An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications

Computational tools and algorithms for ion mobility spectrometry-mass spectrometry

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

An interdisciplinary course on computer-aided drug discovery to broaden student participation in original scientific research

We present a new highly interdisciplinary project-based course in computer aided drug discovery (CADD). This course was developed in response to a call for alternative pedagogical approaches during the COVID-19 pandemic, which caused the cancellation of a face-to-face summer research program sponsored by the Louisiana Biomedical Research Network (LBRN). The course integrates guided research and educational experiences for chemistry, biology, and computer science students. We implement research-based methods with publicly available tools in bioinformatics and molecular modeling to identify and prioritize promising antiviral drug candidates for COVID-19. The purpose of this course is three-fold: I. Implement an active learning and inclusive pedagogy that fosters student engagement and research mindset; II. Develop student interdisciplinary research skills that are highly beneficial in a broader scientific context; III. Demonstrate that pedagogical shifts (initially incurred during the COVID-19 pandemic) can furnish longer-term instructional benefits. The course, which has now been successfully taught a total of five times, incorporates four modules, including lectures/discussions, live demos, inquiry-based assignments, and science communication.

Predicting permeation of compounds across the outer membrane of P. aeruginosa using molecular descriptors

The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds' intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.

Integrative studies of ionic liquid interface layers: bridging experiments, theoretical models and simulations

Ionic liquids (ILs) are a class of salts existing in the liquid state below 100 °C, possessing low volatility, high thermal stability as well as many highly attractive solvent and electrochemical capabilities, etc., making them highly tunable for a great variety of applications, such as lubricants, electrolytes, and soft functional materials. In many applications, ILs are first either physi- or chemisorbed on a solid surface to successively create more functional materials. The functions of ILs at solid surfaces can differ considerably from those of bulk ILs, mainly due to distinct interfacial layers with tunable structures resulting in new ionic liquid interface layer properties and enhanced performance. Due to an almost infinite number of possible combinations among the cations and anions to form ILs, the diversity of various solid surfaces, as well as different external conditions and stimuli, a detailed molecular-level understanding of their structure-property relationship is of utmost significance for a judicious design of IL-solid interfaces with appropriate properties for task-specific applications. Many experimental techniques, such as atomic force microscopy, surface force apparatus, and so on, have been used for studying the ion structuring of the IL interface layer. Molecular Dynamics simulations have been widely used to investigate the microscopic behavior of the IL interface layer. To interpret and clarify the IL structure and dynamics as well as to predict their properties, it is always beneficial to combine both experiments and simulations as close as possible. In another theoretical model development to bridge the structure and properties of the IL interface layer with performance, thermodynamic prediction & property modeling has been demonstrated as an effective tool to add the properties and function of the studied nanomaterials. Herein, we present recent findings from applying the multiscale triangle "experiment-simulation-thermodynamic modeling" in the studies of ion structuring of ILs in the vicinity of solid surfaces, as well as how it qualitatively and quantitatively correlates to the overall ILs properties, performance, and function. We introduce the most common techniques behind "experiment-simulation-thermodynamic modeling" and how they are applied for studying the IL interface layer structuring, and we highlight the possibilities of the IL interface layer structuring in applications such as lubrication and energy storage.

Directional ΔG Neural Network (DrΔG-Net): A Modular Neural Network Approach to Binding Free Energy Prediction

The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems. We introduce DrΔG-Net (or simply Dragnet), an equivariant graph neural network that can blend ligand-based and protein-ligand data-driven approaches. It is based on a 3D fingerprint representation of the ligand alone and in complex with the protein target. Dragnet is a global scoring function to predict the binding affinity of arbitrary protein-ligand complexes, but can be easily tuned via transfer learning to specific systems or end points, performing similarly to common 2D ligand-based approaches in these tasks. Dragnet is evaluated on a total of 28 validation proteins with a set of congeneric ligands derived from the Binding DB and one custom set extracted from the ChEMBL Database. In general, a handful of experimental binding affinities are sufficient to optimize the scoring function for a particular protein and ligand scaffold. When not available, predictions from physics-based methods such as absolute free energy perturbation can be used for the transfer learning tuning of Dragnet. Furthermore, we use our data to illustrate the present limitations of data-driven modeling of binding free energy predictions.

Isolation, Characterization, Genome Annotation, and Evaluation of Tyrosinase Inhibitory Activity in Secondary Metabolites of Paenibacillus sp. JNUCC32: A Comprehensive Analysis through Molecular Docking and Molecular Dynamics Simulation

A potential strain, Paenibacillus sp. JNUCC32, was isolated and subjected to whole-genome sequencing. Genome functional annotation revealed its active metabolic capabilities. This study aimed to investigate the pivotal secondary metabolites in the biological system. Fermentation and extraction were performed, resulting in the isolation of seven known compounds: tryptophol (1), 3-(4-hydroxyphenyl)propionic acid (2), ferulic acid (3), maculosin (4), brevianamide F (5), indole-3-acetic acid (6), and butyric acid (7). Tryptophol exhibited favorable pharmacokinetic properties and demonstrated certain tyrosinase inhibitory activity (IC50 = 999 μM). For further analysis of its inhibition mechanism through molecular docking and molecular dynamics (MD) simulation, tryptophol formed three hydrogen bonds and a pro-Michaelis complex with tyrosinase (binding energy = -5.3 kcal/mol). The MD simulation indicated favorable stability for the tryptophol-mushroom tyrosinase complex, primarily governed by hydrogen bond interactions. The crucial residues VAL-283 and HIS-263 in the docking were also validated. This study suggests tryptophol as a potential candidate for antibrowning agents and dermatological research.

