Designing new drug candidates as inhibitors against wild and mutant type neuraminidases: molecular docking, molecular dynamics and binding free energy calculations

Influenza virus is the cause of the death of millions of people with about 3-4 pandemics every hundred years in history. It also turns into a seasonal disease, bringing about approximately 5-15% of the population to be infected and 290,000-650,000 people to die every year. These numbers reveal that it is necessary to be on the alert to work towards influenza in order to protect public health. There are FDA-approved antiviral drugs such as oseltamivir and zanamivir recommended by the World Center for Disease Prevention. However, after the recent outbreaks such as bird flu and swine flu, increasing studies have shown that the flu virus has gained resistance to these drugs. So, there is an urgent need to find new drugs effective against this virus. This study aims to investigate new drug candidates targeting neuraminidase (NA) for the treatment of influenza by using computer aided drug design approaches. They involve virtual scanning, de novo design, rational design, docking, MD, MMGB/PBSA. The investigation includes H1N1, H5N1, H2N2 and H3N2 neuraminidase proteins and their mutant variants possessing resistance to FDA-approved drugs. Virtual screening consists of approximately 30 thousand molecules while de novo and rational designs produced over a hundred molecules. These approaches produced three lead molecules with binding energies for both non-mutant (-34.84, -59.99 and -60.66 kcal/mol) and mutant (-40.40, -58.93, -76.19 kcal/mol) H2N2 NA calculated by MM-PBSA compared with those of oseltamivir -25.64 and -18.40 respectively. The results offer new drug candidates against influenza infection.Communicated by Ramaswamy H. Sarma.

Synthesis, antimycobacterial screening, molecular docking, ADMET prediction and pharmacological evaluation on novel pyran-4-one bearing hydrazone, triazole and isoxazole moieties: Potential inhibitors of SARS CoV-2

The respiratory infection tuberculosis is caused by the bacteria Mycobacterium tuberculosis and its unrelenting spread caused millions of deaths around the world. Hence, it is needed to explore potential and less toxic anti-tubercular drugs. In the present work, we report the synthesis and antitubercular activity of four different (hydrazones 7-12, O-ethynyl oximes 19-24, triazoles 25-30, and isoxazoles 31-36) hybrids. Among these hybrids 9, 10, 33, and 34, displayed high antitubercular activity at 3.12 g/mL with >90% of inhibitions. The hybrids also showed good docking energies between -6.8 and -7.8 kcal/mol. Further, most active molecules were assayed for their DNA gyrase reduction ability towards M. tuberculosis and E.coli DNA gyrase by the DNA supercoiling and ATPase gyrase assay methods. All four hybrids showed good IC50 values comparable to that of the reference drug. In addition, the targets were also predicted as a potential binder for papain-like protease (SARS CoV-2 PLpro) by molecular docking and a good interaction result was observed. Besides, all targets were predicted for their absorption, distribution, metabolism, and excretion - toxicity (ADMET) profile and found a significant amount of ADMET and bioavailability.

Impact of Applicability Domains to Generative Artificial Intelligence

Molecular generative artificial intelligence is drawing significant attention in the drug design community, with several experimentally validated proof of concepts already published. Nevertheless, generative models are known for sometimes generating unrealistic, unstable, unsynthesizable, or uninteresting structures. This calls for methods to constrain those algorithms to generate structures in drug-like portions of the chemical space. While the concept of applicability domains for predictive models is well studied, its counterpart for generative models is not yet well-defined. In this work, we empirically examine various possibilities and propose applicability domains suited for generative models. Using both public and internal data sets, we use generative methods to generate novel structures that are predicted to be actives by a corresponding quantitative structure-activity relationships model while constraining the generative model to stay within a given applicability domain. Our work looks at several applicability domain definitions, combining various criteria, such as structural similarity to the training set, similarity of physicochemical properties, unwanted substructures, and quantitative estimate of drug-likeness. We assess the structures generated from both qualitative and quantitative points of view and find that the applicability domain definitions have a strong influence on the drug-likeness of generated molecules. An extensive analysis of our results allows us to identify applicability domain definitions that are best suited for generating drug-like molecules with generative models. We anticipate that this work will help foster the adoption of generative models in an industrial context.

Photocatalytic removal of 2-chlorophenol from water by using waste eggshell-derived calcium ferrite

A new approach to recycling low-value eggshell food waste was to produce a CaFe₂O₄ semiconductor with a narrow band gap (E_g = 2.81 eV) via hydrothermal treatments of powdered eggshell suspended in aqueous ferric salt (Fe³⁺) solutions at varying Fe loadings. It was possible to obtain a single phase of CaFe₂O₄ without any Ca(OH)₂ and CaO impurities using an optimal Fe loading (30 wt% of Fe³⁺ by eggshell weight). The CaFe₂O₄ material was used as a photocatalyst for the breakdown of 2-chlorophenol (2-CP, a herbicide model chemical) as a pollutant in water. The CaFe₂O₄ with a Fe loading of 7.1 wt% exhibited a high 2-CP removal efficiency of 86.1% after 180 min of UV-visible light irradiation. Additionally, the eggshell-derived CaFe₂O₄ photocatalyst can be effectively reused, giving a high removal efficiency of 70.5% after the third cycle, without the requirement of regeneration processes (washing or re-calcination). Although radical trapping experiments confirmed that hydroxyl radicals were generated in the photocatalytic reactions, photogenerated holes play a significant role in the high 2-CP degradation efficiencies. The performance of the bioderived CaFe₂O₄ photocatalysts in the removal of pesticides from water demonstrated the benefits of resource recycling in the area of materials science and in environmental remediation and protection.

Physics-inspired machine learning of localized intensive properties

Machine learning (ML) has been widely applied to chemical property prediction, most prominently for the energies and forces in molecules and materials. The strong interest in predicting energies in particular has led to a 'local energy'-based paradigm for modern atomistic ML models, which ensures size-extensivity and a linear scaling of computational cost with system size. However, many electronic properties (such as excitation energies or ionization energies) do not necessarily scale linearly with system size and may even be spatially localized. Using size-extensive models in these cases can lead to large errors. In this work, we explore different strategies for learning intensive and localized properties, using HOMO energies in organic molecules as a representative test case. In particular, we analyze the pooling functions that atomistic neural networks use to predict molecular properties, and suggest an orbital weighted average (OWA) approach that enables the accurate prediction of orbital energies and locations.

Generative Models as an Emerging Paradigm in the Chemical Sciences

Traditional computational approaches to design chemical species are limited by the need to compute properties for a vast number of candidates, e.g., by discriminative modeling. Therefore, inverse design methods aim to start from the desired property and optimize a corresponding chemical structure. From a machine learning viewpoint, the inverse design problem can be addressed through so-called generative modeling. Mathematically, discriminative models are defined by learning the probability distribution function of properties given the molecular or material structure. In contrast, a generative model seeks to exploit the joint probability of a chemical species with target characteristics. The overarching idea of generative modeling is to implement a system that produces novel compounds that are expected to have a desired set of chemical features, effectively sidestepping issues found in the forward design process. In this contribution, we overview and critically analyze popular generative algorithms like generative adversarial networks, variational autoencoders, flow, and diffusion models. We highlight key differences between each of the models, provide insights into recent success stories, and discuss outstanding challenges for realizing generative modeling discovered solutions in chemical applications.

