Density Functional Theory of Electronic Structure

Water-soluble extracellular vesicle probes based on conjugated oligoelectrolytes

We developed a series of transmembrane conjugated oligoelectrolytes (COEs) with tunable optical emissions from the UV to the near IR to address the false-positive problem when detecting nanometer-sized extracellular vesicles (EVs) by flow cytometry. The amphiphilic molecular framework of COEs is defined by a linear conjugated structure and cationic charged groups at each terminal site. Consequently, COEs have excellent water solubility and the absence of nanoaggregates at concentrations up to 50 μM, and unbound COE dyes can be readily removed through ultrafiltration. These properties enable unambiguous and simple detection of COE-labeled small EVs using flow cytometry with negligible background signals. We also demonstrated the time-lapsed tracking of small EV uptake into mammalian cells and the endogenous small EV labeling using COEs. Briefly, COEs provide a class of membrane-targeting dyes that behave as biomimetics of the lipid bilayer and a general and practical labeling strategy for nanosized EVs.

Machine Learning Diffusion Monte Carlo Forces

Diffusion Monte Carlo (DMC) is one of the most accurate techniques available for calculating the electronic properties of molecules and materials, yet it often remains a challenge to economically compute forces using this technique. As a result, ab initio molecular dynamics simulations and geometry optimizations that employ Diffusion Monte Carlo forces are often out of reach. One potential approach for accelerating the computation of "DMC forces" is to machine learn these forces from DMC energy calculations. In this work, we employ Behler-Parrinello Neural Networks to learn DMC forces from DMC energy calculations for geometry optimization and molecular dynamics simulations of small molecules. We illustrate the unique challenges that stem from learning forces without explicit force data and from noisy energy data by making rigorous comparisons of potential energy surface, dynamics, and optimization predictions among ab initio density functional theory (DFT) simulations and machine-learning models trained on DFT energies with forces, DFT energies without forces, and DMC energies without forces. We show for three small molecules─C₂, H₂O, and CH₃Cl─that machine-learned DMC dynamics can reproduce average bond lengths and angles within a few percent of known experimental results at one hundredth of the typical cost. Our work describes a much-needed means of performing dynamics simulations on high-accuracy, DMC PESs and for generating DMC-quality molecular geometries given current algorithmic constraints.

Assignment of Vibrational Bands of Critical Surface Species Containing Nitrogen in the Selective Catalytic Reduction of NO by NH3

The selective catalytic reduction (SCR) of NO by NH₃ on metal oxides plays a key role in minimizing NO_x emissions. Electronic structure calculations at the density functional theory level have been performed to predict the vibrational modes of NH₃/NH₄⁺ bound to validated cluster models of vanadium oxide bound to a TiO₂ surface. Excellent agreement of the scaled calculated values with the observed bands attributed to surface-bound species is found. The presence of NH₃ bound to Lewis acid sites and NH₄⁺ bound to Brønsted acid sites when VOH groups are present is supported by our predictions. NH₄⁺ is expected to dominate the spectra even at low concentrations, with predicted intensities 5 to 30 times greater than those predicted for surface-bound NH₃. This is particularly evident in the lowest-energy N-H stretches of surface NH₄⁺ due to partial proton transfer interactions with the vanadium oxide surface model. The current work is consistent with experimental vibrational spectroscopy results and does not support the presence of a significant amount of NH₂ on the catalyst surface for the SCR reaction on VO_x/TiO₂. The combined experimental and computational results support the presence of both NH₃- and NH₄⁺-type species bound to the surface.

State averaged CASSCF in AMOEBA polarizable water model for simulating nonadiabatic molecular dynamics with nonequilibrium solvation effects

