Insights from incorporating quantum computing into drug design workflows.

MOTIVATION: While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. RESULTS: We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the coronavirus (SARS-CoV-2) protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. AVAILABILITY AND IMPLEMENTATION: Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Structure and Computational Studies of New Sulfonamide Compound: {(4-nitrophenyl)sulfonyl}tryptophan.

Synthesis of sulfonamide through an indirect method that avoids contamination of the product with no need for purification has been carried out using the indirect process. Here, we report the synthesis of a novel sulfonamide compound, ({4-nitrophenyl}sulfonyl)tryptophan (DNSPA) from 4-nitrobenzenesulphonylchloride and L-tryptophan precursors. The slow evaporation method was used to form single crystals of the named compound from methanolic solution. The compound was characterized by X-ray crystallographic analysis and spectroscopic methods (NMR, IR, mass spectrometry, and UV-vis). The sulfonamide N-H NMR signal at 8.07-8.09 ppm and S-N stretching vibration at 931 cm-1 indicate the formation of the target compound. The compound crystallized in the monoclinic crystal system and P21 space group with four molecules of the compound in the asymmetric unit. Molecular aggregation in the crystal structure revealed a 12-molecule aggregate synthon sustained by O-H⋯O hydrogen bonds and stabilised by N-H⋯O intermolecular contacts. Experimental studies were complemented by DFT calculations at the B3LYP/6-311++G(d,p) level of theory. The computed structural and spectroscopic data are in good agreement with those obtained experimentally. The energies of interactions between the units making up the molecule were calculated. Molecular docking studies showed that DNSPA has a binding energy of -6.37 kcal/mol for E. coli DNA gyrase (5MMN) and -6.35 kcal/mol for COVID-19 main protease (6LU7).

Electronic Structures and Reactivities of COVID-19 Drugs: A DFT Study.

These days, the world is facing the threat of pandemic Coronavirus Disease 2019 (COVID-19). Although a vaccine has been found to combat the pandemic, it is essential to find drugs for an effective treatment method against this disease as soon as possible. In this study, electronic and thermodynamic properties, molecular electrostatic potential (MEP) analysis, and frontier molecular orbitals (FMOs) of nine different covid drugs were studied with Density Functional Theory (DFT). In addition, the relationship between the electronic structures of these drugs and their biological effectiveness was examined. All parameters were computed at the B3LYP/6-311++g(d,p) level. The Solvent effect was evaluated using conductor-like polarizable continuum model (CPCM) as the solvation model. It was observed that electrophilic indexes were important to understand the efficiencies of these drugs in COVID-19 disease. Paxlovid, hydroxyquinone, and nitazoxanide were found as the most thermodynamically stable molecules. Thermodynamic parameters also demonstrated that these drugs were more stable in the aqueous media. Global descriptors and the reactivity of these drugs were found to be related. Nitazoxanide molecule exhibited the highest dipole moment. The high dipole moments of drugs can cause hydrophilic interactions that increase their effectiveness in an aqueous solution.

Wave-like behaviour in (0,1) binary sequences.

A comprehensive study of the properties of finite (0,1) binary systems from the mathematical viewpoint of quantum theory is presented. This is a quantum-inspired extension of the GenomeBits model to characterize observed genome sequences, where a complex wavefunction [Formula: see text] is considered as an analogous probability measure and it is related to an alternating (0,1) binary series having independent distributed terms. The real and imaginary spectrum of [Formula: see text] vs. the nucleotide base positions display characteristic features of sound waves. This approach represents a novel perspective for identifying and "observing" emergent properties of genome sequences in the form of wavefunctions via superposition states. The motivation is to develop a simple algorithm to perform wave calculations from binary sequences and to apply these wave functions to sonification.

A QUBO formulation for top-τ eigencentrality nodes.