Benchmarking First-Principles Reaction Equilibrium Composition Prediction

The availability of thermochemical properties allows for the prediction of the equilibrium compositions of chemical reactions. The accurate prediction of these can be crucial for the design of new chemical synthesis routes. However, for new processes, these data are generally not completely available. A solution is the use of thermochemistry calculated from first-principles methods such as Density Functional Theory (DFT). Before this can be used reliably, it needs to be systematically benchmarked. Although various studies have examined the accuracy of DFT from an energetic point of view, few studies have considered its accuracy in predicting the temperature-dependent equilibrium composition. In this work, we collected 117 molecules for which experimental thermochemical data were available. From these, we constructed 2648 reactions. These experimentally constructed reactions were then benchmarked against DFT for 6 exchange-correlation functionals and 3 quality of basis sets. We show that, in reactions that do not show temperature dependence in the equilibrium composition below 1000 K, over 90% are predicted correctly. Temperature-dependent equilibrium compositions typically demonstrate correct qualitative behavior. Lastly, we show that the errors are equally caused by errors in the vibrational spectrum and the DFT electronic ground state energy.

A Genetic Programming Approach to Engineering MRI Reporter Genes

Here we develop a mechanism of protein optimization using a computational approach known as "genetic programming". We developed an algorithm called Protein Optimization Engineering Tool (POET). Starting from a small library of literature values, the use of this tool allowed us to develop proteins that produce four times more MRI contrast than what was previously state-of-the-art. Interestingly, many of the peptides produced using POET were dramatically different with respect to their sequence and chemical environment than existing CEST producing peptides, and challenge prior understandings of how those peptides function. While existing algorithms for protein engineering rely on divergent evolution, POET relies on convergent evolution and consequently allows discovery of peptides with completely different sequences that perform the same function with as good or even better efficiency. Thus, this novel approach can be expanded beyond developing imaging agents and can be used widely in protein engineering.

Integrating structure-based approaches in generative molecular design

Generative molecular design for drug discovery and development has seen a recent resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by computationally exploring much larger chemical spaces than traditional virtual screening techniques. However, most generative models thus far have only utilized small-molecule information to train and condition de novo molecule generators. Here, we instead focus on recent approaches that incorporate protein structure into de novo molecule optimization in an attempt to maximize the predicted on-target binding affinity of generated molecules. We summarize these structure integration principles into either distribution learning or goal-directed optimization and for each case whether the approach is protein structure-explicit or implicit with respect to the generative model. We discuss recent approaches in the context of this categorization and provide our perspective on the future direction of the field.

A computational overview of integrase strand transfer inhibitors (INSTIs) against emerging and evolving drug-resistant HIV-1 integrase mutants

AIDS (Acquired immunodeficiency syndrome) is one of the chronic and potentially life-threatening epidemics across the world. Hitherto, the non-existence of definitive drugs that could completely cure the Human immunodeficiency virus (HIV) implies an urgent necessity for the discovery of novel anti-HIV agents. Since integration is the most crucial stage in retroviral replication, hindering it can inhibit overall viral transmission. The 5 FDA-approved integrase inhibitors were computationally investigated, especially owing to the rising multiple mutations against their susceptibility. This comparative study will open new possibilities to guide the rational design of novel lead compounds for antiretroviral therapies (ARTs), more specifically the structure-based design of novel Integrase strand transfer inhibitors (INSTIs) that may possess a better resistance profile than present drugs. Further, we have discussed potent anti-HIV natural compounds and their interactions as an alternative approach, recommending the urgent need to tap into the rich vein of indigenous knowledge for reverse pharmacology. Moreover, herein, we discuss existing evidence that might change in the near future.

Phenylpyrazolone-1,2,3-triazole Hybrids as Potent Antiviral Agents with Promising SARS-CoV-2 Main Protease Inhibition Potential

COVID-19 infection is now considered one of the leading causes of human death. As an attempt towards the discovery of novel medications for the COVID-19 pandemic, nineteen novel compounds containing 1,2,3-triazole side chains linked to phenylpyrazolone scaffold and terminal lipophilic aryl parts with prominent substituent functionalities were designed and synthesized via a click reaction based on our previous work. The novel compounds were assessed using an in vitro effect on the growth of SARS-CoV-2 virus-infested Vero cells with different compound concentrations: 1 and 10 μM. The data revealed that most of these derivatives showed potent cellular anti-COVID-19 activity and inhibited viral replication by more than 50% with no or weak cytotoxic effect on harboring cells. In addition, in vitro assay employing the SARS-CoV-2-Main protease inhibition assay was done to test the inhibitors' ability to block the common primary protease of the SARS-CoV-2 virus as a mode of action. The obtained results show that the one non-linker analog 6h and two amide-based linkers 6i and 6q were the most active compounds with IC₅₀ values of 5.08, 3.16, and 7.55 μM, respectively, against the viral protease in comparison to data of the selective antiviral agent GC-376. Molecular modeling studies were done for compound placement within the binding pocket of protease which reveal conserved residues hydrogen bonding and non-hydrogen interactions of 6i analog fragments: triazole scaffold, aryl part, and linker. Moreover, the stability of compounds and their interactions with the target pocket were also studied and analyzed by molecular dynamic simulations. The physicochemical and toxicity profiles were predicted, and the results show that compounds behave as an antiviral activity with low or no cellular or organ toxicity. All research results point to the potential usage of new chemotype potent derivatives as promising leads to be explored in vivo that might open the door to rational drug development of SARS-CoV-2 Main protease potent medicines.

Development of a Synthetic Biosensor for Chemical Exchange MRI Utilizing In Silico Optimized Peptides

Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) has been identified as a novel alternative to classical diagnostic imaging. Over the last several decades, many studies have been conducted to determine possible CEST agents, such as endogenously expressed compounds or proteins, that can be utilized to produce contrast with minimally invasive procedures and reduced or non-existent levels of toxicity. In recent years there has been an increased interest in the generation of genetically engineered CEST contrast agents, typically based on existing proteins with CEST contrast or modified to produce CEST contrast. We have developed an in-silico method for the evolution of peptide sequences to optimize CEST contrast and showed that these peptides could be combined to create de novo biosensors for CEST MRI. A single protein, superCESTide 2.0, was designed to be 198 amino acids. SuperCESTide 2.0 was expressed in E. coli and purified with size-exclusion chromatography. The magnetic transfer ratio asymmetry (MTR asym ) generated by superCESTide 2.0 was comparable to levels seen in previous CEST reporters, such as protamine sulfate (salmon protamine, SP), Poly-L-Lysine (PLL), and human protamine (hPRM1). This data shows that novel peptides with sequences optimized in silico for CEST contrast that utilizes a more comprehensive range of amino acids can still produce contrast when assembled into protein units expressed in complex living environments.

Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights

Artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) is a general-purpose method that was shown to achieve high accuracy for many applications with a speed close to its baseline semiempirical quantum mechanical (SQM) method ODM2*. Here, we evaluate the hitherto unknown performance of out-of-the-box AIQM1 without any refitting for reaction barrier heights on eight datasets, including a total of ∼24 thousand reactions. This evaluation shows that AIQM1's accuracy strongly depends on the type of transition state and ranges from excellent for rotation barriers to poor for, e.g., pericyclic reactions. AIQM1 clearly outperforms its baseline ODM2* method and, even more so, a popular universal potential, ANI-1ccx. Overall, however, AIQM1 accuracy largely remains similar to SQM methods (and B3LYP/6-31G* for most reaction types) suggesting that it is desirable to focus on improving AIQM1 performance for barrier heights in the future. We also show that the built-in uncertainty quantification helps in identifying confident predictions. The accuracy of confident AIQM1 predictions is approaching the level of popular density functional theory methods for most reaction types. Encouragingly, AIQM1 is rather robust for transition state optimizations, even for the type of reactions it struggles with the most. Single-point calculations with high-level methods on AIQM1-optimized geometries can be used to significantly improve barrier heights, which cannot be said for its baseline ODM2* method.

Evaluation of tight-binding DFT performance for the description of organic photochromes properties

Photochromic molecules are widely studied and developed for their many potential applications. To optimize the required properties through theoretical models, a considerable chemical space is to be explored, and their environment in devices is to be accounted for.. To this end, cheap and reliable computational methods can be powerful tools to steer synthetic developments. As ab initio methods remain costly for extensive studies (in terms of the size of the system and/or number of molecules), semiempirical methods such as density functional tight-binding (TB) could offer a good compromise between accuracy computational cost. However, these approaches necessitate benchmarking on the families of compounds of interest. Thus, the aim of the present study is to evaluate the accuracy of several key features calculated with TB methods (DFTB2, DFTB3, GFN2-xTB, and LC-DFTB2) for three sets of photochromic organic molecules: azobenzene (AZO), norbornadiene/quadricyclane (NBD/QC), and dithienylethene (DTE) derivatives. The features considered here are the optimized geometries, the difference in energy between the two isomers (ΔE), and of the energies of the first relevant excited states. All the TB results are compared to those obtained with DFT methods and state-of-the-art electronic structure calculation methods: DLPNO-CCSD(T) for ground states and DLPNO-STEOM-CCSD for excited states. Our results show that, overall, DFTB3 is the TB method leading to the best results for the geometries and the ΔE values and can be used alone for these purposes for NBD/QC and DTE derivatives. Single point calculations at the r2SCAN-3c level using TB geometries allow circumventing the deficiencies of the TB methods in the AZO series. For electronic transition calculations, the range separated LC-DFTB2 method is the most accurate TB method tested for AZO and NBD/QC derivatives, in close agreement with the reference.

Hepatitis C Virus Dynamic Transmission Models Among People Who Inject Drugs

Background

Transmission dynamic model is a concrete structure to describe and investigate the complex system of host-pathogen interactions. Hepatitis C virus (HCV) is a blood-borne virus that is transmitted from infectious to susceptible individuals when they come into contact with HCV-contaminated equipment. Injecting drug use is the most known transmission route, and about 80% of new HCV cases have been confirmed as having acquired HCV infection via drug injection.

Objective

The main objective of this review paper was to review the importance of HCV dynamic transmission model, that enables the readers to understand the mechanism how HCV is transmissible from infectious to susceptible hosts and the effective controlling strategies.

Methods

PubMed Central, Google Scholar, and Web of Science electronic databases have been used to search data by using key terms like "HCV transmission model among people who inject drug (PWID)", HCV potential herd immunity", and "basic reproductive number for HCV transmission in PWID." Data from research findings other than English version have been excluded from being used, and the most recently published data have been considered to be included.

Conclusion

HCV belongs to the Hepacivirus genus within the Flaviviridae family. HCV infection is acquired when the susceptible individuals in populations come into contact with medical equipment such as shared syringes and needles, or swabs contaminated with infected blood. Construction of HCV transmission dynamic model is very significant in order to predict the duration and magnitude of its epidemic and to evaluate the potential impact of intervention. Comprehensive harm reduction and care/support service strategies are the best approach for intervention regarding HCV infection transmission among PWID.

Docking-based generative approaches in the search for new drug candidates

Despite the popularity of virtual screening (VS) of existing compound libraries, the search for new potential drug candidates also takes advantage of generative protocols, where new compound suggestions are enumerated using various algorithms. To increase the activity potency of generative approaches, they have recently been coupled with molecular docking, a leading methodology of structure-based drug design (SBDD). In this review, we summarize progress since docking-based generative models emerged. We propose a new taxonomy for these methods and discuss their importance for the field of computer-aided drug design (CADD). In addition, we discuss the most promising directions for the further development of generative protocols coupled with docking.

Four Routes to 3-(3-Methoxy-1,3-dioxopropyl)pyrrole, a Core Motif of Rings C and E in Photosynthetic Tetrapyrroles

The photosynthetic tetrapyrroles share a common structural feature comprised of a β-ketoester motif embedded in an exocyclic ring (ring E). As part of a total synthesis program aimed at preparing native structures and analogues, 3-(3-methoxy-1,3-dioxopropyl)pyrrole was sought. The pyrrole is a precursor to analogues of ring C and the external framework of ring E. Four routes were developed. Routes 1-3 entail a Pd-mediated coupling process of a 3-iodopyrrole with potassium methyl malonate, whereas route 4 relies on electrophilic substitution of TIPS-pyrrole with methyl malonyl chloride. Together, the four routes afford considerable latitude. A long-term objective is to gain the capacity to create chlorophylls and bacteriochlorophylls and analogues thereof by facile de novo means for diverse studies across the photosynthetic sciences.

Synthesis of bis-furyl-pyrrolo[3,4-b]pyridin-5-ones via Ugi-Zhu reaction and in vitro activity assays against human SARS-CoV-2 and in silico studies on its main proteins

An Ugi-Zhu three-component reaction (UZ-3CR) coupled in one pot manner to a cascade process (N-acylation/aza Diels-Alder cycloaddition/decarboxylation/dehydration) was performed to synthesize a series of bis-furyl-pyrrolo[3,4-b]pyridin-5-ones in 45 to 82% overall yields using ytterbium triflate as a catalyst, toluene as a solvent, and microwaves as a heat source. The synthesized molecules were evaluated in vitro against human SARS-CoV-2 through a time-of-addition approach, finding that compound 1e, at a concentration of 10.0 μM, exhibited a significant reduction at the initial infection stages, thus showing prophylactic potential. On the other hand, it was found that compound 1d, at the same concentration, was significantly active when applied post-infection, thus exhibiting a therapeutic profile. Moreover, compound 1f showed both, prophylactic and therapeutic activity. Then, to understand interactions between synthesized compounds and the main proteins related to the virus, docking studies were performed on spike-glycoprotein, main-protease, and Nsp3 protein, finding moderate to strong binding energies, matching accurately with the in vitro results. Additionally, a pharmacophore model was computed behind further rational drug design.

