inhibitors of MAO-A such as moclobemide. Considering the HOMO–LUMO energy change (ΔE), which has a direct correlation with the rest of the values (Fig. 4), the MAO-A inhibitors can be divided into three groups: pargyline and tranylcypromine with values of 5.9 and 5.8 eV, phenelzine and clorgyline with a ΔE of 4.7 eV, and finally the M30 which has a lower value of 3.1 eV. Moclobemide, which is the reference molecule as a reversible inhibitor, has a lower value than the first two groups, but higher than M30. The HOMO–LUMO energy change is an indicator of the kinetic stability of the molecule. The higher the ΔE, the greater the stability of the molecule; a high reactivity of the molecule is associated with a lower value of ΔE (Luo et al. 2006; Miar et al. 2021).

Fig. 4
figure 4

Molecular orbital diagram, HOMO–LUMO energy change (ΔE); a Commercial drugs; and b Chalcone derivatives

DFT results analyses of already approved drugs were used as a comparison point. A color gradient was established from red to green, with the most reactive drugs being represented by the red end and the most stable ones by the green end (Table 3).

Table 3 DFT results of MAO-A inhibitors and new molecules

Ionization potential (I) is the energy required to remove an electron from the ground state of a molecule; if a higher energy is required, it is considered more stable (green). On the other hand, the electron affinity (A) is the energy released when a molecule in its ground state captures an electron; a high value of ionization potential indicates chemical stability (green), while a high value of electron affinity indicates that it is more likely to accept electrons and therefore more unstable (red). The ionization potential has values from 5.6 to 5.77 eV in the MAO-A, except for phenelzine, which has a value of 4.6 eV; no difference is observed in this parameter between reversible and irreversible inhibitors. While the electronic affinity presents two extremes, from − 0.254 to 2.7 eV, leaving moclobemide at an intermediate value of 1.19 eV.

Hardness (η) and softness (σ) are descriptors of the behavior of a molecule in a chemical reaction. Hard molecules have a high resistance to changing their electron distribution during a reaction (higher values are stable, green), while soft molecules have a low resistance to changing their electron distribution during a reaction (lower values are stable, green). As in previous parameters, moclobemide has an intermediate value between the rest of the irreversible inhibitors. It could be considered that a low reactivity also implies that once the covalent bond between flavin adenine dinucleotide (FAD) and the drug is formed, it cannot be easily broken.

The chemical potential (µ) indicates the possibility of a chemical reaction, the high value of µ (less negative) means that it is easier to donate electrons (electron donor), while a small value of µ (more negative) means that it is easier to accept electrons (electron acceptor). Moclobemide and M30 have the highest probability of accepting electrons in a chemical reaction, with the 356 molecules having a chemical potential very close to that of moclobemide, which is the reversible inhibitor.

The electronegativity (χ) of a molecule measures its ability to attract electrons, a lower value is more stable (green). Electrophilicity (ω) is a predictor of the electrophilic nature of a chemical species; measures the propensity of the molecule to accept an electron, with high values of ω they are more reactive (red). The following is a classification of organic molecules based on electrophilicity; weak electrophiles have ω less than 0.8 eV, moderate electrophiles have ω in the range between 0.8 and 1.5 eV, and strong electrophiles have ω greater than 1.5 eV (Edim et al. 2021). Based on this classification, pargyline, phenelzine, and tranylcypromine are moderate electrophiles, and all other molecules are strong electrophiles. The highest values of electronegativity and electrophilic are for drug M30, which presented different values from the rest of the drugs.

In brief, molecule 356 was the best candidate to inhibit MAO-A, due to its high affinity for MAO-A (− 12.9 kcal/mol), and low affinity energy for MAO-B (− 8.8 kcal/mol), which indicates good selectivity. Additionally, molecule 356 presents electronic characteristics and values of medium reactivity similar to moclobemide, considering M30 as a more reactive molecule and pargyline as a more stable molecule.