The efficient calculation of the centrality or "hierarchy" of nodes in a network has gained great relevance in recent years due to the generation of large amounts of data. The eigenvector centrality (aka eigencentrality) is quickly becoming a good metric for centrality due to both its simplicity and fidelity. In this work we lay the foundations for solving the eigencentrality problem of ranking the importance of the nodes of a network with scores from the eigenvector of the network, using quantum computational paradigms such as quantum annealing and gate-based quantum computing. The problem is reformulated as a quadratic unconstrained binary optimization (QUBO) that can be solved on both quantum architectures. The results focus on correctly identifying a given number of the most important nodes in numerous networks given by the sparse vector solution of our QUBO formulation of the problem of identifying the top-τ highest eigencentrality nodes in a network on both the D-Wave and IBM quantum computers.

How Reproducible Are QM/MM Simulations? Lessons from Computational Studies of the Covalent Inhibition of the SARS-CoV-2 Main Protease by Carmofur.

This work explores the level of transparency in reporting the details of computational protocols that is required for practical reproducibility of quantum mechanics/molecular mechanics (QM/MM) simulations. Using the reaction of an essential SARS-CoV-2 enzyme (the main protease) with a covalent inhibitor (carmofur) as a test case of chemical reactions in biomolecules, we carried out QM/MM calculations to determine the structures and energies of the reactants, the product, and the transition state/intermediate using analogous QM/MM models implemented in two software packages, NWChem and Q-Chem. Our main benchmarking goal was to reproduce the key energetics computed with the two packages. Our results indicate that quantitative agreement (within the numerical thresholds used in calculations) is difficult to achieve. We show that rather minor details of QM/MM simulations must be reported in order to ensure the reproducibility of the results and offer suggestions toward developing practical guidelines for reporting the results of biosimulations.

Metal oxide nanocage as drug delivery systems for Favipiravir, as an effective drug for the treatment of COVID-19: a computational study.

This paper is a summary of research that looks at the potential of fullerene-like (MO)12 nanoclusters (NCs) in drug-carrying systems using density functional theory. Favipiravir/Zn12O12 (- 34.80 kcal/mol), Favipiravir/Mg12O12 (- 34.98 kcal/mol), and Favipiravir/Be12O12 (- 30.22 kcal/mol) were rated in order of drug adsorption degrees. As a result, Favipiravir attachment to (MgO)12 and (ZnO)12 might be simple, increasing Favipiravir loading efficiency. In addition, the quantum theory of atoms in molecules (QTAIM) assessment was utilized to look at the interactions between molecules. The FMO, ESP, NBO, and Eads reactivity patterns were shown to be in excellent agreement with the QTAIM data. The electrostatic properties of the system with the biggest positive charge on the M atom and the largest Eads were shown to be the best. This system was shown to be the best attraction site for nucleophilic agents. The findings show that (MgO)12 and (ZnO)12 have great carrier potential and may be used in medication delivery.

Exploring the Catalytic Mechanism of the RNA Cap Modification by nsp16-nsp10 Complex of SARS-CoV-2 through a QM/MM Approach.

The inhibition of key enzymes that may contain the viral replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have assumed central importance in drug discovery projects. Nonstructural proteins (nsps) are essential for RNA capping and coronavirus replication since it protects the virus from host innate immune restriction. In particular, nonstructural protein 16 (nsp16) in complex with nsp10 is a Cap-0 binding enzyme. The heterodimer formed by nsp16-nsp10 methylates the 5'-end of virally encoded mRNAs to mimic cellular mRNAs and thus it is one of the enzymes that is a potential target for antiviral therapy. In this study, we have evaluated the mechanism of the 2'-O methylation of the viral mRNA cap using hybrid quantum mechanics/molecular mechanics (QM/MM) approach. It was found that the calculated free energy barriers obtained at M062X/6-31+G(d,p) is in agreement with experimental observations. Overall, we provide a detailed molecular analysis of the catalytic mechanism involving the 2'-O methylation of the viral mRNA cap and, as expected, the results demonstrate that the TS stabilization is critical for the catalysis.