Exploring the structural and dynamic differences between human carnosinase I (CN1) and II (CN2)

Human carnosinases (CNs) are dimeric dipeptidases in the metallopeptidase M20 family. Two isoforms of carnosinases (Zn²⁺ -containing carnosinase 1 (CN1) found in serum and Mn²⁺ -carnosinase 2 (CN2) in tissue) were identified. Both CNs cleave histidine-containing (Xaa-His) dipeptides such as carnosine where CN2 was found to accept a broader spectrum of substrates. A loss of CN function, resulting in a high carnosine concentration, reduces risk for diabetes and neurological disorders. Although several studies on CN activities and its Michaelis complex were conducted, all shed the light on CN1 activity where the CN2 data is limited. Also, the molecular details on CN1 and CN2 similarity and dissimilarity in structure and function remain unclear. Thus, in this work, molecular dynamics (MD) simulations were employed to study structure and dynamics of human CN1 and CN2 in comparison. The results show that the different catalytic ability of both CNs is due to their pocket size and environment. CN2 can accept a wider range of substrate due to the wider mouth of a binding pocket. The L1 loop seems to play a role in gating activity. Comparing to CN2, CN1 provides more electronegative entrance, more wettability, and higher stability of catalytic metal ion-pair in the active site which allow more efficient water-mediated catalysis. The microscopic understanding obtained here can serve as a basis for CN inhibition strategies resulting in higher carnosine levels and consequently mitigating complications associated with diseases such as diabetes and neurological disorder.

Intense femtosecond optical pulse shaping approaches to spatiotemporal control

For studying any event, measurement can never be enough; "control" is required. This means mere passive tracking of the event is insufficient and being able to manipulate it is necessary. To maximize this capability to exert control and manipulate, both spatial and temporal domains need to be jointly accounted for, which has remained an intractable problem at microscopic scales. Simultaneous control of dynamics and position of an observable event requires a holistic combination of spatial and temporal control principles, which gives rise to the field of spatiotemporal control. For this, we present a novel femtosecond pulse-shaping approach. We explain how to achieve spatiotemporal control by spatially manipulating the system through trapping and subsequently or simultaneously exerting temporal control using shaped femtosecond pulses. By leveraging ultrafast femtosecond lasers, the prospect of having temporal control of molecular dynamics increases, and it becomes possible to circumvent the relaxation processes at microscopic timescales. Optical trapping is an exemplary demonstration of spatial control that results in the immobilization of microscopic objects with radiation pressure from a tightly focused laser beam. Conventional single-beam optical tweezers use continuous-wave (CW) lasers for achieving spatial control through photon fluxes, but these lack temporal control knobs. We use a femtosecond high repetition rate (HRR) pulsed laser to bypass this lack of dynamical control in the time domain for optical trapping studies. From a technological viewpoint, the high photon flux requirement of stable optical tweezers necessitates femtosecond pulse shaping at HRR, which has been a barrier until the recent Megahertz pulse shaping developments. Finally, recognizing the theoretical distinction between tweezers with femtosecond pulses and CW lasers is of paramount interest. Non-linear optical (NLO) interactions must be included prima facie to understand pulsed laser tweezers in areas where they excel, like the two-photon-fluorescence-based detection. We show that our theoretical model can holistically address the common drawback of all tweezers. We are able to mitigate the effects of laser-induced heating by balancing this with femtosecond laser-induced NLO effects. An interesting side-product of HRR femtosecond-laser-induced thermal lens is the development of femtosecond thermal lens spectroscopy (FTLS) and its ability to provide sensitive molecular detection.

Critical behavior of the diffusive susceptible-infected-recovered model

The critical behavior of the nondiffusive susceptible-infected-recovered model on lattices had been well established in virtue of its duality symmetry. By performing simulations and scaling analyses for the diffusive variant on the two-dimensional lattice, we show that diffusion for all agents, while rendering this symmetry destroyed, constitutes a singular perturbation that induces asymptotically distinct dynamical and stationary critical behavior from the nondiffusive model. In particular, the manifested crossover behavior in the effective mean-square radius exponents reveals that slow crossover behavior in general diffusive multispecies reaction systems may be ascribed to the interference of multiple length scales and timescales at early times.

Potential of magnetic quinoa biosorbent composite and HNO3 treated biosorbent for effective sequestration of chromium (VI) from contaminated water

The present study aims to prepare novel quinoa biosorbent (QB), acid activated QB (QB/Acid) and its nanocomposite with magnetic nanoparticles (QB/MNPs) for batch scale Cr removal from contaminated water. The Cr adsorption was systematically studied at different pH (2-9), adsorbent dosage (1-3 g/L), initial concentration (25-200 mg/L), contact time (180 min) and competing ions in water. Maximum Cr adsorption was observed onto QB/MNPs (57.4 mg/L), followed by QB/Acid (46.35 mg/g) and QB (39.9 mg/g). The Cr removal by QB/MNPs was higher than QB/Acid and QB. Results revealed that the highest Cr removal was obtained at optimum pH 4, 25 mg/L, and 2 g/L dosage. The FTIR spectra displayed various functional groups on adsorbents surface serving as a potential scaffold to remove Cr from contaminated water. The equilibrium and kinetic Cr adsorption data best fitted with Freundlich and pseudo-second order models, respectively (R² ≥ 0.96). The QB/MNPs showed excellent reusability in five adsorption/desorption cycles (4.7% decline) with minor leaching of Fe (below threshold level). The coexisting ions in groundwater showed an inhibitory effect on Cr sequestration (5%) from water. The comparison of Cr adsorption by QB/MNPs and QB/Acid showed better potential for Cr sequestration than various previously explored adsorbents in the literature.

Guided discovery of chemical reaction pathways with imposed activation

Computational power and quantum chemical methods have improved immensely since computers were first applied to the study of reactivity, but the de novo prediction of chemical reactions has remained challenging. We show that complex reaction pathways can be efficiently predicted in a guided manner using chemical activation imposed by geometrical constraints of specific reactive modes, which we term imposed activation (IACTA). Our approach is demonstrated on realistic and challenging chemistry, such as a triple cyclization cascade involved in the total synthesis of a natural product, a water-mediated Michael addition, and several oxidative addition reactions of complex drug-like molecules. Notably and in contrast with traditional hand-guided computational chemistry calculations, our method requires minimal human involvement and no prior knowledge of the products or the associated mechanisms. We believe that IACTA will be a transformational tool to screen for chemical reactivity and to study both by-product formation and decomposition pathways in a guided way.

Pea eggplant (Solanum torvum Swartz) is a source of plant food polyphenols with SARS-CoV inhibiting potential

Background

Pea eggplant (Solanum torvum Swartz) commonly known as turkey berry or 'terung pipit' in Malay is a vegetable plant widely consumed by the local community in Malaysia. The shrub bears pea-like turkey berry fruits (TBFs), rich in phytochemicals of medicinal interest. The TBF phytochemicals hold a wide spectrum of pharmacological properties. In this study, the TBF phytochemicals' potential inhibitory properties were evaluated against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) of the Coronavirus disease 2019 (COVID-19). The TBF polyphenols were screened against SARS-CoV receptors via molecular docking and the best receptor-ligand complex was validated further by molecular dynamics (MD) simulation.