A combined in silico approaches of 2D-QSAR, molecular docking, molecular dynamics and ADMET prediction of anti-cancer inhibitor activity for actinonin derivatives

Inhibition of human mitochondrial peptide deformylase (HsPDF) plays a major role in reducing growth, proliferation, and cellular cancer survival. In this work, a series of 32 actinonin derivatives for HsPDF (PDB: 3G5K) inhibitor's anticancer activity was computationally analyzed for the first time, using an in silico study considering 2D-QSAR modeling, and molecular docking studies, and validated by molecular dynamics and ADMET properties. The results of multilinear regression (MLR) and artificial neural networks (ANN) statistical analysis reveal a good correlation between pIC50 activity and the seven (7) descriptors. The developed models were highly significant with cross-validation, the Y-randomization test and their applicability range. In addition, all considered data sets show that the AC30 compound, exhibits the best binding affinity (docking score = -212.074 kcal/mol and H-bonding energy = -15.879 kcal/mol). Furthermore, molecular dynamics simulations were performed at 500 ns, confirming the stability of the studied complexes under physiological conditions and validating the molecular docking results. Five selected actinonin derivatives (AC1, AC8, AC15, AC18 and AC30), exhibiting best docking score, were rationalized as potential leads for HsPDF inhibition, in well agreement with experimental outcomes. Furthermore, based on the in silico study, new six molecules (AC32, AC33, AC34, AC35, AC36 and AC37) were suggested as HsPDF inhibition candidates, which would be combined with in-vitro and in-vivo studies to perspective validation of their anticancer activity. Indeed, the ADMET predictions indicate that these six new ligands have demonstrated a fairly good drug-likeness profile.

Ce-ZSM-5: A Highly Capable Catalyst for the Construction of Acridines in Water and Their Computational Study against DNA Gyrase Protein

Cerium doped ZSM-5 (Ce-ZSM-5) as an environmentally benign and reusable catalyst for the construction of acridines in aqueous medium. This method produced corresponding acridines with good yields and shorter reaction times. Also avoids the use of hazardous solvents and involves a simple work-up process. The solid catalyst was prepared by doping Ce ions with ZSM-5 (Zeolite Socony Mobil-5) and confirmed by XRD, BET SA-PSD and SEM. The synthesised acridine derivatives were confirmed by 1 H-NMR, 13 C-NMR and FT-IR spectroscopic data. The docking studies of the synthesised compounds are performed by the PyRx auto dock tool against DNA gyrase protein. The products 5a and 6d are found to be the best fit ligands against DNA gyrase protein.

MPFFPSDC: A multi-pooling feature fusion model for predicting synergistic drug combinations

Drug combination therapies are common practice in the treatment of cancer, but not all combinations result in synergy. As traditional screening approaches are restricted in their ability to uncover synergistic drug combinations, computer-aided medicine is becoming a increasingly prevalent in this field. In this work, a predictive model of potential interactions between drugs named MPFFPSDC is presented, which can maintain the symmetry of drug inputs and eliminate inconsistencies in predictive results caused by different drug inputting sequences or positions. The experimental results show that MPFFPSDC outperforms comparative models in major performance indicators and exhibits better generalization for independent data. Furthermore, the case study demonstrates that our model can capture molecular substructures that contribute to the synergistic effect of two drugs. These results indicate that MPFFPSDC not only offers strong predictive performance, but also has good model interpretability that may provide new insights for the study of drug interaction mechanisms and the development of new drugs.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

New insights into enhanced electrochemical advanced oxidation mechanism of B-doped graphene aerogel: Experiments, molecular dynamics simulations and DFT

B-doped graphene, as an efficient and environmental-friendly metal-free catalyst, has aroused much attention in the electrochemical advanced oxidation process (EAOP), but the bottleneck in this field is to determine the relationship between the surface structure regulation and activity of catalysts. Herein, the B-doped graphene aerogel (BGA) fabricated gas diffusion electrode was prepared and used as a cathode for EAOP to remove tetracycline (TC). Higher free radical yield (169.59 μM), faster reaction speed (0.35 min^-1) and higher TC removal rate (99.93%) were found in the BGA system. Molecular dynamics simulation unveiled the interaction energy of BGA was greater than the raw graphene aerogel (GA). The adsorption-activation process of H₂O₂ and the degradation process of TC occurred in the first adsorption layer of catalysts. And both processes turned more orderly after B doping, which accelerated the reaction efficiency. Results of density functional theory displayed the contribution of three B-doped structures to improve the binding strength between H₂O₂ and BGA was: - BCO₂ (-0.23 eV) > - BC₂O (-0.16 eV) > - BC₃ (-0.09 eV). -BCO₂ was inferred to be the main functional region of H₂O₂ in-situ activation to hydroxyl radical (•OH), while -BC₂O and -BC₃ were responsible for improving H₂O₂ production.

Anti-HIV Potential of Beesioside I Derivatives as Maturation Inhibitors: Synthesis, 3D-QSAR, Molecular Docking and Molecular Dynamics Simulations

HIV-1 maturation is the final step in the retroviral lifecycle that is regulated by the proteolytic cleavage of the Gag precursor protein. As a first-in-class HIV-1 maturation inhibitor (MI), bevirimat blocks virion maturation by disrupting capsid-spacer peptide 1 (CA-SP1) cleavage, which acts as the target of MIs. Previous alterations of beesioside I (1) produced (20S,24S)-15ꞵ,16ꞵ-diacetoxy-18,24; 20,24-diepoxy-9,19-cyclolanostane-3ꞵ,25-diol 3-O-3′,3′-dimethylsuccinate (3, DSC), showing similar anti-HIV potency compared to bevirimat. To ascertain the binding modes of this derivative, further modification of compound 1 was conducted. Three-dimensional quantitative structure−activity relationship (3D-QSAR) analysis combined with docking simulations and molecular dynamics (MD) were conducted. Five new derivatives were synthesized, among which compound 3b showed significant activity against HIV-1NL4-3 with an EC50 value of 0.28 µM. The developed 3D-QSAR model resulted in great predictive ability with training set (r2 = 0.99, q2 = 0.55). Molecular docking studies were complementary to the 3D-QSAR analysis, showing that DSC was differently bound to CA-SP1 with higher affinity than that of bevirimat. MD studies revealed that the complex of the ligand and the protein was stable, with root mean square deviation (RMSD) values <2.5 Å. The above results provided valuable insights into the potential of DSC as a prototype to develop new antiviral agents.