Organic Photo-antimicrobials: Principles, Molecule Design, and Applications.

The emergence of multi-drug-resistant pathogens threatens the healthcare systems world-wide. Recent advances in phototherapy (PT) approaches mediated by photo-antimicrobials (PAMs) provide new opportunities for the current serious antibiotic resistance. During the PT treatment, reactive oxygen species or heat produced by PAMs would react with the cell membrane, consequently leaking cytoplasm components and effectively eradicating different pathogens like bacteria, fungi, viruses, and even parasites. This Perspective will concentrate on the development of different organic photo-antimicrobials (OPAMs) and their application as practical therapeutic agents into therapy for local infections, wound dressings, and removal of biofilms from medical devices. We also discuss how to design highly efficient OPAMs by modifying the chemical structure or conjugating with a targeting component. Moreover, this Perspective provides a discussion of the general challenges and direction for OPAMs and what further needs to be done. It is hoped that through this overview, OPAMs can prosper and will be more widely used for microbial infections in the future, especially at a time when the global COVID-19 epidemic is getting more serious.

Quantum simulations of SARS-CoV-2 main protease Mpro enable high-quality scoring of diverse ligands.

The COVID-19 pandemic has led to unprecedented efforts to identify drugs that can reduce its associated morbidity/mortality rate. Computational chemistry approaches hold the potential for triaging potential candidates far more quickly than their experimental counterparts. These methods have been widely used to search for small molecules that can inhibit critical proteins involved in the SARS-CoV-2 replication cycle. An important target is the SARS-CoV-2 main protease Mpro, an enzyme that cleaves the viral polyproteins into individual proteins required for viral replication and transcription. Unfortunately, standard computational screening methods face difficulties in ranking diverse ligands to a receptor due to disparate ligand scaffolds and varying charge states. Here, we describe full density functional quantum mechanical (DFT) simulations of Mpro in complex with various ligands to obtain absolute ligand binding energies. Our calculations are enabled by a new cloud-native parallel DFT implementation running on computational resources from Amazon Web Services (AWS). The results we obtain are promising: the approach is quite capable of scoring a very diverse set of existing drug compounds for their affinities to M pro and suggest the DFT approach is potentially more broadly applicable to repurpose screening against this target. In addition, each DFT simulation required only ~ 1 h (wall clock time) per ligand. The fast turnaround time raises the practical possibility of a broad application of large-scale quantum mechanics in the drug discovery pipeline at stages where ligand diversity is essential.

Surface Engineering of Graphene through Heterobifunctional Supramolecular-Covalent Scaffolds for Rapid COVID-19 Biomarker Detection.

Graphene is a two-dimensional semiconducting material whose application for diagnostics has been a real game-changer in terms of sensitivity and response time, variables of paramount importance to stop the COVID-19 spreading. Nevertheless, strategies for the modification of docking recognition and antifouling elements to obtain covalent-like stability without the disruption of the graphene band structure are still needed. In this work, we conducted surface engineering of graphene through heterofunctional supramolecular-covalent scaffolds based on vinylsulfonated-polyamines (PA-VS). In these scaffolds, one side binds graphene through multivalent π-π interactions with pyrene groups, and the other side presents vinylsulfonated pending groups that can be used for covalent binding. The construction of PA-VS scaffolds was demonstrated by spectroscopic ellipsometry, Raman spectroscopy, and contact angle measurements. The covalent binding of -SH, -NH2, or -OH groups was confirmed, and it evidenced great chemical versatility. After field-effect studies, we found that the PA-VS-based scaffolds do not disrupt the semiconducting properties of graphene. Moreover, the scaffolds were covalently modified with poly(ethylene glycol) (PEG), which improved the resistance to nonspecific proteins by almost 7-fold compared to the widely used PEG-monopyrene approach. The attachment of recognition elements to PA-VS was optimized for concanavalin A (ConA), a model lectin with a high affinity to glycans. Lastly, the platform was implemented for the rapid, sensitive, and regenerable recognition of SARS-CoV-2 spike protein and human ferritin in lab-made samples. Those two are the target molecules of major importance for the rapid detection and monitoring of COVID-19-positive patients. For that purpose, monoclonal antibodies (mAbs) were bound to the scaffolds, resulting in a surface coverage of 436 ± 30 ng/cm2. KD affinity constants of 48.4 and 2.54 nM were obtained by surface plasmon resonance (SPR) spectroscopy for SARS-CoV-2 spike protein and human ferritin binding on these supramolecular scaffolds, respectively.