Method

The SARS-CoV receptor structure files (viral structural components) were retrieved from the Protein Data Bank (PDB) database: membrane protein (PDB ID: 3I6G), main protease (PDB ID: 5RE4), and spike glycoproteins (PDB ID: 6VXX and 6VYB). The receptor binding pocket regions were identified by Discovery Studio (BIOVIA) for targeted docking with TBF polyphenols (genistin, kaempferol, mellein, rhoifolin and scutellarein). The ligand and SARS-CoV family receptor structure files were pre-processed using the AutoDock tools. Molecular docking was performed with the Lamarckian genetic algorithm using AutoDock Vina 4.2 software. The best pose (ligand-receptor complex) from the molecular docking analysis was selected based on the minimum binding energy (MBE) and extent of structural interactions, as indicated by BIOVIA visualization tool. The selected complex was validated by a 100 ns MD simulation run using the GROMACS software. The dynamic behaviour and stability of the receptor-ligand complex were evaluated by the root mean square displacement (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), solvent accessible surface volume (SASV) and number of hydrogen bonds.

Results

At RMSD = 0, the TBF polyphenols showed fairly strong physical interactions with SARS-CoV receptors under all possible combinations. The MBE of TBF polyphenol-bound SARS CoV complexes ranged from -4.6 to -8.3 kcal/mol. Analysis of the structural interactions showed the presence of hydrogen bonds, electrostatic and hydrophobic interactions between the receptor residues (RR) and ligands atoms. Based on the MBE values, the 3I6G-rhoifolin (MBE = -8.3 kcal/mol) and 5RE4-genistin (MBE = -7.6 kcal/mol) complexes were ranked with the least value. However, the latter showed a greater extent of interactions between the RRs and the ligand atoms and thus was further validated by MD simulation. The MD simulation parameters of the 5RE4-genistin complex over a 100 ns run indicated good structural stability with minimal flexibility within genistin binding pocket region. The findings suggest that S. torvum polyphenols hold good therapeutics potential in COVID-19 management.

The Essential Role of Water Molecules in the Reaction Mechanism of Protein O-Fucosyltransferase 2

Protein O-fucosyltransferase 2 (PoFUT2) is an inverting glycosyltransferase (GT) that fucosylates thrombospondin repeats (TSRs) from group 1 and 2. PoFUT2 recognizes a large and diverse number of TSRs through a dynamic network of water-mediated interactions. By X-ray structural studies of C. elegans PoFUT2 complexed to a TSR of group 2, we demonstrate that this GT recognizes similarly the 3D structure of TSRs from both groups 1 and 2. Its active site is highly exposed to the solvent, suggesting that water molecules might also play an essential role in the fucosylation mechanism. We applied QM/MM methods using human PoFUT2 as a model, and found that HsPoFUT2 follows a classical S_N 2 reaction mechanism in which water molecules contribute to a great extent in facilitating the release of the leaving pyrophosphate unit, causing the H transfer from the acceptor nucleophile (Thr/Ser) to the catalytic base, which is the last event in the reaction. This demonstrates the importance of water molecules not only in recognition of the ligands but also in catalysis.

Effect of chlorogenic acid plus donepezil on critical neurocortical enzyme activities, inflammatory markers, and synaptophysin immunoreactivity in scopolamine-assaulted rats, supported by multiple ligand simultaneous docking

The effect of chlorogenic acid (a natural phenolic acid ubiquitous in plant foods) on selected therapeutic properties of donepezil (DON) in a scopolamine (SCOP)-induced rat model of amnesia was the focus of this study. Adult albino (Wister strain) rats were allocated into five groups (n = 11) consisting of control, SCOP, SCOP + chlorogenic acid (CGA), SCOP + DON, and SCOP + CGA + DON for 7 days. Post-treatment, the rat brain cerebral cortex homogenate was assayed for cholinesterase and monoamine oxidase activities. Also, the reactive oxygen species, total thiol and nitric oxide contents, alongside catalase, and superoxide dismutase activities were determined. Routine histology for neuronal and glial cells as well as synaptophysin immunoreactivity was also carried out on the cerebral cortex. Thereafter, multiple ligand simultaneous docking was carried out for DON and CGA at the active sites of AChE and BChE. The results revealed that the biochemical parameters, glial cells, and synaptophysin immunoreactivity were significantly impaired in the cerebral cortex of scopolamine-treated rats. However, impaired butyrylcholinesterase and monoamine oxidase activity, together with antioxidant, glial cells, and synaptophysin levels were significantly ameliorated in scopolamine-treated rats administered DON + CGA compared to donepezil alone. The docking of both DON and CGA at the active sites of AChE or BChE showed higher binding energy to both enzymes compared to individual interactions of either DON or CGA. Hence, this study has been able to show that CGA could improve some of the therapeutic effects of DON, which could broaden the therapeutic spectrum of this drug. PRACTICAL APPLICATIONS: This study showed that chlorogenic acid (a major phenolic acid found in plant foods such as coffee) modulated some of the therapeutic properties of donepezil (an anticholinesterase drug used in the treatment of mild-to-moderate Alzheimer's disease). The combinations elicited better anti-butyrylcholinesterase, antimonoamine oxidase, and antioxidant properties, thus presenting this food-drug interaction as potentially able to offer better therapeutic properties.

Novel ciprofloxacin and norfloxacin-tetrazole hybrids as potential antibacterial and antiviral agents: targeting S. aureus topoisomerase and SARS-CoV-2-MPro

This study was designed to synthesize hybridizing molecules from ciprofloxacin and norfloxacin by enhancing their biological activity with tetrazoles. The synthesized compounds were investigated in the interaction with the target enzyme of fluoroquinolones (DNA gyrase) and COVID-19 main protease using molecular similarity, molecular docking, and QSAR studies. A QSAR study was carried out to explore the antibacterial activity of our compounds over Staphylococcus aureus a QSAR study, using descriptors obtained from the docking with DNA gyrase, in combination with steric type descriptors, was done obtaining suitable statistical parameters (R2=87.00, QLMO2=71.67, and QEXT2=73.49) to support our results. The binding interaction of our compounds with CoV-2-Mpro was done by molecular docking and were compared with different covalent and non-covalent inhibitors of this enzyme. For the docking studies we used several crystallographic structures of the CoV-2-Mpro. The interaction energy values and binding mode with several key residues, by our compounds, support the capability of them to be CoV-2-Mpro inhibitors. The characterization of the compounds was completed using FT-IR, ¹H-NMR, ¹³C-NMR, ¹⁹F-NMR and HRMS spectroscopic methods. The results showed that compounds 1, 4, 5, 10 and 12 had the potential to be further studied as new antibacterial and antiviral compounds

Best-Practice DFT Protocols for Basic Molecular Computational Chemistry

Nowadays, many chemical investigations are supported by routine calculations of molecular structures, reaction energies, barrier heights, and spectroscopic properties. The lion's share of these quantum-chemical calculations applies density functional theory (DFT) evaluated in atomic-orbital basis sets. This work provides best-practice guidance on the numerous methodological and technical aspects of DFT calculations in three parts: Firstly, we set the stage and introduce a step-by-step decision tree to choose a computational protocol that models the experiment as closely as possible. Secondly, we present a recommendation matrix to guide the choice of functional and basis set depending on the task at hand. A particular focus is on achieving an optimal balance between accuracy, robustness, and efficiency through multi-level approaches. Finally, we discuss selected representative examples to illustrate the recommended protocols and the effect of methodological choices.