Chalcone derivatives have served as the central nucleus for the design of drugs such as antimicrobials (Guo et al. 2019), anticancer (Ouyang et al. 2021), and anti-inflammatories (Amir et al. 2022), among others (Duran et al. 2021; Weyesa et al. 2021). Proposals for chalcone derivatives as MAO-B inhibitors have been made using coumarin conjugates (Moya-Alvarado et al. 2021), ethoxylated derivatives (Maliyakkal et al. 2022), aldoxime and hydroxy functionalized (Oh et al. 2022). MAO-A inhibitors derived from chalcone have also been designed and they have activity as antidepressants (at a dose of 100 mg/kg) and selectivity for this isoform A (Tan et al. 2022). Chalcone's pharmacological and pharmacokinetic properties can be modified by introducing different substituents on either of its two rings or by attaching other biologically active functional groups (Gomes et al. 2017). These chalcone derivatives features have prompted our decision of selecting these compounds for studying viable drugs to treat neurodegenerative disorders.

We obtained the new molecules considering that the MAO-A active site is hydrophobic, and it has a different shape from MAO-B. MAO-A active site allows larger molecules to interact with the FAD (flavin adenine dinucleotide) cofactor for not having a constriction (tunnel) (Ramsay et al. 2020; Maršavelski et al. 2022).

Irreversible inhibition occurs due to the formation of a covalent bond between the inhibitor and FAD. For example, tranylcypromine forms an adduct at C4a (Bonivento et al. 2010; Ramsay et al. 2020), while phenelzine, pargyline, clorgyline, selegiline, and rasagiline form adducts on N5 (Binda et al. 2002, 2004, 2008; De Colibus et al. 2005). Some studies determined that the interaction of inhibitors with the FAD cofactor should be a reversible interaction in drug design (Binda et al. 2005; Borštnar et al. 2011; Ganesan 2018; Mardani Moghanaki et al. 2022). Our results indicate that a covalent bond will not be formed as there is no stable interaction between the ligand and the FAD.

Irreversible inhibitors are expected to have higher affinity energy than reversible inhibitors. However, moclobemide was the drug with the highest affinity energy for MAO-A with respect to MAO-B (0.72 kcal/mol), and this is a selective reversible inhibitor (Haefely et al. 1992; Kumar et al. 2022). Therefore, we did not consider the affinity energy as a measure of the potential reversibility of the proposed compounds. To address this issue, we proposed a complete study using molecular dynamics and DFT calculations. Although MAO-A irreversible inhibitors have been reported to be more effective than antidepressants, side effects represent a health problem, so inhibitors such as moclobemide are more recommended (Shulman et al. 2013).

Various studies indicate that molecular docking results coincide with experimental selectivity of the affinity of inhibitors between MAO-A and MAO-B (Chaurasiya et al. 2019; Larit et al. 2018; Yelekçi et al. 2007).

The analysis of our docking results indicates that compound 356 occupies the active site of the MAO-A protein and therefore could be a potential competitive inhibitor, as mentioned in the results section. The stability of the interaction of compound 356 was corroborated by molecular dynamics and the results indicate that the interaction between compound 356 and MAO-A is stable and will also have selectivity with respect to MAO-B. Indicating that this molecule has potential as a component in a drug design for its inhibitory activity of MAO-A, and for being selective and reversible.

The comparison of our proposed compound with inhibitors of MAO-A and MAO-B currently used such as pargyline, tranylcypromine, phenelzine, clorgiline, moclobemide and M30 indicates that compound 356 has medium stability, with similar behavior with moclobemide. Therefore, this study allows the possibility for an in vitro and in vivo evaluation. Computational studies such as ours can enhance the generation of new drugs because they are inexpensive compared with experimental studies for each molecule.


In this study, a library of 10,100 compounds was generated and screened using bioinformatic tools to obtain the best molecule to selectively and reversibly inhibit MAO-A. Molecule 356 showed the best characteristics of high affinity for MAO-A with respect to MAO-B, stability in the binding site and electronic characteristics that will allow it to be a reversible inhibitor. The feature of being a reversible inhibitor could minimize the side effects of the drug, because it may not interfere with enzyme’s function when monoamine concentrations are low This proposal should decrease experimental costs in drug testing for neurodegenerative diseases. Therefore, our in silico design of a reversible inhibitor of isoform A of enzyme monoamine oxidase can be used in further experimental designs of novel drugs with minimal side effects.