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

Structural insights into conformational stability and binding of thiazolo-[2,3-b] quinazolinone derivatives with EGFR-TKD and in-vitro study

Heterocyclic molecules are well-known drugs against various diseases including cancer. Many tyrosine kinase inhibitors including erlotinib, osimertinib, and sunitinib were developed and approved but caused adverse effects among treated patients. Which prevents them from being used as cancer therapeutics. In this study, we strategically developed heterocyclic thiazolo-[2,3-b]quinazolinone derivatives by an organic synthesis approach. These synthesized molecules were assessed against the epidermal growth factor receptor tyrosine kinase domain (EGFR-TKD) by in silico methods. Molecular docking simulations unravel derivative 17 showed better binding energy scores and followed Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties. The binding affinity displayed by synthetic congener and reference molecule erlotinib was found to be -8.26 ± 0.0033 kcal/mol and -7.54 ± 0.1411 kcal/mol with the kinase domain. Further, molecular dynamic simulations were conducted thrice to validate the molecular docking study and achieved significant results. Both synthetic derivative and reference molecule attained stability in the active site of the TKD. The synthetic congener and erlotinib showed free energy binding (ΔGbind) -102.975 ± 3.714 kJ/mol and -130.378 ± 0.355 kJ/mol computed by Molecular Mechanics Poison Boltzmann Surface Area (MM-PBSA) method. In addition, the motions of each sampled system including the Apo complex were determined by the principal component analysis and Gibbs energy landscape analysis. The in-vitro apoptosis study was performed using MCF-7 and H-1299 cancer cell lines. However, thiazolo-[2,3-]-quinazoline derivative 17 showed fair anti-proliferative activity against MCF-7 and H-1299. Further, the in-vivo study is necessary to determine the effectivity of the potent anti-proliferative, non-toxic molecule against TKD.

Molecular Modeling and Simulation of the Peptidoglycan Layer of Gram-Positive Bacteria Staphylococcus aureus

The peptidoglycan (PG) layer is a vital component of the bacterial cell wall that protects the cell from rupturing due to internal pressure. Its ubiquity across the bacterial kingdom but not animals has made it the target of drug discovery efforts. The PG layer composed of cross-linked PG strands is porous enough to allow the diffusion of molecules through the PG mesh and into the cell. The lack of an accurate atomistic model of the PG mesh has limited the computational investigations of drug diffusion in Gram-positive bacteria, which lack the outer membrane but consist of a much thicker PG layer compared to Gram-negative bacteria. In this work, we built an atomistic model of the Staphylococcus aureus PG layer architecture with horizontally aligned PG strands and performed molecular dynamics simulations of the diffusion of curcumin molecules through the PG mesh. An accurate model of the Gram-positive bacterial cell wall may aid in developing novel antibiotics to tackle the threat posed by antibiotic resistance.

Modeling Polyzwitterion-Based Drug Delivery Platforms: A Perspective of the Current State-of-the-Art and Beyond

Drug delivery platforms are anticipated to have biocompatible and bioinert surfaces. PEGylation of drug carriers is the most approved method since it improves water solubility and colloid stability and decreases the drug vehicles’ interactions with blood components. Although this approach extends their biocompatibility, biorecognition mechanisms prevent them from biodistribution and thus efficient drug transfer. Recent studies have shown (poly)zwitterions to be alternatives for PEG with superior biocompatibility. (Poly)zwitterions are super hydrophilic, mainly stimuli-responsive, easy to functionalize and they display an extremely low protein adsorption and long biodistribution time. These unique characteristics make them already promising candidates as drug delivery carriers. Furthermore, since they have highly dense charged groups with opposite signs, (poly)zwitterions are intensely hydrated under physiological conditions. This exceptional hydration potential makes them ideal for the design of therapeutic vehicles with antifouling capability, i.e., preventing undesired sorption of biologics from the human body in the drug delivery vehicle. Therefore, (poly)zwitterionic materials have been broadly applied in stimuli-responsive “intelligent” drug delivery systems as well as tumor-targeting carriers because of their excellent biocompatibility, low cytotoxicity, insignificant immunogenicity, high stability, and long circulation time. To tailor (poly)zwitterionic drug vehicles, an interpretation of the structural and stimuli-responsive behavior of this type of polymer is essential. To this end, a direct study of molecular-level interactions, orientations, configurations, and physicochemical properties of (poly)zwitterions is required, which can be achieved via molecular modeling, which has become an influential tool for discovering new materials and understanding diverse material phenomena. As the essential bridge between science and engineering, molecular simulations enable the fundamental understanding of the encapsulation and release behavior of intelligent drug-loaded (poly)zwitterion nanoparticles and can help us to systematically design their next generations. When combined with experiments, modeling can make quantitative predictions. This perspective article aims to illustrate key recent developments in (poly)zwitterion-based drug delivery systems. We summarize how to use predictive multiscale molecular modeling techniques to successfully boost the development of intelligent multifunctional (poly)zwitterions-based systems.

Covalent docking and molecular dynamics simulations reveal the specificity-shifting mutations Ala237Arg and Ala237Lys in TEM beta-lactamase

The rate of modern drug discovery using experimental screening methods still lags behind the rate at which pathogens mutate, underscoring the need for fast and accurate predictive simulations of protein evolution. Multidrug-resistant bacteria evade our defenses by expressing a series of proteins, the most famous of which is the 29-kilodalton enzyme, TEM β-lactamase. Considering these challenges, we applied a covalent docking heuristic to measure the effects of all possible alanine 237 substitutions in TEM due to this codon’s importance for catalysis and effects on the binding affinities of commercially-available β-lactam compounds. In addition to the usual mutations that reduce substrate binding due to steric hindrance, we identified two distinctive specificity-shifting TEM mutations, Ala237Arg and Ala237Lys, and their respective modes of action. Notably, we discovered and verified through minimum inhibitory concentration assays that, while these mutations and their bulkier side chains lead to steric clashes that curtail ampicillin binding, these same groups foster salt bridges with the negatively-charged side-chain of the cephalosporin cefixime, widely used in the clinic to treat multi-resistant bacterial infections. To measure the stability of these unexpected interactions, we used molecular dynamics simulations and found the binding modes to be stable despite the application of biasing forces. Finally, we found that both TEM mutants also bind strongly to other drugs containing negatively-charged R-groups, such as carumonam and ceftibuten. As with cefixime, this increased binding affinity stems from a salt bridge between the compounds’ negative moieties and the positively-charged side chain of the arginine or lysine, suggesting a shared mechanism. In addition to reaffirming the power of using simulations as molecular microscopes, our results can guide the rational design of next-generation β-lactam antibiotics and bring the community closer to retaking the lead against the recurrent threat of multidrug-resistant pathogens.

