Theoretical Study of Natural Product Biosynthesis Using Computational Chemistry.

The biosynthetic pathways of natural products are complicated, and it is difficult to fully elucidate their details using experimental chemistry alone. In recent years, efforts have been made to elucidate the biosynthetic reaction mechanisms by combining computational and experimental methods. In this review, we will discuss the biosynthetic studies using computational chemistry for various terpene compounds such as cyclooctatin, sesterfisherol, quiannulatene, trichobrasilenol, asperterpenol, preasperterpenoid, spiroviolene, and mangicol.

Computational Chemistry Strategies to Investigate the Antioxidant Activity of Flavonoids-An Overview.

Despite several decades of research, the beneficial effect of flavonoids on health is still enigmatic. Here, we focus on the antioxidant effect of flavonoids, which is elementary to their biological activity. A relatively new strategy for obtaining a more accurate understanding of this effect is to leverage computational chemistry. This review systematically presents various computational chemistry indicators employed over the past five years to investigate the antioxidant activity of flavonoids. We categorize these strategies into five aspects: electronic structure analysis, thermodynamic analysis, kinetic analysis, interaction analysis, and bioavailability analysis. The principles, characteristics, and limitations of these methods are discussed, along with current trends.

Transacylation and hydrolysis of the acyl glucuronides of ibuprofen and its α-methyl-substituted analogues investigated by 1H NMR spectroscopy and computational chemistry: Implications for drug design.

Drugs and drug metabolites containing a carboxylic-acid moiety can undergo in vivo conjugation to form 1-ß-O-acyl-glucuronides (1-ß-O-AGs). In addition to hydrolysis, these conjugates can undergo spontaneous acyl migration, and anomerisation reactions, resulting in a range of positional isomers. Facile transacylation has been suggested as a mechanism contributing to the toxicity of acyl glucuronides, with the kinetics of these processes thought to be a factor. Previous 1H NMR spectroscopic and HPLC-MS studies have been conducted to measure the degradation rates of the 1-ß-O-AGs of three nonsteroidal anti-inflammatory drugs (ibufenac, R-ibuprofen, S-ibuprofen) and a dimethyl-analogue (termed here as "bibuprofen"). These studies have also determined the relative contributions of hydrolysis and acyl migration in both buffered aqueous solution, and human plasma. Here, a detailed kinetic analysis is reported, providing the individual rate constants for the acyl migration and hydrolysis reactions observed in buffer for each of the 4 AGs, together with the overall degradation rate constants of the parent 1-ß-O-AGs. Computational modelling of the reactants and transition states of the transacylation reaction using density functional theory indicated differences in the activation energies that reflected the influence of both substitution and stereochemistry on the rate of transacylation/hydrolysis.

Computational Organic Chemistry: The Frontier for Understanding and Designing Bioorthogonal Cycloadditions.

Computational organic chemistry has become a valuable tool in the field of bioorthogonal chemistry, offering insights and aiding in the progression of this branch of chemistry. In this review, I present an overview of computational work in this field, including an exploration of both the primary computational analysis methods used and their application in the main areas of bioorthogonal chemistry: (3 + 2) and [4 + 2] cycloadditions. In the context of (3 + 2) cycloadditions, detailed studies of electronic effects have informed the evolution of cycloalkyne/1,3-dipole cycloadditions. Through computational techniques, researchers have found ways to adjust the electronic structure via hyperconjugation to enhance reactions without compromising stability. For [4 + 2] cycloadditions, methods such as distortion/interaction analysis and energy decomposition analysis have been beneficial, leading to the development of bioorthogonal reactants with improved reactivity and the creation of orthogonal reaction pairs. To conclude, I touch upon the emerging fields of cheminformatics and machine learning, which promise to play a role in future reaction discovery and optimization.

Exploring Geometrical, Electronic and Spectroscopic Properties of 2-Nitroimidazole-Based Radiopharmaceuticals via Computational Chemistry Methods.