Chalcone backbone was used as the starting structure to perform different substitutions with R groups at para and meta positions (Fig. 5A, B, respectively). Ten different R groups were used as substituents (Fig. 5C), from three pharmacophoric groups: hydrophobic, hydrogen bond donors and acceptors. The SMILIB software (Schüller et al. 2007) was used and 100 molecules (resulting from substitutions in para) and 10,000 molecules (from substitutions in meta) were obtained in SMILES code. Both resulting databases (10,100 compounds) were opened in DataWarrior software (Sander et al. 2015; Yang et al. 2019), where they were screened using the following criteria: ≤ 5 hydrogen bond donors, ≤ 10 hydrogen bond acceptors, molecular weight ≤ 500 Da, and cLogP ≤ 5. Obtaining a resulting database of 3321 molecules. 3D coordinates of the resulting molecules were constructed using OpenBabel software (O’Boyle et al. 2011) and transformed into pdbqt format.

Fig. 5
figure 5

Developmental components of chalcone derivatives. A Substitution of chalcone in para sites of benzene rings. B Substitution in meta sites. C Substituents used in both substitution schemes. Images generated using MarvinSketch

Molecular docking assays were performed to select the best ligands. Crystallized MAO-A protein (code 2BXR) was downloaded, UCSF Chimera software was used to clean the protein and add polar hydrogens, only FAD cofactor was preserved (Pettersen et al. 2004). Using AutoDock Tools software, the protein was converted to pdbqt format, and the grid coordinates for directed docking with AutoDock VINA were generated, and virtual screening was performed using bash scripting (Jaghoori et al. 2016; Trott and Olson 2010). The 100 molecules with the highest affinity energy were selected and molecular docking assays were performed with MAO-B (code 2V5Z), selecting the 10 molecules with the greatest difference between the affinity energy of both enzymes (Table 1). For comparison purposes, molecular docking was performed with the common drugs used as MAO-A inhibitors, with the enzymes MAO-A and MAO-B (Table 2) (Happe 2007).

Molecular dynamics tests were carried out to evaluate the stability of the ligand-receptor complexes that resulted from the molecular docking tests. Charmm-gui platform was used to prepare the different inputs, and the Gromacs 2021.1 software for molecular dynamics (Abraham et al. 2015; Jo et al. 2008; Lindahl et al. 2021; Kutzner 2022). Each protein was preprocessed using the PDB reader tool (Jo et al. 2014). On the other hand, the resulting docking ligands with the highest affinity energy were selected and changed to mol2 format using OpenBabel (O’Boyle et al. 2011). The “mol2” files of the ligands were loaded into the Ligand Reader & Modeler tool to generate the parameters and topology files (Kim et al. 2017). The ligand-receptor complexes were integrated into a single.pdb file to be used in the “Solution Builder'' tool to create the system that was used as input for Gromacs (Lee et al. 2016). Water box was cubic, fit to protein size, and 10 Å of edge distance. Each system was neutralized using KCl ions placed by Monte-Carlo method, at concentration of 0.15 M. Each system underwent 5000 steps of steepest descents energy minimization to remove steric overlap. Afterward, all the systems were subjected to a NVT (constant number of particles, volume and temperature) equilibration phase for 125,000 steps, using the V-rescale temperature-coupling method, with constant coupling of 1 ps at 303.15 K (Bussi et al. 2007). Subsequently, the molecular dynamics was carried out for 100 ns using the CHARMM36m force field (Vanommeslaeghe et al. 2010). Trajectory Analysis: Gromacs utilities were used to evaluate the root mean square deviation (RMSD) of the complexes, as well as that of each protein and ligand, root mean square fluctuation (RMSF), and hydrogen bonds. The data were graphed using the GRACE program.