Molecular modeling of lactoferrin for food and nutraceutical applications: insights from in silico techniques

Lactoferrin is a protein, primarily found in milk that has attracted the interest of the food industries due to its health properties. Nevertheless, the instability of lactoferrin has limited its commercial application. Recent studies have focused on encapsulation to enhance the stability of lactoferrin. However, the molecular insights underlying the changes of structural properties of lactoferrin and the interaction with protectants remain poorly understood. Computational approaches have proven useful in understanding the structural properties of molecules and the key binding with other constituents. In this review, comprehensive information on the structure and function of lactoferrin and the binding with various molecules for food purposes are reviewed, with a special emphasis on the use of molecular dynamics simulations. The results demonstrate the application of modeling and simulations to determine key residues of lactoferrin responsible for its stability and interactions with other biomolecular components under various conditions, which are also associated with its functional benefits. These have also been extended into the potential creation of enhanced lactoferrin for commercial purposes. This review provides valuable strategies in designing novel nutraceuticals for food science practitioners and those who have interests in acquiring familiarity with the application of computational modeling for food and health purposes.

Fundamental Aspects of Lipid-Based Excipients in Lipid-Based Product Development

Poor aqueous solubility of drugs is still a foremost challenge in pharmaceutical product development. The use of lipids in designing formulations provides an opportunity to enhance the aqueous solubility and consequently bioavailability of drugs. Pre-dissolution of drugs in lipids, surfactants, or mixtures of lipid excipients and surfactants eliminate the dissolution/dissolving step, which is likely to be the rate-limiting factor for oral absorption of poorly water-soluble drugs. In this review, we exhaustively summarize the lipids excipients in relation to their classification, absorption mechanisms, and lipid-based product development. Methodologies utilized for the preparation of solid and semi-solid lipid formulations, applications, phase behaviour, and regulatory perspective of lipid excipients are discussed.

Application of per-Residue Energy Decomposition to Design Peptide Inhibitors of PSD95 GK Domain

Specific interaction between the postsynaptic density protein 95 (PSD95) and synapse-associated protein 90/postsynaptic density 95–associated protein (SAPAP) is crucial for excitatory synaptic development and plasticity. Designing inhibitors that target the guanylate kinase (GK) domain of PSD95, which is responsible for the interaction, is a promising manipulation tool for the investigation of the function of PSD95 GK and the etiology of its related psychiatric disorders. Herein, we designed new peptide inhibitors of PSD95 GK/SAPAP with higher binding affinity by using molecular dynamics simulations. First, the interactions between PSD95 GK and their reported phosphorylated and unphosphorylated peptides were explored by molecular dynamics simulations. Besides the hydrogen bonding interactions mediated by the phospho-serine (p-Ser) or corresponding phosphomimic residue Asp/Glu, the hydrophobic interactions from the other amino acids also contribute to the PSD95 GK/SAPAP interaction. As an unphosphorylated synthetic peptide with moderate binding affinity and relatively lower molecular weight, the QSF inhibitory peptide was selected for further modification. Based on per-residue energy decomposition results of the PSD95 GK/QSF complex, ten peptides were designed to enhance the binding interactions, especially the hydrophobic interactions. The top-ranked five peptides with lower binding energy were eventually synthesized. The binding affinities of the synthesized peptides were determined using fluorescence polarization (FP) assay. As expected, all peptides have higher binding affinity than the QSF peptide (Ki = 5.64 ± 0.51 μM). Among them, F10W was the most potent inhibitor (Ki = 0.75 ± 0.25 μM), suggesting that enhancement of the hydrophobic interactions is an important strategy for the design of new inhibitory peptides targeting PSD95 GK.

Successes and challenges in multiscale modelling of artificial metalloenzymes: the case study of POP-Rh2 cyclopropanase

Molecular modelling applications in metalloenzyme design are still scarce due to a series of challenges. On top of that, the simulations of metal-mediated binding and the identification of catalytic competent geometries require both large conformational exploration and simulation of fine electronic properties. Here, we demonstrate how the incorporation of new tools in multiscale strategies, namely substrate diffusion exploration, allows taking a step further. As a showcase, the enantioselective profiles of the most outstanding variants of an artificial Rh₂-based cyclopropanase (GSH, HFF and RFY) developed by Lewis and co-workers (Nat. Commun., 2015, 6, 7789 and Nat. Chem., 2018, 10, 318-324) have been rationalized. DFT calculations on the free-cofactor-mediated process identify the carbene insertion and the cyclopropanoid formation as crucial events, the latter being the enantiodetermining step, which displays up to 8 competitive orientations easily altered by the protein environment. The key intermediates of the reaction were docked into the protein scaffold showing that some mutated residues have direct interaction with the cofactor and/or the co-substrate. These interactions take the form of a direct coordination of Rh in GSH and HFF and a strong hydrophobic patch with the carbene moiety in RFY. Posterior molecular dynamics sustain that the cofactor induces global re-arrangements of the protein. Finally, massive exploration of substrate diffusion, based on the GPathFinder approach, defines this event as the origin of the enantioselectivity in GSH and RFY. For HFF, fine molecular dockings suggest that it is likely related to local interactions upon diffusion. This work shows how modelling of long-range mutations on the catalytic profiles of metalloenzymes may be unavoidable and software simulating substrate diffusion should be applied.