This paper presents a state-averaged complete active space self-consistent field (SA-CASSCF) in the atomic multipole optimized energetics for biomolecular application (AMOEBA) polarizable water model, which enables rigorous simulation of non-adiabatic molecular dynamics with nonequilibrium solvation effects. The molecular orbital and configuration interaction coefficients of the solute wavefunction, and the induced dipoles on solvent atoms, are solved by minimizing the state averaged energy variationally. In particular, by formulating AMOEBA water models and the polarizable continuum model (PCM) in a unified way, the algorithms developed for computing SA-CASSCF/PCM energies, analytical gradients, and non-adiabatic couplings in our previous work can be generalized to SA-CASSCF/AMOEBA by properly substituting a specific list of variables. Implementation of this method will be discussed with the emphasis on how the calculations of different terms are partitioned between the quantum chemistry and molecular mechanics codes. We will present and discuss results that demonstrate the accuracy and performance of the implementation. Next, we will discuss results that compare three solvent models that work with SA-CASSCF, i.e., PCM, fixed-charge force fields, and the newly implemented AMOEBA. Finally, the new SA-CASSCF/AMOEBA method has been interfaced with the ab initio multiple spawning method to carry out non-adiabatic molecular dynamics simulations. This method is demonstrated by simulating the photodynamics of the model retinal protonated Schiff base molecule in water.

Photodegradation of a mixture of five pharmaceuticals commonly found in wastewater: Experimental and computational analysis

Photochemical transformation of pharmaceuticals plays an important role in their natural attenuation, especially in lagoon-based wastewater treatment plants and surface waters receiving substantial sunlight. In this study, the photodegradation of five important pharmaceuticals was studied in samples obtained from a wastewater treatment plant and surface water sources. Batch photodegradation studies for a mixture of pharmaceuticals (diclofenac, sulfamethoxazole, acetaminophen, carbamazepine and gemfibrozil) were carried out in a photochemical reactor. Multiple aliquots of samples removed from the reactor during the experiment were analyzed through high-performance liquid chromatography (HPLC) coupled to a photodiode array (PDA) detector. Intermediate products formed due to photodegradation were identified by ultra-high-performance liquid chromatography coupled with a time-of-flight mass spectrometry (UHPLC-MS/MS). Diclofenac and sulfamethoxazole were found to undergo direct photodegradation due to strong light absorption, whereas the indirect route of photosensitized degradation in the presence of dissolved organic matter (DOM) and model humic acid was significant for acetaminophen, carbamazepine, and gemfibrozil. The reactive radicals such as hydroxyl (OH^•), singlet oxygen (¹O₂) and excited states of DOM (*DOM) were predominantly responsible for the indirect photodegradation of acetaminophen, gemfibrozil and carbamazepine, respectively. Computational analysis revealed that chlorine and carbon atoms belonging to the benzene ring of diclofenac were more reactive to radical attack. Sulfamethoxazole photodegradation occurred through oxidation of the NH₂ group. Acetaminophen was more susceptible to electrophilic radical attack at the O-11, and N-7 positions and carbon atoms ortho to the phenolic oxygen and the amine group. The double bonds between C-7, C-8 and C-13 were the most reactive sites for carbamazepine that participated in the phototransformation pathway. Organic matter plays a critical role in the photodegradation of emerging contaminants. The coupling of DFT calculations with UHPLC-MS/MS analysis provided insights on key functional groups participating in the phototransformation pathway. Thus, both parent pharmaceuticals and the photodegradation intermediates should be considered during wastewater treatment.

Investigation of the Effect of Substituents on Electronic and Charge Transport Properties of Benzothiazole Derivatives

A series of new benzothiazole-derived donor-acceptor-based compounds (Comp1-4) were synthesized and characterized with the objective of tuning their multifunctional properties, i.e., charge transport, electronic, and optical. All the proposed structural formulations (Comp1-4) were commensurate using FTIR, ¹H NMR, ¹³C NMR, ESI-mass, UV-vis, and elemental analysis techniques. The effects of the electron-donating group (-CH₃) and electron-withdrawing group (-NO₂) on the optoelectronic and charge transfer properties were studied. The substituent effect on absorption was calculated at the TD-B3LYP/6-31+G** level in the gas and solvent phases. The effect of solvent polarity on the absorption spectra using various polar and nonpolar solvents, i.e., ethanol, acetone, DMF, and DMSO was investigated. Light was shed on the charge transport in benzothiazole compounds by calculating electron affinity, ionization potential, and reorganization energies. Furthermore, the synthesized compounds were used to prepare thin films on the FTO substrate to evaluate the charge carrier mobility and other related device parameters with the help of I-V characteristic measurements.