Teaching undergraduate physical chemistry lab with kinetic analysis of COVID-19 in the United States

A physical chemistry lab for undergraduate students described in this report is about applying kinetic models to analyze the spread of COVID-19 in the United States and obtain the reproduction numbers. The susceptible-infectious-recovery (SIR) model and the SIR-vaccinated (SIRV) model are explained to the students and are used to analyze the COVID-19 spread data from U.S. Centers for Disease Control and Prevention (CDC). The basic reproduction number R 0 and the real-time reproduction number R t of COVID-19 are extracted by fitting the data with the models, which explains the spreading kinetics and provides a prediction of the spreading trend in a given state. The procedure outlined here shows the differences between the SIR model and the SIRV model. The SIRV model considers the effect of vaccination which helps explain the later stages of the ongoing pandemic. The predictive power of the models is also shown giving the students some certainty in the predictions they made for the following months.

Neural-network-backed evolutionary search for SrTiO3(110) surface reconstructions

The determination of atomic structures in surface reconstructions has typically relied on structural models derived from intuition and domain knowledge. Evolutionary algorithms have emerged as powerful tools for such structure searches. However, when density functional theory is used to evaluate the energy the computational cost of a thorough exploration of the potential energy landscape is prohibitive. Here, we drive the exploration of the rich phase diagram of TiO x overlayer structures on SrTiO3(110) by combining the covariance matrix adaptation evolution strategy (CMA-ES) and a neural-network force field (NNFF) as a surrogate energy model. By training solely on SrTiO3(110) 4×1 overlayer structures and performing CMA-ES runs on 3×1, 4×1 and 5×1 overlayers, we verify the transferability of the NNFF. The speedup due to the surrogate model allows taking advantage of the stochastic nature of the CMA-ES to perform exhaustive sets of explorations and identify both known and new low-energy reconstructions.

Using SIR Epidemic Modeling and Control to Teach Process Dynamics and Control to Chemical Engineers

The COVID-19 pandemic has brought about unprecedented opportunities to introduce control systems topics in the undergraduate engineering curriculum. This paper describes two computer modeling assignments based on MATLAB with Simulink developed for CHE 461: Process Dynamics and Control taught at Arizona State University during the fall 2020 semester. A myriad of important concepts, among these dynamic modeling using conservation and accounting principles, linearization, state-space system and transfer function model representations, PID feedback control and Internal Model Control design can be applied to the problem and explained to students in the context of a significant world event representing a unique "process" system, notably the COVID-19 pandemic.

Theoretical calculations of formation and reactivity of o-quinomethide derivatives of resorcin[4]arene with reference to empirical data

This paper describes theoretical reaction pathways of alkoxybenzyl derivatives of resorcin[4]arene leading to the formation of o-quinomethide derivatives of resorcin[4]arene (o-QMR[4]A). For each case, the activation energies for the formation of one o-QMR[4]A unit and the activation energies for the backward reaction were calculated. Based on the calculated reaction pathways, the reaction mechanism of o-QMR[4]A formation was proposed. Using the example of o-QMR[4]A generated from a methoxy derivative of resorcin[4]arene, the activation energies with selected nucleophiles were calculated and the reaction mechanisms discussed. Reaction path calculations were performed using the nudged elastic band method and semiempirical extended tight-binding method (GFN2-xTB). Using hydroxybenzyl derivatives of resorcin[4]arene as an example, a comparison of calculated activation energies by selected density-functional theory methods with GFN2-xTB and B97-3c geometries was performed. B97-3c and wB97XD methods were used to calculate the energies of the reactants (R), transition states (TS) and products (P) of the analysed reactions. Theoretical reaction mechanisms were discussed with respect to the orbital-weighted Fukui dual descriptor (Δfw ) and experimental data.

Molecular design of redox carriers for electrochemical CO2 capture and concentration

Developing improved methods for CO₂ capture and concentration (CCC) is essential to mitigating the impact of our current emissions and can lead to carbon net negative technologies. Electrochemical approaches for CCC can achieve much higher theoretical efficiencies compared to the thermal methods that have been more commonly pursued. The use of redox carriers, or molecular species that can bind and release CO₂ depending on their oxidation state, is an increasingly popular approach as carrier properties can be tailored for different applications. The key requirements for stable and efficient redox carriers are discussed in the context of chemical scaling relationships and operational conditions. Computational and experimental approaches towards developing redox carriers with optimal properties are also described.

Review of Electrostatic Force Calculation Methods and Their Acceleration in Molecular Dynamics Packages Using Graphics Processors

Molecular dynamics (MD) simulations probe the conformational repertoire of macromolecular systems using Newtonian dynamic equations. The time scales of MD simulations allow the exploration of biologically relevant phenomena and can elucidate spatial and temporal properties of the building blocks of life, such as deoxyribonucleic acid (DNA) and protein, across microsecond (μs) time scales using femtosecond (fs) time steps. A principal bottleneck toward extending MD calculations to larger time scales is the long-range electrostatic force measuring component of the naive nonbonded force computation algorithm, which scales with a complexity of (N, number of atoms). In this review, we present various methods to determine electrostatic interactions in often-used open-source MD packages as well as the implementation details that facilitate acceleration of the electrostatic interaction calculation.

Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design

Inverse molecular design involves algorithms that sample molecules with specific target properties from a multitude of candidates and can be posed as an optimization problem. High-dimensional optimization tasks in the natural sciences are commonly tackled via population-based metaheuristic optimization algorithms such as evolutionary algorithms. However, often unavoidable expensive property evaluation can limit the widespread use of such approaches as the associated cost can become prohibitive. Herein, we present JANUS, a genetic algorithm inspired by parallel tempering. It propagates two populations, one for exploration and another for exploitation, improving optimization by reducing property evaluations. JANUS is augmented by a deep neural network that approximates molecular properties and relies on active learning for enhanced molecular sampling. It uses the SELFIES representation and the STONED algorithm for the efficient generation of structures, and outperforms other generative models in common inverse molecular design tasks achieving state-of-the-art target metrics across multiple benchmarks. As neither most of the benchmarks nor the structure generator in JANUS account for synthesizability, a significant fraction of the proposed molecules is synthetically infeasible demonstrating that this aspect needs to be considered when evaluating the performance of molecular generative models.