Tumor hypoxia plays an important role in the clinical management and treatment planning of various cancers. The use of 2-nitroimidazole-based radiopharmaceuticals has been the most successful for positron emission tomography (PET) and single photon emission computed tomography (SPECT) imaging probes, offering noninvasive means to assess tumor hypoxia. In this study we performed detailed computational investigations of the most used compounds for PET imaging, focusing on those derived from 2-nitroimidazole: fluoromisonidazole (FMISO), fluoroazomycin arabinoside (FAZA), fluoroetanidazole (FETA), fluoroerythronitroimidazole (FETNIM) and 2-(2-nitroimidazol-1-yl)-N-(2,2,3,3,3-pentafluoropropyl)acetamide (EF5). Conformational analysis, structural parameters, vibrational IR and Raman properties (within both harmonic and anharmonic approximations), as well as the NMR shielding tensors and spin-spin coupling constants were obtained by density functional theory (DFT) calculations and then correlated with experimental findings, where available. Furthermore, time-dependent DFT computations reveal insight into the excited states of the compounds. Our results predict a significant change in the conformational landscape of most of the investigated compounds when transitioning from the gas phase to aqueous solution. According to computational data, the 2-nitroimidazole moiety determines to a large extent the spectroscopic properties of its derivatives. Due to the limited structural information available in the current literature for the investigated compounds, the findings presented herein deepen the current understanding of the electronic structures of these five radiopharmaceuticals.

Molecule auto-correction to facilitate molecular design.

Ensuring that computationally designed molecules are chemically reasonable is at best cumbersome. We present a molecule correction algorithm that morphs invalid molecular graphs into structurally related valid analogs. The algorithm is implemented as a tree search, guided by a set of policies to minimize its cost. We showcase how the algorithm can be applied to molecular design, either as a post-processing step or as an integral part of molecule generators.

Molecular Contrastive Pretraining with Collaborative Featurizations.

Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery. Recently, prosperous progress has been made in molecular pretraining with different molecular featurizations, including 1D SMILES strings, 2D graphs, and 3D geometries. However, the role of molecular featurizations with their corresponding neural architectures in molecular pretraining remains largely unexamined. In this paper, through two case studies─chirality classification and aromatic ring counting─we first demonstrate that different featurization techniques convey chemical information differently. In light of this observation, we propose a simple and effective MOlecular pretraining framework with COllaborative featurizations (MOCO). MOCO comprehensively leverages multiple featurizations that complement each other and outperforms existing state-of-the-art models that solely rely on one or two featurizations on a wide range of molecular property prediction tasks.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives.

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Monospecific and ultrasensitive detection of ofloxacin: A computational chemistry-assisted hapten screening strategy and analysis of molecular recognition mechanism.

Contamination in food and the environment with fluoroquinolones (FQs) has become a serious threat to the global ecological balance and public health safety. Ofloxacin (OFL) is one of the most widely utilized sterilization agents in FQs. In the process of monitoring OFL, broad-spectrum monoclonal antibodies (mAb) cannot meet the demand for monospecific detection. Here, a computational chemistry-assisted hapten screening strategy was proposed in this study. Differences in the properties of antigenic epitopes were precisely extracted through a comprehensive comparative study of 16 common FQs molecules and a monospecific and ultrasensitive mAb-3B4 for OFL was successfully prepared. The screened fleroxacin (FLE) hapten was applied in a heterologous competition strategy resulting in a 20-fold improvement in the half inhibitory concentration (IC50) of mAb-3B4 to 0.0375 µg L-1 and cross-reacted only with marbofloxacin (MAR) in regulated FQs. In addition, a single-chain variable fragment (scFv) for OFL was constructed for the first time with an IC50 of 0.378 µg L-1. Molecular recognition mechanism studies validated the reliability of this strategy and revealed the key amino acid sites responsible for OFL specificity and sensitivity. Finally, ic-ELISA and GICA were established for OFL in real samples. This work provides new ideas for the preparation of monospecific mAb and improves the monitoring system of FQs.

The comparison of the speed of solving chemistry calculation tasks in the traditional way and with the use of ICT.