Tuning the Electronic and Charge Transport Properties of Schiff Base Compounds by Electron Donor and/or Acceptor Groups

Organic semiconductors have gained substantial interest as active materials in electronic devices due to their advantages over conventional semiconductors. We first designed four Schiff base compounds, then the effect of electron donor/acceptor groups (methyl/nitro) was studied on the compounds' electronic and transport nature. The absorption spectra (λ_abs) were computed by time-dependent DFT at TD-B3LYP/6-31+G** level. The effect of different solvents (ethanol, DMF, DMSO, and acetone) was investigated on the λ_abs. The substitution of the -NO₂ group to the furan moiety at the 5th position in Compound 3 leads to a red-shift in the absorption spectrum. A smaller hole reorganization energy value in Compound 3 would be beneficial to get the hole's intrinsic mobility. In contrast, a reduced-electron reorganization energy value of Compound 4 than hole may result in enhanced electron charge transfer capabilities. The reorganization energies of compounds 1 and 2 exposed balanced hole/electron transport probability. The optical, electronic, and charge transport properties at the molecular level indicate that Compound 3 is suitable for organic electronic device applications.

Computational investigation of natural compounds as potential main protease (Mpro) inhibitors for SARS-CoV-2 virus

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is significantly impacting human lives, overburdening the healthcare system and weakening global economies. Plant-derived natural compounds are being largely tested for their efficacy against COVID-19 targets to combat SARS-CoV-2 infection. The SARS-CoV-2 Main protease (M^pro) is considered an appealing target because of its role in replication in host cells. We curated a set of 7809 natural compounds by combining the collections of five databases viz Dr Duke's Phytochemical and Ethnobotanical database, IMPPAT, PhytoHub, AromaDb and Zinc. We applied a rigorous computational approach to identify lead molecules from our curated compound set using docking, dynamic simulations, the free energy of binding and DFT calculations. Theaflavin and ginkgetin have emerged as better molecules with a similar inhibition profile in both SARS-CoV-2 and Omicron variants.

On the Periodicity of the Information Theory and Conceptual DFT-Based Reactivity Descriptors

The periodic trends in conceptual density functional and information theory-based reactivity descriptors are reported for the atoms H to Ba (Z = 1 to 56). Ionization potential, electron affinity, electronegativity, and hardness show periodic behavior following the Aufbau principle and popular electronic structure principles. They are in agreement with those reported in standard chemistry textbooks. The trend in the electrophilicity index, however, shows an interesting behavior, where it contradicts earlier reports. Our calculation reveals that the noble gas elements correspond to minimum ω values in each period which obey the minimum electrophilicity principle as well as reflect their low reactivity. Periodic trends in electroaccepting and electrodonating powers, along with that of net electrophilicity, are as expected. The behavior of information theory-based Shannon and GBP entropies, along with the Shannon entropy of shape function are also explored across the periodic table.

Strong Field Double Ionization of Formaldehyde Investigated Using Momentum Resolved Covariance Imaging and Trajectory Surface Hopping

We use covariance velocity map imaging of fragment ions from the strong field double ionization of formaldehyde in conjunction with trajectory surface hopping calculations to determine the ionization yields to different singlet and triplet states of the dication. The calculated kinetic energy release for trajectories initiated on different electronic states is compared with the experimental values based on momentum resolved covariance measurements. We determine the state resolved double ionization yields as a function of laser intensity and pulse duration down to 6 fs (two optical cycles).

The impact of G-quadruplex dynamics on inter-tetrad electronic couplings: a hybrid computational study

The G-quadruplex is a fascinating nucleic acid motif with implications in biology, medicine, and nanotechnologies. G-quadruplexes can form in the telomeres at the edges of chromosomes and in other guanine-rich regions of the genome. They can also be engineered for exploitation as biological materials for nanodevices. Their higher stiffness and higher charge transfer rates make them better candidates in nanodevices than duplex DNA. For the development of molecular nanowires, it is important to optimize electron transport along the wire axis. One powerful basis to do so is by manipulating the structure, based on known effects that structural changes have on electron transport. Here, we investigate such effects, by a combination of classical simulations of the structure and dynamics and quantum calculations of electronic couplings. We find that this structure-function relationship is complex. A single helix shape parameter alone does not embody such complexity, but rather a combination of distances and angles between stacked bases influences charge transfer efficiency. By analyzing linear combinations of shape descriptors for different topologies, we identify the structural features that most affect charge transfer efficiency. We discuss the transferability of the proposed model and the limiting effects of inherent flexibility.