Chemoton 2.0: Autonomous Exploration of Chemical Reaction Networks

Fueled by advances in hardware and algorithm design, large-scale automated explorations of chemical reaction space have become possible. Here, we present our approach to an open-source, extensible framework for explorations of chemical reaction mechanisms based on the first-principles of quantum mechanics. It is intended to facilitate reaction network explorations for diverse chemical problems with a wide range of goals such as mechanism elucidation, reaction path optimization, retrosynthetic path validation, reagent design, and microkinetic modeling. The stringent first-principles basis of all algorithms in our framework is key for the general applicability that avoids any restrictions to specific chemical systems. Such an agile framework requires multiple specialized software components of which we present three modules in this work. The key module, Chemoton, drives the exploration of reaction networks. For the exploration itself, we introduce two new algorithms for elementary-step searches that are based on Newton trajectories. The performance of these algorithms is assessed for a variety of reactions characterized by a broad chemical diversity in terms of bonding patterns and chemical elements. Chemoton successfully recovers the vast majority of these. We provide the resulting data, including large numbers of reactions that were not included in our reference set, to be used as a starting point for further explorations and for future reference.

Potential of nanocomposites of zero valent copper and magnetite with Eleocharis dulcis biochar for packed column and batch scale removal of Congo red dye

The current study is the first attempt to prepare nanocomposites of Eleocharis dulcis biochar (EDB) with nano zero-valent Copper (nZVCu/EDB) and magnetite nanoparticles (MNPs/EDB) for batch and column scale sequestration of Congo Red dye (CR) from synthetic and natural water. The adsorbents were characterized with advanced analytical techniques. The impact of EDB, MNPs/EDB and nZVCu/EDB dosage (1-4 g/L), pH (4-10), initial concentration of CR (20-500 mg/L), interaction time (180 min) and material type to remove CR from water was examined at ambient temperature. The CR removal followed sequence of nZVCu/EDB > MNPs/EDB > EDB (84.9-98% > 77-95% > 69.5-93%) at dosage 2 g/L when CR concentration was increased from 20 to 500 mg/L. The MNPs/EDB and nZVCu/EDB showed 10.9% and 20.1% higher CR removal than EDB. The adsorption capacity of nZVCu/EDB, MNPs/EDB and EDB was 212, 193 and 174 mg/g, respectively. Freundlich model proved more suitable for sorption experiments while pseudo 2nd order kinetic model well explained the adsorption kinetics. Fixed bed column scale results revealed excellent retention of CR (99%) even at 500 mg/L till 2 h when packed column was filled with 3.0 g nZVCu/EDB, MNPs/EDB and EDB. These results revealed that nanocomposites with biochar can be applied efficiently for the decontamination of CR contaminated water.

Curiosity in exploring chemical spaces: Intrinsicrewards for molecular reinforcement learning

Computer aided design of molecules has the potential to disrupt the field of drug and material discovery. Machine learning and deep learning in particular, made big strides in recent years and promises to greatly benefit computer aided methods. Reinforcement learning is a particularly promising approach since it enables de novo molecule design, that is molecular design, without providing any prior knowledge. However, the search space is vast, and therefore any reinforcement learning agent needs to perform efficient exploration. In this study, we examine three versions of intrinsic motivation to aid efficient exploration. The algorithms are adapted from intrinsic motivation in the literature that were developed in other settings, predominantly video games. We show that the \textit{curious} agents finds better performing molecules on two of three benchmarks. This indicates an exciting new research direction for reinforcement learning agents that can explore the chemical space out of their own motivation. This has the potential to eventually lead to unexpected new molecular designs no human has thought about so far.

Graph-based molecular Pareto optimisation

Computer-assisted design of small molecules has experienced a resurgence in academic and industrial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate molecules that fulfil required chemical properties is encouraging, the use of deep learning models requires significant, if not prohibitive, amounts of data and computational power. At the same time, open-sourcing of more traditional techniques such as graph-based genetic algorithms for molecular optimisation [Jensen, Chem. Sci., 2019, 12, 3567-3572] has shown that simple and training-free algorithms can be efficient and robust alternatives. Further research alleviated the common genetic algorithm issue of evolutionary stagnation by enforcing molecular diversity during optimisation [Van den Abeele, Chem. Sci., 2020, 42, 11485-11491]. The crucial lesson distilled from the simultaneous development of deep generative models and advanced genetic algorithms has been the importance of chemical space exploration [Aspuru-Guzik, Chem. Sci., 2021, 12, 7079-7090]. For single-objective optimisation problems, chemical space exploration had to be discovered as a useable resource but in multi-objective optimisation problems, an exploration of trade-offs between conflicting objectives is inherently present. In this paper we provide state-of-the-art and open-source implementations of two generations of graph-based non-dominated sorting genetic algorithms (NSGA-II, NSGA-III) for molecular multi-objective optimisation. We provide the results of a series of benchmarks for the inverse design of small molecule drugs for both the NSGA-II and NSGA-III algorithms. In addition, we introduce the dominated hypervolume and extended fingerprint based internal similarity as novel metrics for these benchmarks. By design, NSGA-II, and NSGA-III outperform a single optimisation method baseline in terms of dominated hypervolume, but remarkably our results show they do so without relying on a greater internal chemical diversity.

Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction

Modern QM modelling methods, such as DFT, have provided detailed mechanistic insights into countless reactions. However, their computational cost inhibits their ability to rapidly screen large numbers of substrates and catalysts in reaction discovery. For a C-C bond forming nitro-Michael addition, we introduce a synergistic semi-empirical quantum mechanical (SQM) and machine learning (ML) approach that allows the prediction of DFT-quality reaction barriers in minutes, even on a standard laptop using widely available modelling software. Mean absolute errors (MAEs) are obtained that are below the accepted chemical accuracy threshold of 1 kcal mol-1 and substantially better than SQM methods without ML correction (5.71 kcal mol-1). Predictive power is shown to hold when the ML models are applied to an unseen set of compounds from the toxicology literature. Mechanistic insight is also achieved via the generation of full SQM transition state (TS) structures which are found to be very good approximations for the DFT-level geometries, revealing important steric interactions in some TSs. This combination of speed, accuracy, and mechanistic insight is unprecedented; current ML barrier models compromise on at least one of these important criteria.

Inverse molecular design of alkoxides and phenoxides for aqueous direct air capture of CO2

Aqueous direct air capture (DAC) is a key technology toward a carbon negative infrastructure. Developing sorbent molecules with water and oxygen tolerance and high CO₂ binding capacity is therefore highly desired. We analyze the CO₂ absorption chemistries on amines, alkoxides, and phenoxides with density functional theory calculations, and perform inverse molecular design of the optimal sorbent. The alkoxides and phenoxides are found to be more suitable for aqueous DAC than amines thanks to their water tolerance (lower pK_a prevents protonation by water) and capture stoichiometry of 1:1 (2:1 for amines). All three molecular systems are found to generally obey the same linear scaling relationship (LSR) between [Formula: see text] and [Formula: see text], since both CO₂ and proton are bonded to the nucleophilic (alkoxy or amine) binding site through a majorly [Formula: see text] bonding orbital. Several high-performance alkoxides are proposed from the computational screening. Phenoxides have comparatively poorer correlation between [Formula: see text] and [Formula: see text], showing promise for optimization. We apply a genetic algorithm to search the chemical space of substituted phenoxides for the optimal sorbent. Several promising off-LSR candidates are discovered. The most promising one features bulky ortho substituents forcing the CO₂ adduct into a perpendicular configuration with respect to the aromatic ring. In this configuration, the phenoxide binds CO₂ and a proton using different molecular orbitals, thereby decoupling the [Formula: see text] and [Formula: see text]. The [Formula: see text] trend and off-LSR behaviors are then confirmed by experiments, validating the inverse molecular design framework. This work not only extensively studies the chemistry of the aqueous DAC, but also presents a transferrable computational workflow for understanding and optimization of other functional molecules.