Efficiency of time use is a key factor in chemistry calculation tasks, affecting both, personal and professional domains. This study is dedicated to finding the fastest methods for accomplishing chemistry tasks. Our investigation delves into the comparative temporal outlays made by students as they engage three different approaches: using an electronic calculator, a basic calculator app on a smartphone, and a desktop computer calculator. As part of our research, we examine a cohort of 52 Slovenian university students, preservice teachers who were actively enrolled in chemistry and related science programs, spanning the academic years of 2019 and 2022.  The results from 2019 show that students can solve the chemistry tasks most quickly using electronic calculator and take the most time to calculate the tasks using smartphones (Δmean = 133 s; ΔSD = 5 s; Δmin = 97 s; Δmax = 131 s). An even larger difference is observed from the 2022 study year (Δmean = 189 s; ΔSD = 129 s; Δmin = 170 s; Δmax = 625 s). In summary, although smartphones are recognised as a multitasking device, replacing traditional single-purpose devices, they have not been able to outperform them.

Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-ß Case Study.

Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the ID4Inno drug discovery platform, featuring innovative artificial intelligence, high-accuracy computational chemistry, and high-performance cloud computing. The workflow was validated by discovering a few potent hit compounds (best IC50 is ∼0.80 µM) against PI5P4K-ß, a novel anti-cancer target. Furthermore, by applying the tools implemented in ID4Inno, we managed to optimize these hit compounds and finally obtained five hit series with different scaffolds, all of which showed high activity against PI5P4K-ß. These results demonstrate the effectiveness of ID4inno in driving hit identification based on artificial intelligence, computational chemistry, and cloud computing.

Extension of the SUGRES-1P Coarse-Grained Model of Polysaccharides to Heparin.

Heparin is an unbranched periodic polysaccharide composed of negatively charged monomers and involved in key biological processes, including anticoagulation, angiogenesis, and inflammation. Its structure and dynamics have been studied extensively using experimental as well as theoretical approaches. The conventional approach of computational chemistry applied to the analysis of biomolecules is all-atom molecular dynamics, which captures the interactions of individual atoms by solving Newton's equation of motion. An alternative is molecular dynamics simulations using coarse-grained models of biomacromolecules, which offer a reduction of the representation and consequently enable us to extend the time and size scale of simulations by orders of magnitude. In this work, we extend the UNIfied COarse-gRaiNed (UNICORN) model of biological macromolecules developed in our laboratory to heparin. We carried out extensive tests to estimate the optimal weights of energy terms of the effective energy function as well as the optimal Debye-Hückel screening factor for electrostatic interactions. We applied the model to study unbound heparin molecules of polymerization degree ranging from 6 to 68 residues. We compare the obtained coarse-grained heparin conformations with models obtained from X-ray diffraction studies of heparin. The SUGRES-1P force field was able to accurately predict the general shape and global characteristics of heparin molecules.

PyConSolv: A Python Package for Conformer Generation of (Metal-Containing) Systems in Explicit Solvent.

We introduce PyConSolv, a freely available Python package that automates the generation of conformers of metal- and nonmetal-containing complexes in explicit solvent, through classical molecular dynamics simulations. Using a streamlined workflow and interfacing with widely used computational chemistry software, PyConSolv is an all-in-one tool for the generation of conformers in any solvent. Input requirements are minimal; only the geometry of the structure and the desired solvent in xyz (XMOL) format are needed. The package can also account for charged systems, by including arbitrary counterions in the simulation. A bonded model parametrization is performed automatically, utilizing AmberTools, ORCA, and Multiwfn software packages. PyConSolv provides a selection of preparametrized solvents and counterions for use in classical molecular dynamics simulations. We show the applicability of our package on a number of (transition-metal-containing) systems. The software is provided open source and free of charge.

Open-Source Machine Learning in Computational Chemistry.

The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.

Molecular mechanisms of caramel-like odorant-olfactory receptor interactions based on a computational chemistry approach.