Recent progress in general force fields of small molecules

Recent advances in computational hardware and free energy algorithms enable a broader application of molecular simulation of binding interactions between receptors and small-molecule ligands. The underlying molecular mechanics force fields (FFs) for small molecules have also achieved advancements in accuracy, user-friendliness, and speed during the past several years (2018-2020). Besides the expansion of chemical space coverage of ligand-like molecules among major popular classical additive FFs and polarizable FFs, new charge models have been proposed for better accuracy and transferability, new chemical perception of avoiding predefined atom types have been applied, and new automated parameterization toolkits, including machine learning approaches, have been developed for users' convenience.

Drug Repurposing Targeting Pseudomonas aeruginosa MvfR Using Docking, Virtual Screening, Molecular Dynamics, and Free-Energy Calculations

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium responsible for acute and chronic infections in planktonic state or in biofilms. The sessile structures are known to confer physical stability, increase virulence, and work as a protective armor against antimicrobial compounds. P. aeruginosa can control the expression of genes, population density, and biofilm formation through a process called quorum sensing (QS), a rather complex and hierarchical system of communication. A recent strategy to try and overcome bacterial resistance is to target QS proteins. In this study, a combined multi-level computational approach was applied to find possible inhibitors against P. aeruginosa QS regulator protein MvfR, also known as PqsR, using a database of approved FDA drugs, as a repurposing strategy. Fifteen compounds were identified as highly promising putative MvfR inhibitors. On those 15 MvfR ligand complexes, molecular dynamic simulations and MM/GBSA free-energy calculations were performed to confirm the docking predictions and elucidate on the mode of interaction. Ultimately, the five compounds that presented better binding free energies of association than the reference molecules (a known antagonist, M64 and a natural inducer, 2-nonyl-4-hydroxyquinoline) were highlighted as very promising MvfR inhibitors.

Computational Approach to Combat COVID-19 Infection: Emerging Tool for Accelerating Drug Research

BACKGROUND

Drug discovery and development process is an expensive, complex, time-consuming and risky. There are different techniques involved in the drug development process which include random screening, computational approach, molecular manipulation and serendipitous research. Among these methods, the computational approach is considered as an efficient strategy to accelerate and economize the drug discovery process.

OBJECTIVE

This approach is mainly applied in various phases of drug discovery process including target identification, target validation, lead identification and lead optimization. Due to increase in the availability of information regarding various biological targets of different disease states, computational approaches such as molecular docking, de novo design, molecular similarity calculation, virtual screening, pharmacophore-based modeling and pharmacophore mapping have been applied extensively.

METHODS

Various drug molecules can be designed by applying computational tools to explore the drug candidates for treatment of Coronavirus infection. The world health organization has announced the novel corona virus disease as COVID-19 and declared it as pandemic globally on 11 February 2020. So, it is thought of interest to scientific community to apply computational methods to design and optimize the pharmacological properties of various clinically available and FDA approved drugs such as remdesivir, ribavirin, favipiravir, oseltamivir, ritonavir, arbidol, chloroquine, hydroxychloroquine, carfilzomib, baraticinib, prulifloxacin, etc for effective treatment of COVID-19 infection.

RESULTS

Further, various survey reports suggest that the extensive studies are carried out by various research communities to find out the safety and efficacy profile of these drug candidates.

CONCLUSION

This review is focused on the study of various aspects of these drugs related to their target sites on virus, binding interactions, physicochemical properties etc.

In Silico Studies of Tumor Targeted Peptide-Conjugated Natural Products for Targeting Over-Expressed Receptors in Breast Cancer Cells Using Molecular Docking, Molecular Dynamics and MMGBSA Calculations

In this work, in silico studies were carried out for the design of diterpene and polyphenol-peptide conjugates to potentially target over-expressed breast tumor cell receptors. Four point mutations were induced into the known tumor-targeting peptide sequence YHWYGYTPQN at positions 1, 2, 8 and 10, resulting in four mutated peptides. Each peptide was separately conjugated with either chlorogenate, carnosate, gallate, or rosmarinate given their known anti-tumor activities, creating dual targeting compounds. Molecular docking studies were conducted with the epidermal growth factor receptor (EGFR), to which the original peptide sequence is known to bind, as well as the estrogen receptor (ERα) and peroxisome proliferator-activated receptor (PPARα) using both Autodock Vina and FireDock. Based on docking results, peptide conjugates and peptides were selected and subjected to molecular dynamics simulations. MMGBSA calculations were used to further probe the binding energies. ADME studies revealed that the compounds were not CYP substrates, though most were Pgp substrates. Additionally, most of the peptides and conjugates showed MDCK permeability. Our results indicated that several of the peptide conjugates enhanced binding interactions with the receptors and resulted in stable receptor-ligand complexes; Furthermore, they may successfully target ERα and PPARα in addition to EGFR and may be further explored for synthesis and biological studies for therapeutic applications.

Molecular Dynamic Simulations and Molecular Docking as a Potential Way for Designed New Inhibitor Drug without Resistance

Mycobacterium tuberculosis is the cause of tuberculosis in humans and is responsible for more than 2 million deaths per year. Despite the development of anti-tuberculosis drugs (Isoniazid, Rifampicin, Ethambutol, pyrazinamide, streptomycin, etc.) and the TB vaccine, this disease has claimed the lives of many people around the world. Drug resistance in this disease is increasing day by day. Conventional methods for discovering and developing drugs are usually time-consuming and expensive. Therefore, a better method is needed to identify, design, and manufacture TB drugs without drug resistance. Bioinformatics applications in obtaining new drugs at the structural level include studies of the mechanism of drug resistance, detection of drug interactions, and prediction of mutant protein structure. In the present study, computer-based approaches including molecular dynamics simulation and molecular docking as a novel and efficient method for the identification and investigation of new cases as well as the investigation of mutated proteins and compounds will be examined .

In silico identification of novel allosteric inhibitors of dengue virus NS2B/NS3 serine protease

Although dengue is a disease that affects more than 100 countries and puts almost 400 million lives at risk each year, there is no approved antiviral in the treatment of this pathology. In this context, proteases are potential biological targets since they are essential in the replication process of this virus. In this study, a library of more than 3,000 structures was used to explore the allosteric inhibition of the NS2B/NS3 protease complex using Consensual Docking techniques. The results show four best ranked structures that were selected for molecular dynamics and free energy simulations. Our analysis corroborate with other studies (experimental and theoretical) presented in the literature. Thus, the computational approach used here proved to be useful for planning new inhibitors in the combat against Dengue disease.