Approaches for enhancing the analysis of chemical space for drug discovery

INTRODUCTION

Chemical space is a powerful, general, and practical conceptual framework in drug discovery and other areas in chemistry that addresses the diversity of molecules and it has various applications. Moreover, chemical space is a cornerstone of chemoinformatics as a scientific discipline. In response to the increase in the set of chemical compounds in databases, generators of chemical structures, and tools to calculate molecular descriptors, novel approaches to generate visual representations of chemical space in low dimensions are emerging and evolving. Such approaches include a wide range of commercial and free applications, software, and open-source methods.

AREAS COVERED

The current state of chemical space in drug design and discovery is reviewed. The topics discussed herein include advances for efficient navigation in chemical space, the use of this concept in assessing the diversity of different data sets, exploring structure-property/activity relationships for one or multiple endpoints, and compound library design. Recent advances in methodologies for generating visual representations of chemical space have been highlighted, thereby emphasizing open-source methods.

EXPERT OPINION

Quantitative and qualitative generation and analysis of chemical space require novel approaches for handling the increasing number of molecules and their information available in chemical databases (including emerging ultra-large libraries). In addition, it is of utmost importance to note that chemical space is a conceptual framework that goes beyond visual representation in low dimensions. However, the graphical representation of chemical space has several practical applications in drug discovery and beyond.

Long-ranged heterogeneous structure in aqueous solutions of the deep eutectic solvent choline and geranate at the liquid-vapor interface

The deep eutectic solvent choline and geranate (CAGE) has shown promise in many therapeutic applications. CAGE facilitates drug delivery through unique modes of action making it an exciting therapeutic option. We examine the behavior of aqueous CAGE solutions at a liquid-vapor interface. We find that the liquid-vapor interface induces large oscillations in the density, which corresponds to spontaneous segregation into regions enriched with geranate and geranic acid and other regions enriched with water and choline. These heterogeneities are observed to extend nanometers into the liquid. Additionally, we find that the geranate and geranic acid orient so that their polar carboxyl or carboxylate groups are on average pointed toward the layer containing water and choline. Finally, we report surface tension and thermal expansion coefficients for various concentrations of aqueous CAGE. We find a non-monotonic trend in the surface tension with concentration. The structural and thermodynamic properties we report provide a new perspective on CAGE behavior, which helps deduce the action of CAGE in more sophisticated systems and inspire other studies and applications of CAGE and related materials.

Bio-inspired chemical space exploration of terpenoids

Many computational methods are devoted to rapidly generating pseudo-natural products to expand the open-ended border of chemical spaces for natural products. However, the accessibility and chemical interpretation were often ignored or underestimated in conventional library/fragment-based or rule-based strategies, thus hampering experimental synthesis. Herein, a bio-inspired strategy (named TeroGen) is developed to mimic the two key biosynthetic stages (cyclization and decoration) of terpenoid natural products, by utilizing physically based simulations and deep learning models, respectively. The precision and efficiency are validated for different categories of terpenoids, and in practice, more than 30 000 sesterterpenoids (10 times as many as the known sesterterpenoids) are predicted to be linked in a reaction network, and their synthetic accessibility and chemical interpretation are estimated by thermodynamics and kinetics. Since it could not only greatly expand the chemical space of terpenoids but also numerate plausible biosynthetic routes, TeroGen is promising for accelerating heterologous biosynthesis, bio-mimic and chemical synthesis of complicated terpenoids and derivatives.

Pareto Optimization of Oligomer Polarizability and Dipole Moment Using a Genetic Algorithm

High-performance electronic components are highly sought after in order to produce increasingly smaller and cheaper electronic devices. Drawing inspiration from inorganic dielectric materials, in which both polarizability and polarization contribute, organic materials can also maximize both. For a large set of small molecules drawn from PubChem, a Pareto-like front appears between the polarizability and dipole moment, indicating the presence of an apparent trade-off between these two properties. We tested this balance in π-conjugated materials by searching for novel conjugated hexamers with simultaneously large polarizabilities and dipole moments with potential use for dielectric materials. Using a genetic algorithm (GA) screening technique in conjunction with an approximate density functional tight-binding method for property calculations, we were able to efficiently search chemical space for optimal hexamers. Given the scope of chemical space, using the GA technique saves considerable time and resources by speeding up molecular searches compared to a systematic search. We also explored the underlying structure-function relationships, including sequence and monomer properties, that characterize large polarizability and dipole moment regimes.

Triazoles and Their Derivatives: Chemistry, Synthesis, and Therapeutic Applications

Among the nitrogen-containing heterocyclic compounds, triazoles emerge with superior pharmacological applications. Structurally, there are two types of five-membered triazoles: 1,2,3-triazole and 1,2,4-triazole. Due to the structural characteristics, both 1,2,3- and 1,2,4-triazoles are able to accommodate a broad range of substituents (electrophiles and nucleophiles) around the core structures and pave the way for the construction of diverse novel bioactive molecules. Both the triazoles and their derivatives have significant biological properties including antimicrobial, antiviral, antitubercular, anticancer, anticonvulsant, analgesic, antioxidant, anti-inflammatory, and antidepressant activities. These are also important in organocatalysis, agrochemicals, and materials science. Thus, they have a broad range of therapeutic applications with ever-widening future scope across scientific disciplines. However, adverse events such as hepatotoxicity and hormonal problems lead to a careful revision of the azole family to obtain higher efficacy with minimum side effects. This review focuses on the structural features, synthesis, and notable therapeutic applications of triazoles and related compounds.

Organic Photoredox Catalysts for CO2 reduction: Driving Discovery with Genetic Algorithms

This work implements a genetic algorithm (GA) to discover organic catalysts for photoredox CO2 reduction that are both highly active and resistant to degradation. The LUMO energy of the ground state catalyst is chosen as the activity descriptor and average Mulliken charge on all ring carbons as the descriptor for resistance to degradation via carboxylation (both obtained using density functional theory), to construct the fitness function of the GA. We combine the results of multiple GA runs, each based on different relative weighting of the two descriptors, and rigorously assess GA performance by calculating electron transfer barriers to CO2 reduction. A large majority of GA predictions exhibit improved performance relative to experimentally studied o-, m-, and p-terphenyl catalysts. Based on stringent cut-offs imposed on the average charge, barrier to electron transfer to CO2, and excitation energy, we recommend 25 catalysts for further experimental investigation of viability towards photoredox CO2 reduction.

Systemic evolutionary chemical space exploration for drug discovery

Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.