Molecular mechanisms of caramel-like odorant-olfactory receptor interactions were investigated based on molecular docking and molecular dynamics simulations. The transmembrane regions TM-3, TM-5 and TM-6 of receptors were main contributors of amino acid residues in the docking. Molecular docking results showed that hydrogen bonding and pi-pi stacking were the key forces for the stabilization of caramel-like odorants. The binding energies were positively correlated with the molecular weight of caramel-like odorants. Residues Asn155 (84%, OR2W1), Asn206 (86%, OR8D1), Ser155 (77%, OR8D1), Asp179 (87%, OR5M3), Val182 (84%, OR2J2) and Tyr260 (94%, OR2J2) with high frequencies played an important role in the complexes formation. Odorants 4-hydroxy-5-methylfuran-3(2H)-one (16#) and methylglyoxal (128#) were screened by molecular field-based similarity analysis, which tended to bind to the receptors OR1G1 and OR52H1 respectively, resulting a caramel-like aroma perception. The obtained results are useful for better understanding the perception of caramel-like odorants and their high-throughput screening.

Exploring the distinct conformational preferences of allyl ethyl ether and allyl ethyl sulfide using rotational spectroscopy and computational chemistry.

The conformational energy landscapes of allyl ethyl ether (AEE) and allyl ethyl sulfide (AES) were investigated using Fourier transform microwave spectroscopy in the frequency range of 5-23 GHz aided by density functional theory B3LYP-D3(BJ)/aug-cc-pVTZ calculations. The latter predicted highly competitive equilibria for both species, including 14 unique conformers of AEE and 12 for the sulfur analog AES within 14 kJ mol-1. The experimental rotational spectrum of AEE was dominated by transitions arising from its three lowest energy conformers, which differ in the arrangement of the allyl side chain, while in AES, transitions due to the two most stable forms, distinct in the orientation of the ethyl group, were observed. Splitting patterns attributed to methyl internal rotation were analyzed for AEE conformers I and II, and the corresponding V3 barriers were determined to be 12.172(55) and 12.373(32) kJ mol-1, respectively. The experimental ground state geometries of both AEE and AES were derived using the observed rotational spectra of the 13C and 34S isotopic species and are highly dependent on the electronic properties of the linking chalcogen (oxygen vs sulfur). The observed structures are consistent with a decrease in hybridization in the bridging atom from oxygen to sulfur. The molecular-level phenomena that drive the conformational preferences are rationalized through natural bond orbital and non-covalent interaction analyses. These show that interactions involving the lone pairs on the chalcogen atom with the organic side chains favor distinct geometries and energy orderings for the conformers of AEE and AES.

PAE viewer: a webserver for the interactive visualization of the predicted aligned error for multimer structure predictions and crosslinks.

The development of AlphaFold for protein structure prediction has opened a new era in structural biology. This is even more the case for AlphaFold-Multimer for the prediction of protein complexes. The interpretation of these predictions has become more important than ever, but it is difficult for the non-specialist. While an evaluation of the prediction quality is provided for monomeric protein predictions by the AlphaFold Protein Structure Database, such a tool is missing for predicted complex structures. Here, we present the PAE Viewer webserver (http://www.subtiwiki.uni-goettingen.de/v4/paeViewerDemo), an online tool for the integrated visualization of predicted protein complexes using a 3D structure display combined with an interactive representation of the Predicted Aligned Error (PAE). This metric allows an estimation of the quality of the prediction. Importantly, our webserver also allows the integration of experimental cross-linking data which helps to interpret the reliability of the structure predictions. With the PAE Viewer, the user obtains a unique online tool which for the first time allows the intuitive evaluation of the PAE for protein complex structure predictions with integrated crosslinks.

Bioinformatics and computational chemistry approaches to explore the mechanism of the anti-depressive effect of ligustilide.