Revealing the promotion of carbonyl groups on vacancy stabilized Pt4/nanocarbons for propane dehydrogenation

Nanocarbons are promising supports for Pt clusters applied in propane dehydrogenation (PDH), owing to their large surface areas and tunable chemical properties. The vacancies and oxygen-containing groups (OCGs) in nanocarbons can enhance catalytic performance by tailoring the coordination environment of Pt clusters. Herein, 46 nanocarbons with coexisting vacancies and OCGs were designed to support Pt clusters, of which the influences on PDH were revealed by density functional theory calculations. Nanocarbons with divacancies (V2) and CO edge groups were screened out as the most appropriate support for Pt clusters in PDH. Due to the V2, tetrahedral Pt clusters were distorted into three-layered configurations, contributing to enhanced binding strength and a favorable reactive pathway starting from the methylene group in propane. This changed the rate-determining step to the first C-H bond scission with a low energy barrier. The introduction of CO edge groups coexisting with V2 further improved the stabilization of Pt clusters, resulting from the increased electron transfer from Pt atoms to C atoms. The abilities to break C-H bonds and inhibit C-C bond cracking were also enhanced as compared to the nanocarbons with only V2. Therefore, this work provides references on the regulation of vacancies and OCGs in carbon-based catalysts.

2D-QSAR, molecular docking, drug-likeness, and ADMET/pharmacokinetic predictions of some non-small cell lung cancer therapeutic agents

Objectives

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with nearly 2 million diagnoses and a 17% 5-year survival rate. The aim of this study was to use computer-aided techniques to identify potential therapeutic agents for NSCLC.

Methods

The two dimensional-quantitative structure-activity relationship (2D-QSAR) modeling was employed on some potential NSCLC therapeutic agents to develop a highly predictive model. Molecular docking-based virtual screening were conducted on the same set of compounds to identify potential hit compounds. The pharmacokinetic features of the best hits were evaluated using SWISSADME and pkCSM online web servers, respectively.

Results

The model generated via 2D-QSAR modeling was highly predictive with R²= 0.798, R²adj = 0.754, Q²CV = 0.673, R² test = 0.531, and cRp² = 0.627 assessment parameters. Molecular docking-based virtual screening identified compounds 25, 32, 15, 21, and 23 with the highest MolDock scores as the best hits, of which compound 25 had the highest MolDock score of -138.329 kcal/mol. All of the identified hits had higher MolDock scores than the standard drug (osimertinib). The best hit compounds were ascertained to be drug-like in nature following the Lipinski's rule of five. Also, their ADMET features displayed average pharmacokinetic profiles.

Conclusion

After successful preclinical testing, the hit compounds identified in this study may serve as potential NSCLC therapeutic agents due to their safety and efficacy with the exception of compound 23, which was found to be toxic. They can also serve as a template for designing novel NSCLC therapeutic agents.

A computational study of potential therapeutics for COVID-19 invoking conceptual density functional theory

The pandemic, COVID-19, has caused social and economic disruption at a larger pace all over the world. Identification of an effective drug for the deadliest disease is still an exigency. One of the most promising approaches to combat the lethal disease is use of repurposed drugs. This study provides insights into some of the potential repurposed drugs viz. camostat mesylate, hydroxychloroquine, nitazoxanide, and oseltamivir in terms of the computational quantum chemical method. Properties of these compounds have been elucidated in terms of Conceptual Density Functional Theory (CDFT)-based descriptors, IR spectra, and thermochemical properties. Computed results specify that hydroxychloroquine is the most reactive drug among them. Thermochemical data reveals that camostat mesylate has the utmost heat capacity, entropy, and thermal energy. Our findings indicate that camostat mesylate and hydroxychloroquine may be investigated further as potential COVID-19 therapeutics. We anticipate that the current study will aid the scientific community to design and develop viable therapeutics against COVID-19.