Generation of Quantum Configurational Ensembles Using Approximate Potentials

Conformational analysis is of paramount importance in drug design: it is crucial to determine pharmacological properties, understand molecular recognition processes, and characterize the conformations of ligands when unbound. Molecular Mechanics (MM) simulation methods, such as Monte Carlo (MC) and molecular dynamics (MD), are usually employed to generate ensembles of structures due to their ability to extensively sample the conformational space of molecules. The accuracy of these MM-based schemes strongly depends on the functional form of the force field (FF) and its parametrization, components that often hinder their performance. High-level methods, such as ab initio MD, provide reliable structural information but are still too computationally expensive to allow for extensive sampling. Therefore, to overcome these limitations, we present a multilevel MC method that is capable of generating quantum configurational ensembles while keeping the computational cost at a minimum. We show that FF reparametrization is an efficient route to generate FFs that reproduce QM results more closely, which, in turn, can be used as low-cost models to achieve the gold standard QM accuracy. We demonstrate that the MC acceptance rate is strongly correlated with various phase space overlap measurements and that it constitutes a robust metric to evaluate the similarity between the MM and QM levels of theory. As a more advanced application, we present a self-parametrizing version of the algorithm, which combines sampling and FF parametrization in one scheme, and apply the methodology to generate the QM/MM distribution of a ligand in aqueous solution.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

Hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) Simulation: A Tool for Structure-based Drug Design and Discovery

Quantum mechanics (QM) is physics based theory which explains the physical properties of nature at the level of atoms and sub-atoms. Molecular mechanics (MM) construct molecular systems through the use of classical mechanics. So, hybrid quantum mechanics and molecular mechanics (QM/MM) when combined together can act as computer-based methods which can be used to calculate structure and property data of molecular structures. Hybrid QM/MM combines the strengths of QM with accuracy and MM with speed. QM/MM simulation can also be applied for the study of chemical process in solutions as well as in the proteins, and has a great scope in structure-based drug design (CADD) and discovery. Hybrid QM/MM also applied to HTS, to derive QSAR models and due to availability of many protein crystal structures; it has a great role in computational chemistry, especially in structure- and fragment-based drug design. Fused QM/MM simulations have been developed as a widespread method to explore chemical reactions in condensed phases. In QM/MM simulations, the quantum chemistry theory is used to treat the space in which the chemical reactions occur; however the rest is defined through molecular mechanics force field (MMFF). In this review, we have extensively reviewed recent literature pertaining to the use and applications of hybrid QM/MM simulations for ligand and structure-based computational methods for the design and discovery of therapeutic agents.

Molecular dynamics: a powerful tool for studying the medicinal chemistry of ion channel modulators

Molecular dynamics (MD) simulations allow researchers to investigate the behavior of desired biological targets at ever-decreasing costs with ever-increasing precision. Among the biological macromolecules, ion channels are remarkable transmembrane proteins, capable of performing special biological processes and revealing a complex regulatory matrix, including modulation by small molecules, either endogenous or exogenous. Recently, given the developments in ion channel structure determination and accessibility of bio-computational techniques, MD and related tools are becoming increasingly popular in the intense research area regarding ligand-channel interactions. This review synthesizes and presents the most important fields of MD involvement in investigating channel-molecule interactions, including, but not limited to, deciphering the binding modes of ligands to their ion channel targets and the mechanisms through which chemical compounds exert their effect on channel function. Special attention is devoted to the importance of more elaborate methods, such as free energy calculations, while principles regarding drug design and discovery are highlighted. Several technical aspects involving the creation and simulation of channel-molecule MD systems (ligand parameterization, proper membrane setup, system building, etc.) are also presented.

Investigation of Pharmaceutical Importance of 2H-Pyran-2-One Analogues via Computational Approaches

Highly functionalized spirocyclic ketals were synthesized through asymmetric oxidative spirocyclization via carbanion-induced ring transformation of 2H-pyran-2-ones with 1,4-cyclohexandione monoethyleneketal under alkaline conditions. Further acidic-hydrolysis of obtained spirocyclic ketals yields highly substituted 2-tetralone in good yield. Computational analysis based on the DFT calculations and MD simulations has been performed in order to predict and understand global and local reactivity properties of newly synthesized derivatives. DFT calculations covered fundamental reactivity descriptors such as molecular electrostatic potential and average local ionization energies. Nitrogen atom and benzene rings have been recognized as the most important molecular sites from these aspects. Additionally, to predict whether studied compounds are stable towards the autoxidation mechanism, we have also studied the bond dissociation energies for hydrogen abstraction and identified the derivative which might form potentially genotoxic impurities. Interactions with water, including both global and local aspects, have been covered thanks to the MD simulations and calculations of interaction energies with water, counting of formed hydrogen interactions, and radial distribution functions. MD simulations were also used to identify which excipient could be used together with these compounds, and it has been established that the polyvinylpyrrolidone polymer could be highly compatible with these compounds, from the aspect of calculated solubility parameters.

AENET-LAMMPS and AENET-TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet-TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet-LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed-memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.

Enzyme Models-From Catalysis to Prodrugs

Enzymes are highly specific biological catalysts that accelerate the rate of chemical reactions within the cell. Our knowledge of how enzymes work remains incomplete. Computational methodologies such as molecular mechanics (MM) and quantum mechanical (QM) methods play an important role in elucidating the detailed mechanisms of enzymatic reactions where experimental research measurements are not possible. Theories invoked by a variety of scientists indicate that enzymes work as structural scaffolds that serve to bring together and orient the reactants so that the reaction can proceed with minimum energy. Enzyme models can be utilized for mimicking enzyme catalysis and the development of novel prodrugs. Prodrugs are used to enhance the pharmacokinetics of drugs; classical prodrug approaches focus on alternating the physicochemical properties, while chemical modern approaches are based on the knowledge gained from the chemistry of enzyme models and correlations between experimental and calculated rate values of intramolecular processes (enzyme models). A large number of prodrugs have been designed and developed to improve the effectiveness and pharmacokinetics of commonly used drugs, such as anti-Parkinson (dopamine), antiviral (acyclovir), antimalarial (atovaquone), anticancer (azanucleosides), antifibrinolytic (tranexamic acid), antihyperlipidemia (statins), vasoconstrictors (phenylephrine), antihypertension (atenolol), antibacterial agents (amoxicillin, cephalexin, and cefuroxime axetil), paracetamol, and guaifenesin. This article describes the works done on enzyme models and the computational methods used to understand enzyme catalysis and to help in the development of efficient prodrugs.