Depression affects people with multiple adverse outcomes, and the side effects of antidepressants are troubling for depression sufferers. Aromatic drugs have been widely used to relieve symptoms of depression with fewer side effects. Ligustilide (LIG) is the main component of volatile oil in angelica sinensis, exhibiting an excellent anti-depressive effect. However, the mechanisms of the anti-depressive effect of LIG remain unclear. Therefore, this study aimed to explore the mechanisms of LIG exerting an anti-depressive effect. We obtained 12,969 depression-related genes and 204 LIG targets by a network pharmacology approach, which were intersected to get 150 LIG anti-depressive targets. Then, we identified core targets by MCODE, including MAPK3, EGF, MAPK14, CCND1, IL6, CASP3, IL2, MYC, TLR4, AKT1, ESR1, TP53, HIF1A, SRC, STAT3, AR, IL1B, and CREBBP. Functional enrichment analysis of core targets showed a significant association with PI3K/AKT and MAPK signaling pathways. Molecular docking showed strong affinities of LIG with AKT1, MAPK14, and ESR1. Finally, we validated the interactions between these proteins and LIG by molecular dynamics (MD) simulations. In conclusion, this study successfully predicted that LIG exerted an anti-depressive effect through multiple targets, including AKT1, MAPK14, and ESR1, and the pathways of PI3K/AKT and MAPK. The study provides a new strategy to explore the molecular mechanisms of LIG in treating depression.

Deciphering and investigating fragment mechanism of quinolones using multi-collision energy mass spectrometry and computational chemistry strategy.

RATIONALE: Quinolones show characteristic fragments in mass spectrometry (MS) analysis due to their common core structures, and energy-dependent differences among these fragments are generated through the same fragmentation pathway of different molecules. Computational chemistry, which provides quantitative results of molecule parameters, is helpful for investigating the mechanisms of chemistry. METHODS: MS/MS spectra of five quinolones, namely norfloxacin (NOR), enoxacin (ENO), enrofloxacin (ENR), gatifloxacin (GAT), and lomefloxacin (LOM), were acquired for deciphering fragmentation pathways under multi-collision energy (CE). Computational methods were used for excluding little possibility pathways from the point of view of energy and stable conformations, whereas optimized collision energy (OCE) and maximum relative intensity (MRI) of major competitive fragments were investigated and confirmed using computational results. RESULTS: Fragmentation results of NOR, ENO, ENR, and GAT were deciphered using experimental and computational data, of which fragmentation regularities were summarized. Fragmentation pathways of LOM were deciphered under the guidance of foregoing regularities. Meanwhile, the whole process was validated by comparing OCE and MRI and computational energy results, which showed good agreement. CONCLUSIONS: A strategy for explaining quinolone fragmentation results of multi-CE values and deciphering fragment mechanism using computational methods was developed. Relevant data and strategy may provide ideas for how to design and decipher new drug molecules with similar structures.

Merging cultures and disciplines to create a drug discovery ecosystem at Virginia commonwealth university: Medicinal chemistry, structural biology, molecular and behavioral pharmacology and computational chemistry.

The Department of Medicinal Chemistry, together with the Institute for Structural Biology, Drug Discovery and Development, at Virginia Commonwealth University (VCU) has evolved, organically with quite a bit of bootstrapping, into a unique drug discovery ecosystem in response to the environment and culture of the university and the wider research enterprise. Each faculty member that joined the department and/or institute added a layer of expertise, technology and most importantly, innovation, that fertilized numerous collaborations within the University and with outside partners. Despite moderate institutional support with respect to a typical drug discovery enterprise, the VCU drug discovery ecosystem has built and maintained an impressive array of facilities and instrumentation for drug synthesis, drug characterization, biomolecular structural analysis and biophysical analysis, and pharmacological studies. Altogether, this ecosystem has had major impacts on numerous therapeutic areas, such as neurology, psychiatry, drugs of abuse, cancer, sickle cell disease, coagulopathy, inflammation, aging disorders and others. Novel tools and strategies for drug discovery, design and development have been developed at VCU in the last five decades; e.g., fundamental rational structure-activity relationship (SAR)-based drug design, structure-based drug design, orthosteric and allosteric drug design, design of multi-functional agents towards polypharmacy outcomes, principles on designing glycosaminoglycans as drugs, and computational tools and algorithms for quantitative SAR (QSAR) and understanding the roles of water and the hydrophobic effect.