Density functionals for core excitations

The core excitation energies and related principal ionization energies are obtained for selected molecules using several density functionals and compared with benchmark equation-of-motion coupled cluster (EOM-CC) results. Both time-dependent and time-independent formulations of excitation spectra in the time-dependent density functional theory and the EOM-CC are employed to obtain excited states that are not always easily accessible with the time-independent method. Among those functionals, we find that the QTP(00) functional, which is only parameterized to reproduce the five IPs of water, provides excellent core IPs and core excitation energies, consistently yielding better excitation and ionization energies. We show that orbital eigenvalues of KS density functional theory play an important role in determining the accuracy of the excitation and photoelectron spectra.

FINETUNA: Fine-tuning Accelerated Molecular Simulations

Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomistic systems. However, simulation techniques based on first principles, such as Density Functional Theory (DFT), are limited in their practical use due to their high computational expense. Machine learning approaches have the potential to approximate DFT in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present an online active learning framework for accelerating the simulation of atomic systems efficiently and accurately by incorporating prior physical information learned by large-scale pre-trained graph neural network models from the Open Catalyst Project. Accelerating these simulations enables useful data to be generated more cheaply, allowing better models to be trained and more atomistic systems to be screened. We also present a method of comparing local optimization techniques on the basis of both their speed and accuracy. Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer learning to incorporate prior information from pre-trained models accelerates simulations by reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive functionality built in to VASP to efficiently compute single point calculations within our online active learning framework without the significant startup costs. This allows VASP to work in tandem with our framework while requiring 75% fewer self-consistent cycles than conventional single point calculations. The online active learning implementation, and examples using VASPInteractive, are available in the open source FINETUNA package on Github.

Effect of polystyrene microplastics on the degradation of sulfamethazine: The role of persistent free radicals

Microplastic (MPs) pollution is increasingly becoming a global environmental problem. MPs entering the environment are subjected to various aging processes, among which photoaging is the most important process leading to MPs oxidation. Persistent free radicals (EPFRs) are formed on the surface of MPs during photoaging, but it is not clear whether EPFRs on the surface of MPs can produce reactive oxygen species (ROS) and thus degrade organic pollutants. In this study, with polystyrene (PS) as the representative plastic and sulfamethazine (SMT) degradation as the target pollutant, the effect and mechanism of light-induced PS on SMT degradation were investigated by experiment and theoretical calculation. It was found that PS can stimulate the production of ROS under sunlight, which can significantly improve the degradation rate of SMT. Through quenching experiment and free radical trapping experiment, it was found that the mechanism of PS promoting the degradation of SMT was mainly due to the production of hydroxyl radical (·OH) in the system, and ·OH was the main ROS species affecting the oxidative degradation of SMT. The characterization results show that the high reactive oxygen generation ability of PS under solar irradiation was due to the abundant photoactive oxidation functional groups on its surface. In addition, the key reaction sites of SMT were predicted by density functional theory (DFT) calculation. The results of different calculations consistently showed that the sulfonamide group of SMT, the pyrimidine heterocycle and the amino group of aniline are the reaction sites of ·OH priority attack. The main intermediates were determined by UHPLC-HRMS/MS. Combined with theoretical calculation, it was proposed that the oxidative degradation pathway of SMT mainly includes SN bond cleavage, SMILES rearrangement and SO₂ group removal. This study clarified the effect of PS on the degradation of organic pollutants under light, and provided theoretical guidance for the degradation mechanism.

Why Ortho- and Para-Hydroxy Metabolites Can Scavenge Free Radicals That the Parent Atorvastatin Cannot? Important Pharmacologic Insight from Quantum Chemistry