Intermolecular Interaction Analyses on SARS-CoV-2 Spike Protein Receptor Binding Domain and Human Angiotensin-Converting Enzyme 2 Receptor-Blocking Antibody/Peptide Using Fragment Molecular Orbital Calculation.

The spike glycoprotein (S-protein) mediates SARS-CoV-2 entry via intermolecular interaction with human angiotensin-converting enzyme 2. The receptor binding domain (RBD) of the S-protein has been considered critical for this interaction and acts as the target of numerous neutralizing antibodies and antiviral peptides. This study used the fragment molecular orbital method to analyze the interactions between the RBD and antibodies/peptides and extracted crucial residues that can be used as epitopes. The interactions evaluated as interfragment interaction energy values between the RBD and 12 antibodies/peptides showed a fairly good correlation with the experimental activity pIC50 (R2 = 0.540). Nine residues (T415, K417, Y421, F456, A475, F486, N487, N501, and Y505) were confirmed as being crucial. Pair interaction energy decomposition analyses showed that hydrogen bonds, electrostatic interactions, and π-orbital interactions are important. Our results provide essential information for understanding SARS-CoV-2-antibody/peptide binding and may play roles in future antibody/antiviral drug design.

Tuning Proton Transfer Thermodynamics in SARS-CoV-2 Main Protease: Implications for Catalysis and Inhibitor Design.

The catalytic reaction in SARS-CoV-2 main protease is activated by a proton transfer (PT) from Cys145 to His41. The same PT is likely also required for the covalent binding of some inhibitors. Here we use a multiscale computational approach to investigate the PT thermodynamics in the apo enzyme and in complex with two potent inhibitors, N3 and the α-ketoamide 13b. We show that with the inhibitors the free energy cost to reach the charge-separated state of the active-site dyad is lower, with N3 inducing the most significant reduction. We also show that a few key sites (including specific water molecules) significantly enhance or reduce the thermodynamic feasibility of the PT reaction, with selective desolvation of the active site playing a crucial role. The approach presented is a cost-effective procedure to identify the enzyme regions that control the activation of the catalytic reaction and is thus also useful to guide the design of inhibitors.

Exploring the potential of novel phenolic compounds as potential therapeutic candidates against SARS-CoV-2, using quantum chemistry, molecular docking and dynamic studies.

In the current study, the interaction of SARS-CoV-2 protein (A and B chains of nsp13) with different recently synthesized phenolic compounds (Sreenivasulu et al., Synthetic Communications, 2020, 112-122) has been studied. The interactions have been investigated by using molecular docking, quantum chemical and molecular dynamics simulations methods. The molecular structures of all the ligands are studied quantum chemically in terms of their optimized structures, 3-D orbital distributions, global chemical descriptors, molecular electrostatic potential plots and HOMO-LUMO orbital energies. All the ligands show reasonably good binding affinities with nsp-13 protein. The ligand L2 shows to have better binding affinities to Chain A and Chain B of nsp13 protein, which are -6.7 and -6.4 kcal/mol. The study of intermolecular interactions indicates that L2 shows different hydrophobic and hydrogen bond interactions with both chains. Furthermore, molecular dynamic simulations of the nsp13-L2 complex are obtained over a time scale of 60 ns, which indicates its stability and flexibility behavior as assessed in terms of its RMSD and RMSF graphs. The ADMET analysis also shows no violation of Lipinski rule (RO5) by studied phenolic compounds. We believe that the current findings will be further confirmed by in vitro and in vivo studies of these recent phenolic compounds for their potential as inhibitors for SARS-Co-V-2 virus.