Combined Use of Structure Analysis, Studies of Molecular Association in Solution, and Molecular Modelling to Understand the Different Propensities of Dihydroxybenzoic Acids to Form Solid Phases

The arrangement of hydroxyl groups in the benzene ring has a significant effect on the propensity of dihydroxybenzoic acids (diOHBAs) to form different solid phases when crystallized from solution. All six diOHBAs were categorized into distinctive groups according to the solid phases obtained when crystallized from selected solvents. A combined study using crystal structure and molecule electrostatic potential surface analysis, as well as an exploration of molecular association in solution using spectroscopic methods and molecular dynamics simulations were used to determine the possible mechanism of how the location of the phenolic hydroxyl groups affect the diversity of solid phases formed by the diOHBAs. The crystal structure analysis showed that classical carboxylic acid homodimers and ring-like hydrogen bond motifs consisting of six diOHBA molecules are prominently present in almost all analyzed crystal structures. Both experimental spectroscopic investigations and molecular dynamics simulations indicated that the extent of intramolecular bonding between carboxyl and hydroxyl groups in solution has the most significant impact on the solid phases formed by the diOHBAs. Additionally, the extent of hydrogen bonding with solvent molecules and the mean lifetime of solute-solvent associates formed by diOHBAs and 2-propanol were also investigated.

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in the use of machine learning (ML) methods for accelerated simulations based on a quantum mechanical (QM) description of the system. We show how recent progress in ML methods has dramatically extended the applicability range of conventional QM-based simulations, allowing to calculate industrially relevant properties with enhanced accuracy, at reduced computational cost, and for length and time scales that would have otherwise not been accessible. We illustrate the benefits of ML-accelerated atomistic simulations for industrial R&D processes by showcasing relevant applications from two very different areas, drug discovery (pharmaceuticals) and energy materials. Writing from the perspective of both a molecular and a materials modeling scientist, this review aims to provide a unified picture of the impact of ML-accelerated atomistic simulations on the pharmaceutical, chemical, and materials industries and gives an outlook on the exciting opportunities that could emerge in the future.

Molecular Dynamics for Antimicrobial Peptide Discovery

Although antimicrobial resistance is an increasingly significant public health concern, there have only been two new classes of antibiotics approved for human use since the 1960s. Understanding the mechanisms of action of antibiotics is critical for novel antibiotic discovery, but novel approaches are needed that do not exclusively rely on experiments. Molecular dynamics simulation is a computational tool that uses simple models of the atoms in a system to discover nanoscale insights into the dynamic relationship between mechanism and biological function. Such insights can lay the framework for elucidating the mechanism of action and optimizing antibiotic templates. Antimicrobial peptides represent a promising solution to escalating antimicrobial resistance, given their lesser tendency to induce resistance than that of small-molecule antibiotics. Simulations of these agents have already revealed how they interact with bacterial membranes and the underlying physiochemical features directing their structure and function. In this minireview, we discuss how traditional molecular dynamics simulation works and its role and potential for the development of new antibiotic candidates with an emphasis on antimicrobial peptides.

New atoms-in-molecules dispersion models for use in ab initio derived force fields

Recently, substantial research efforts have gone into bridging the accuracy-efficiency gap between parameterized force field models and quantum chemical calculations by extracting molecule-specific force fields directly from ab initio data in a robust and automated manner. One of the challenging aspects is deriving localized atomic polarizabilities for pairwise distributed dispersion models. The Tkatchenko-Scheffler model is based upon correcting free-atom C₆ coefficients according to the square of the ratio of the atom-in-molecule volume to the free-atom volume. However, it has recently been shown that a more accurate relationship can be found if static atomic polarizabilities are also taken into account. Using this relationship, we develop two modified Tkatchenko-Scheffler dispersion models and benchmark their performance against SAPT2+3 reference data and other commonly used dispersion models.

Computational avenues in oral protein and peptide therapeutics

Proteins and peptides are amongst the most sought-after biomolecules because of their exceptional potential to cater to a vast range of diseases. Although widely studied and researched, the oral delivery of these biomolecules remains a challenge. Alongside formulation strategies, approaches to overcome the inherent barriers for peptide absorption are being designed at the molecular level to establish a sound rationale and to achieve higher bioavailability. Computer-aided drug design (CADD) is a modern in silico approach for developing successful bio-formulations. CADD enables intricate study of the biomolecules in conjunction with their target sites or receptors at the molecular level. Knowledge of the molecular interactions of proteins and peptides makes way for the pre-screening of suitable formulation components and facilitates their delivery.

Discovery of novel heterocyclic amide-based inhibitors: an integrative in-silico approach to targeting soluble epoxide hydrolase

Inhibition of soluble epoxide hydrolase (sEH) is considered as an emerging druggable target to reduce blood pressure, improve insulin sensitivity, and decrease inflammation. Despite the availability of different classes of sEH small molecule inhibitors for the potential treatment of hypertension, only a few candidates have reached clinical trials, making the optimal control of blood pressure presently unattainable. This necessity motivated us to explore a series of novel quinazoline-4(3H)-one and 4,6-disubstituted pyridin-2(1H)-one derivatives targeting sEH enzyme. Herein, comprehensive computational investigations were performed to probe the inhibition efficacy of these potent compounds in terms of inhibitor-enzyme interactions against sEH. In this study, the 39 in-house with a focused library comprising 39 in-house synthesized compounds were selected. The structure-based pharmacophore modeling was developed based on the crystal structure of sEH with its co-crystallized biologically active inhibitor. The generated hypotheses were applied for virtual screening-based PHASE fitness scores. Docking-based virtual screening workflows were used to generate lead compounds using HTVS, SP and XP based GLIDE G-score values. The candidate leads were filtered using ADMET pharmacological and physicochemical properties screening. A 100-ns of molecular dynamics simulations with Molecular dynamics simulations (100 ns) were performed to explore the binding affinities of the considered compounds. Our study identified four best candidates from quinazoline-4(3H)-one derivatives, which indicated that a quinazolinone ring serves as a suitable scaffold to develop novel small molecule sEH inhibitors.