DFT calculations were performed using Gaussian 09 software. GaussView 5.0.8 graphical interface was used to design the molecules (Frisch et al. 2016). Subsequently, calculations and optimization were made using the Becke-3 Parameter-Lee–Yang–Parr (B3LYP) model and the base 6-31G to optimize the best conformation of the molecules (Runge & Gross 1984). First step was the optimization of the geometric structures and vibrational frequencies in search of a minimum of local energy, which is corroborated by verifying that the frequencies presented in the calculation are not negative. Next, the ionic energy and cationic energy calculations for the minimum energy structures were performed. Finally, the electronic energies, ionization potentials, electronic affinity, hardness, and softness for each molecule were obtained.

Quantum calculations allow us to understand the stability of molecules and their chemical reactivity. Koopmans developed a theorem to relate the chemical activities of molecular structures to their electronic properties (Luo et al. 2006). Quantum chemical descriptors derived from Koopman theorem: ionization potential (I), electron affinity (A), hardness (η), softness (σ), chemical potential (µ), electronegativity (Χ), and electrophilicity (ω). These parameters are derived from HOMO energy and LUMO energy calculations that are mathematically defined below:

Equations 18:

$$\Delta E = E_{{{\text{LUMO}}}} - E_{{{\text{HOMO}}}}$$
$$I = - E_{{{\text{HOMO}}}}$$
$$A = - E_{{{\text{LUMO}}}}$$
$$\eta = \frac{I - A}{2}$$
$$\sigma = \frac{1}{\eta } = \frac{I - A}{2}$$
$$X = \frac{I + A}{2}$$
$$\mu = - X$$
$$\omega = \frac{{X^{2} }}{2\eta }$$

Availability of data and materials

Data generated or analyzed during this study are included in this published article and its Additional files 1, 2 and 3.



Enzyme monoamine oxidase


Enzyme monoamine oxidase isoform A


Enzyme monoamine oxidase isoform B


Density functional theory


Computer-aided drug discovery


Root mean square deviation


Root mean square fluctuation


Flavin adenine dinucleotide, redox cofactor


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The authors are grateful to CONACYT Frontiers of Science Project No. 845101, “Accelerated Discovery of Antifouling Materials (ADAM)” for their financial and technical support. Karla Gabriela Montes Rangel and Sebastian García Perez for their support in validating some bibliographic data. The computational resources for this investigation were facilitated by UNISON/Acarus. And the support from Luis Aguilar, Alejandro De León, and Jair García of the Laboratorio Nacional de Visualización Científica Avanzada, UNAM. Special gratitude to Ana L. Ramos-Jacques at Edit Academy for her assistance in editing and proofreading the manuscript.


This research received no specific grant from any funding agency in the commercial, or not-for-profit sectors.

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RCA and NADE performed and analyzed the Molecular docking results regarding the enzymes; RCA was a major contributor in writing the manuscript. FLNS performed and analyzed the DFT results. QGF contributed to and supported the process of conceptualization of the research, contributing critical questions to formulate the research problem, as well as reviewing and analyzing the overall results of the report. HMAR was responsible for the conceptualization, management, and supervision of the activities of the research group; He defined the objectives and hypothesis, specified the research, prioritizing the scope of the project, and placed the reasoning behind the research approach. All authors reviewed and approved the collection methods and analysis methods. All authors read and approved the final manuscript.

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Correspondence to A. R. Hernandez-Martinez.

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Supplementary Information

Additional file 1.

Route 1 of molecule 356 retrosynthesis proposal obtained with SciFinder.

Additional file 2.

Route 2 of molecule 356 retrosynthesis proposal obtained with SciFinder.

Additional file 3.

SMILIB compounds full data_base.

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Reyes-Chaparro, A., Flores-Lopez, N.S., Quintanilla-Guerrero, F. et al. Design of new reversible and selective inhibitors of monoamine oxidase A and a comparison with drugs already approved. Bull Natl Res Cent 47, 46 (2023).

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