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.

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.

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.

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.

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.

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.

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.

Quasiclassical Trajectory Simulation as a Protocol to Build Locally Accurate Machine Learning Potentials.

Direct trajectory calculations have become increasingly popular in recent computational chemistry investigations. However, the exorbitant computational cost of ab initio trajectory calculations usually limits its application in mechanistic explorations. Recently, machine learning-based potential energy surface (ML-PES) provides a powerful strategy to circumvent the heavy computational cost and meanwhile maintain the required accuracy. Despite the appealing potential, constructing a robust ML-PES is still challenging since the training set of the PES should cover a broad enough configuration space. In this work, we demonstrate that when the concerned properties could be collected by the localized sampling of the configuration space, quasiclassical trajectory (QCT) calculations can be invoked to efficiently obtain locally accurate ML-PESs. We prove our concept with two model reactions: methyl migration of i-pentane cation and dimerization of cyclopentadiene. We found that the locally accurate ML-PESs are sufficiently robust for reproducing the static and dynamic features of the reactions, including the time-resolved free energy and entropy changes, and time gaps.

Machine Learning and Computational Chemistry for the Endocannabinoid System.

Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.

Investigation of the substituted-titanium nanocages using computational chemistry.

We are investigated substitution effects of titanium heteroatoms on band gap, charge and local reactivity of C20-nTin heterofullerenes (n = 1-5), at different levels and basis sets. The C18Ti2-2 nanocage is considered as the most kinetically stable species with the widest band gap of 2.86 eV, in which two carbon atoms are substituted by two Ti atoms in equatorial position, individually. The charges on carbon atoms of C20 are roughly zero, while high positive charge (1.256) on the surface of C19Ti1 prompts this heteofullerene for hydrogen storage. The positive atomic charge on Ti atoms and negative atomic charge on their adjacent C atoms implies that these sites can be influenced more readily by nucleophilic and electrophilic regents, respectively. We examined the usefulness of local reactivity descriptors to predict the reactivity of Ti-C atomic sites on the external surface of the heterofullerenes. The properties determined include Fukui function (F.F.); f (k) and local softness s (k) on the surfaces of the investigated hollow cages. Geometry optimization results reveal that titanium atoms can be comfortably incorporated into the CC network of fullerene. It is most likely associated with the triple-coordination characteristic of titanium atoms, which can well match with the sp2-hybridized carbon bonding structure. According to the values of f (k) and s (k) for the C15Ti5 heterofullerene; the carbon atoms in the cap regions exhibit a different reactivity pattern than those in the equatorial portion of the heterofullerene. The titanium impurity can significantly improve the fullerene's surface reactivity and it allows controlling their surface properties. The band gap of C20-nTin …..(H2)n structures is decreased with increasing n. Hence, C15Ti5 is found as the best hydrogen adsorbent.

Computational Chemistry and Molecular Modeling of Reversible MAO Inhibitors.

Proper elucidation of drug-target interaction is one of the most significant steps at the early stages of the drug development research. Computer-aided drug design tools have substantial contribution to this stage. In this chapter, we specifically concentrate on the computational methods widely used to develop reversible inhibitors for monoamine oxidase (MAO) isozymes. In this context, current computational techniques in identifying the best drug candidates showing high potency are discussed. The protocols of structure-based drug design methodologies, namely, molecular docking, in silico screening, and molecular dynamics simulations, are presented. Employing case studies of safinamide binding to MAO B, we demonstrate how to use AutoDock 4.2.6 and NAMD software packages.