Determination of drugs and drug metabolites by ion mobility-mass spectrometry: A review

Ion mobility-mass spectrometry (IM-MS) has gained increased applications in the characterization and identification of drugs and drug metabolites, largely owning to the complementary separation of analyte ions based on their gas-phase size and shape in the IM dimension in addition to their mass-to-charge ratios. In this review, we discuss recent advances in such applications. We first introduce various types of IM techniques, focusing on those that allow the measurement of collision cross section (CCS), the physical property of an ion that reflects its gas-phase size and shape. Next, we discuss the IM-MS landscape of the large chemical space of drugs and multimodal distributions of certain drugs in IM separation due to the presence of protomers. We then review drug metabolism reactions and discuss the application of IM-MS in separation and identification of isomeric drug metabolites. Subsequently, we discuss various approaches to generate theoretical and predicted CCS data, including theory-based calculation methods and data-driven prediction models, and currently available resources on these approaches. Finally, current limitations and future directions of application of IM-MS are discussed.

Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review

BACKGROUND

Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in finding a potential therapeutic agent for the disease. Considering the matter of time, the computational methods of drug repurposing offer the best chance of selecting one drug from a list of approved drugs for the life-threatening condition of COVID-19. The present systematic review aims to provide an overview of studies that have used computational methods for drug repurposing in COVID-19.

METHODS

We undertook a systematic search in five databases and included original articles in English that applied computational methods for drug repurposing in COVID-19.

RESULTS

Twenty-one original articles utilizing computational drug methods for COVID-19 drug repurposing were included in the systematic review. Regarding the quality of eligible studies, high-quality items including the use of two or more approved drug databases, analysis of molecular dynamic simulation, multi-target assessment, the use of crystal structure for the generation of the target sequence, and the use of AutoDock Vina combined with other docking tools occurred in about 52%, 38%, 24%, 48%, and 19% of included studies. Studies included repurposed drugs mainly against non-structural proteins of SARS-CoV2: the main 3C-like protease (Lopinavir, Ritonavir, Indinavir, Atazanavir, Nelfinavir, and Clocortolone), RNA-dependent RNA polymerase (Remdesivir and Ribavirin), and the papain-like protease (Mycophenolic acid, Telaprevir, Boceprevir, Grazoprevir, Darunavir, Chloroquine, and Formoterol). The review revealed the best-documented multi-target drugs repurposed by computational methods for COVID-19 therapy as follows: antiviral drugs commonly used to treat AIDS/HIV (Atazanavir, Efavirenz, and Dolutegravir Ritonavir, Raltegravir, and Darunavir, Lopinavir, Saquinavir, Nelfinavir, and Indinavir), HCV (Grazoprevir, Lomibuvir, Asunaprevir, Ribavirin, and Simeprevir), HBV (Entecavir), HSV (Penciclovir), CMV (Ganciclovir), and Ebola (Remdesivir), anticoagulant drug (Dabigatran), and an antifungal drug (Itraconazole).

CONCLUSIONS

The present systematic review provides a list of existing drugs that have the potential to influence SARS-CoV2 through different mechanisms of action. For the majority of these drugs, direct clinical evidence on their efficacy for the treatment of COVID-19 is lacking. Future clinical studies examining these drugs might come to conclude, which can be more useful to inhibit COVID-19 progression.

On the Use of the Discrete Constant pH Molecular Dynamics to Describe the Conformational Space of Peptides

Solvent pH is an important property that defines the protonation state of the amino acids and, therefore, modulates the interactions and the conformational space of the biochemical systems. Generally, this thermodynamic variable is poorly considered in Molecular Dynamics (MD) simulations. Fortunately, this lack has been overcome by means of the Constant pH Molecular Dynamics (CPHMD) methods in the recent decades. Several studies have reported promising results from these approaches that include pH in simulations but focus on the prediction of the effective pKa of the amino acids. In this work, we want to shed some light on the CPHMD method and its implementation in the AMBER suitcase from a conformational point of view. To achieve this goal, we performed CPHMD and conventional MD (CMD) simulations of six protonatable amino acids in a blocked tripeptide structure to compare the conformational sampling and energy distributions of both methods. The results reveal strengths and weaknesses of the CPHMD method in the implementation of AMBER18 version. The change of the protonation state according to the chemical environment is presumably an improvement in the accuracy of the simulations. However, the simulations of the deprotonated forms are not consistent, which is related to an inaccurate assignment of the partial charges of the backbone atoms in the CPHMD residues. Therefore, we recommend the CPHMD methods of AMBER program but pointing out the need to compare structural properties with experimental data to bring reliability to the conformational sampling of the simulations.

The Current Landscape of Novel Formulations and the Role of Mathematical Modeling in Their Development

Drug delivery is an integral part of the drug development process, influencing safety and efficacy of active pharmaceutical ingredients. The application of nanotechnology has enabled the discovery of novel formulations for numerous therapeutic purposes across multiple disease areas. However, evaluation of novel formulations in clinical scenarios is slow and hampered due to various ethical and logistical barriers. Computational models have the ability to integrate existing domain knowledge and mathematical correlations, to rationalize the feasibility of using novel formulations for safely enhancing drug delivery, identifying suitable candidates, and reducing the burden on preclinical and clinical studies. In this review, types of novel formulations and their application through several routes of administration and the use of modeling approaches that can find application in different stages of the novel formulation development process are discussed.