The pharmaceutical success of atorvastatin (ATV), a widely employed drug against the "bad" cholesterol (LDL) and cardiovascular diseases, traces back to its ability to scavenge free radicals. Unfortunately, information on its antioxidant properties is missing or unreliable. Here, we report detailed quantum chemical results for ATV and its ortho- and para-hydroxy metabolites (o-ATV, p-ATV) in the methanolic phase. They comprise global reactivity indices, bond order indices, and spin densities as well as all relevant enthalpies of reaction (bond dissociation BDE, ionization IP and electron attachment EA, proton detachment PDE and proton affinity PA, and electron transfer ETE). With these properties in hand, we can provide the first theoretical explanation of the experimental finding that, due to their free radical scavenging activity, ATV hydroxy metabolites rather than the parent ATV, have substantial inhibitory effect on LDL and the like. Surprisingly (because it is contrary to the most cases currently known), we unambiguously found that HAT (direct hydrogen atom transfer) rather than SPLET (sequential proton loss electron transfer) or SET-PT (stepwise electron transfer proton transfer) is the thermodynamically preferred pathway by which o-ATV and p-ATV in methanolic phase can scavenge DPPH• (1,1-diphenyl-2-picrylhydrazyl) radicals. From a quantum chemical perspective, the ATV's species investigated are surprising because of the nontrivial correlations between bond dissociation energies, bond lengths, bond order indices and pertaining stretching frequencies, which do not fit the framework of naive chemical intuition.

Stable excited dication: trapping on the S1 state of formaldehyde dication after strong field ionization

Combined theoretical and experimental work examines the dynamics of dication formaldehyde produced by strong field ionization. Trajectory surface hopping dynamics on the first several singlet electronic states of the formaldehyde dication are used to examine the relaxation pathways and dissociation channels, while kinetic energy distributions after strong field ionization of formaldehyde and deuterated formaldehyde are used to confirm the theoretical predictions. We find that the first excited state of the formaldehyde dication is stable, neither decays to the ground state nor dissociates, even though the ground state and higher lying states are directly dissociative. The stability of the first excited state is explained by its symmetry which does not allow for radiative or nonradiative transitions to the ground state and by large barriers to dissociate on the excited state surface.

Non-iterative Method for Constructing Valence Antibonding Molecular Orbitals and a Molecule-adapted Minimum Basis

While bonding molecular orbitals exhibit constructive interference relative to atomic orbitals, antibonding orbitals show destructive interference. When full localization of occupied orbitals into bonds is possible, bonding and antibonding orbitals exist in 1:1 correspondence with each other. Antibonding orbitals play an important role in chemistry because they are frontier orbitals that determine orbital interactions, as well as much of the response of the bonding orbital to perturbations. In this work, we present an efficient method to construct antibonding orbitals by finding the orbital that yields the maximum opposite spin pair correlation amplitude in second order perturbation theory (AB2) and compare it with other techniques with increasing the size of the basis set. We conclude the AB2 antibonding orbitals are a more robust alternative to the Sano orbitals as initial guesses for valence bond calculations, due to having a useful basis set limit. The AB2 orbitals are also useful for efficiently constructing an active space, and work as good initial guesses for valence excited states. In addition, when combined with the localized occupied orbitals, and relocalized, the result is a set of molecule-adapted minimal basis functions that is built without any reference to atomic orbitals of the free atom. As examples, they are applied to population analysis of halogenated methane derivatives, H-Be-Cl, and \ce{SF6} where they show some advantages relative to good alternative methods.

Designing of potent anti-diabetic molecules by targeting SIK2 using computational approaches

Diabetes mellitus (DM) is one of the major health problems worldwide. WHO have estimated that 439 million people may have DM by the year 2030. Several classes of drugs such as sulfonylureas, meglitinides, thiazolidinediones etc. are available to manage this disease, however, there is no cure for this disease. Salt inducible kinase 2 (SIK2) is expressed several folds in adipose tissue than in normal tissues and thus SIK2 is one of the attractive targets for DM treatment. SIK2 inhibition improves glucose homeostasis. Several analogues have been reported and experimentally proven against SIK for DM treatment. But, identifying potential SIK2 inhibitors with improved efficacy and good pharmacokinetic profiles will be helpful for the effective treatment of DM. The objective of the present study is to identify selective SIK2 inhibitors with good pharmacokinetic profiles. Due to the unavailability of SIK2 structure, the modeled structure of SIK2 will be an important to understand the atomic level of SIK2 inhibitors in the binding site pocket. In this study, different molecular modeling studies such as Homology Modeling, Molecular Docking, Pharmacophore-based virtual screening, MD simulations, Density Functional Theory calculations and WaterMap analysis were performed to identify potential SIK2 inhibitors. Five molecules from different databases such as Binding_4067, TosLab_837067, NCI_349155, Life chemicals_ F2565-0113, Enamine_7623111186 molecules were identified as possible SIK2 inhibitors.