Computational simulations on the binding and reactivity of a nitrile inhibitor of the SARS-CoV-2 main protease.

We present a detailed computational analysis of the binding mode and reactivity of the novel oral inhibitor PF-07321332 developed against the SARS-CoV-2 3CL protease. Alchemical free energy calculations suggest that positions P3 and P4 could be susceptible to improvement in order to get a larger binding strength. QM/MM simulations unveil the reaction mechanism for covalent inhibition, showing that the nitrile warhead facilitates the recruitment of a water molecule for the proton transfer step.

Pharmacophore-Based Virtual Screening, Quantum Mechanics Calculations, and Molecular Dynamics Simulation Approaches Identified Potential Natural Antiviral Drug Candidates against MERS-CoV S1-NTD.

Middle East respiratory syndrome coronavirus (MERS-CoV) is a highly infectious zoonotic virus first reported into the human population in September 2012 on the Arabian Peninsula. The virus causes severe and often lethal respiratory illness in humans with an unusually high fatality rate. The N-terminal domain (NTD) of receptor-binding S1 subunit of coronavirus spike (S) proteins can recognize a variety of host protein and mediates entry into human host cells. Blocking the entry by targeting the S1-NTD of the virus can facilitate the development of effective antiviral drug candidates against the pathogen. Therefore, the study has been designed to identify effective antiviral drug candidates against the MERS-CoV by targeting S1-NTD. Initially, a structure-based pharmacophore model (SBPM) to the active site (AS) cavity of the S1-NTD has been generated, followed by pharmacophore-based virtual screening of 11,295 natural compounds. Hits generated through the pharmacophore-based virtual screening have re-ranked by molecular docking and further evaluated through the ADMET properties. The compounds with the best ADME and toxicity properties have been retrieved, and a quantum mechanical (QM) based density-functional theory (DFT) has been performed to optimize the geometry of the selected compounds. Three optimized natural compounds, namely Taiwanhomoflavone B (Amb23604132), 2,3-Dihydrohinokiflavone (Amb23604659), and Sophoricoside (Amb1153724), have exhibited substantial docking energy >-9.00 kcal/mol, where analysis of frontier molecular orbital (FMO) theory found the low chemical reactivity correspondence to the bioactivity of the compounds. Molecular dynamics (MD) simulation confirmed the stability of the selected natural compound to the binding site of the protein. Additionally, molecular mechanics generalized born surface area (MM/GBSA) predicted the good value of binding free energies (ΔG bind) of the compounds to the desired protein. Convincingly, all the results support the potentiality of the selected compounds as natural antiviral candidates against the MERS-CoV S1-NTD.

Exploration of natural compounds with anti-SARS-CoV-2 activity via inhibition of SARS-CoV-2 Mpro.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a dreaded pandemic in lack of specific therapeutic agent. SARS-CoV-2 Mpro, an essential factor in viral pathogenesis, is recognized as a prospective therapeutic target in drug discovery against SARS-CoV-2. To tackle this pandemic, Food and Drug Administration-approved drugs are being screened against SARS-CoV-2 Mpro via in silico and in vitro methods to detect the best conceivable drug candidates. However, identification of natural compounds with anti-SARS-CoV-2 Mpro potential have been recommended as rapid and effective alternative for anti-SARS-CoV-2 therapeutic development. Thereof, a total of 653 natural compounds were identified against SARS-CoV-2 Mpro from NP-lib database at MTi-OpenScreen webserver using virtual screening approach. Subsequently, top four potential compounds, i.e. 2,3-Dihydroamentoflavone (ZINC000043552589), Podocarpusflavon-B (ZINC000003594862), Rutin (ZINC000003947429) and Quercimeritrin 6"-O-L-arabinopyranoside (ZINC000070691536), and co-crystallized N3 inhibitor as reference ligand were considered for stringent molecular docking after geometry optimization by DFT method. Each compound exhibited substantial docking energy >-12 kcal/mol and molecular contacts with essential residues, including catalytic dyad (His41 and Cys145) and substrate binding residues, in the active pocket of SARS-CoV-2 Mpro against N3 inhibitor. The screened compounds were further scrutinized via absorption, distribution, metabolism, and excretion - toxicity (ADMET), quantum chemical calculations, combinatorial molecular simulations and hybrid QM/MM approaches. Convincingly, collected results support the potent compounds for druglikeness and strong binding affinity with the catalytic pocket of SARS-CoV-2 Mpro. Hence, selected compounds are advocated as potential inhibitors of SARS-CoV-2 Mpro and can be utilized in drug development against SARS-CoV-2 infection.

High-Throughput Screening and Quantum Mechanics for Identifying Potent Inhibitors Against Mac1 Domain of SARS-CoV-2 Nsp3.

SARS-CoV-2 encodes the Mac1 domain within the large nonstructural protein 3 (Nsp3), which has an ADP-ribosylhydrolase activity conserved in other coronaviruses. The enzymatic activity of Mac1 makes it an essential virulence factor for the pathogenicity of coronavirus (CoV). They have a regulatory role in counteracting host-mediated antiviral ADP-ribosylation, which is unique part of host response towards viral infections. Mac1 shows highly conserved residues in the binding pocket for the mono and poly ADP-ribose. Therefore, SARS-CoV-2 Mac1 enzyme is considered as an ideal drug target and inhibitors developed against them can possess a broad antiviral activity against CoV. ADP-ribose-1 phosphate bound closed form of Mac1 domain is considered for screening with large database of ZINC. XP docking and QPLD provides strong potential lead compounds, that perfectly fits inside the binding pocket. Quantum mechanical studies expose that, substrate and leads have similar electron donor ability in the head regions, that allocates tight binding inside the substrate-binding pocket. Molecular dynamics study confirms the substrate and new lead molecules presence of electron donor and acceptor makes the interactions tight inside the binding pocket. Overall binding phenomenon shows both substrate and lead molecules are well-adopt to bind with similar binding mode inside the closed form of Mac1.

A computational evaluation of targeted oxidation strategy (TOS) for potential inhibition of SARS-CoV-2 by disulfiram and analogues.

In the new millennium, the outbreak of new coronavirus has happened three times: SARS-CoV, MERS-CoV, and SARS-CoV-2. Unfortunately, we still have no pharmaceutical weapons against the diseases caused by these viruses. The pandemic of SARS-CoV-2 reminds us the urgency to search new drugs with totally different mechanism that may target the weaknesses specific to coronaviruses. Herein, we disclose a computational evaluation of targeted oxidation strategy (TOS) for potential inhibition of SARS-CoV-2 by disulfiram, a 70-year-old anti-alcoholism drug, and predict a multiple-target mechanism. A preliminary list of promising TOS drug candidates targeting the two thiol proteases of SARS-CoV-2 are proposed upon virtual screening of 32,143 disulfides.

Quantum Machine Learning Algorithms for Drug Discovery Applications.

The growing quantity of public and private data sets focused on small molecules screened against biological targets or whole organisms provides a wealth of drug discovery relevant data. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large data sets with thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, which is widely used in drug discovery, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on a QC. Here, we show how to achieve compression with data sets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands of molecules (whole cell screening data sets for plague and M. tuberculosis) with SVM and the data reuploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This study illustrates the steps needed in order to be "quantum computer ready" in order to apply quantum computing to drug discovery and to provide the foundation on which to build this field.