Quantum-Chemical Descriptors in QSAR/QSPR Studies

Investigation of in silico studies for cytochrome P450 isoforms specificity

Cytochrome P450 (CYP450) enzymes comprise a highly diverse superfamily of heme-thiolate proteins that responsible for catalyzing over 90 % of enzymatic reactions associated with xenobiotic metabolism in humans. Accurately predicting whether chemicals are substrates or inhibitors of different CYP450 isoforms can aid in pre-selecting hit compounds for the drug discovery process, chemical toxicology studies, and patients treatment planning. In this work, we investigated in silico studies on CYP450s specificity over past twenty years, categorizing these studies into structure-based and ligand-based approaches. Subsequently, we utilized 100 of the most frequently prescribed drugs to test eleven machine learning-based prediction models which were published between 2015 and 2024. We analyzed various aspects of the evaluated models, such as their datasets, algorithms, and performance. This will give readers with a comprehensive overview of these prediction models and help them choose the most suitable one to do prediction. We also provide our insights for future research trend in both structure-based and ligand-based approaches in this field.

Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors

Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental in this transformation is Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided drug design tool uses machine learning to predict the biological activity of new compounds based on the numerical representation of chemical structures against various biological targets. With diabetes mellitus becoming a significant health challenge in recent times, there is intense research interest in modulating antidiabetic drug targets. α-Glucosidase is an antidiabetic target that has gained attention due to its ability to suppress postprandial hyperglycaemia, a key contributor to diabetic complications. This review explored a detailed approach to developing QSAR models, focusing on strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical machine learning algorithms to modern deep learning algorithms. We also highlighted studies that have used these approaches to develop predictive models for α-glucosidase inhibitors to modulate this critical antidiabetic drug target.

Experimental insights and modeling innovations: Deciphering Fe(VI) oxidation in imidazole ionic liquids through QSAR and RFR

In this investigation, we conducted a detailed analysis of the oxidation of 16 imidazole ionic liquid variants by Fe(VI) under uniform experimental setups, thereby securing a dataset of second-order reaction rate constants (kobs). This methodology ensures superior data consistency and comparability over traditional methods that amalgamate disparate data from varied studies. Utilizing 16 chemical structural parameters obtained via Density Functional Theory (DFT) as descriptors, we developed a Quantitative Structure Activity Relationship (QSAR) model. Through rigorous correlation analysis, Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Applicability Domain (AD) evaluation, we identified a pronounced negative correlation between the molecular orbital gap energy (Egap) and kobs. MLR analysis further underscored Egap as a pivotal predictive variable, with its lower values indicating heightened oxidative reactivity towards Fe(VI) in the ionic liquids, leading the QSAR model to achieve a predictive accuracy of 0.95. Furthermore, we integrated an advanced machine learning approach - Random Forest Regression (RFR), which adeptly highlighted the critical factors influencing the oxidation efficiency of imidazole ionic liquids by Fe(VI) through elaborate decision trees, feature importance assessment, Recursive Feature Elimination (RFE), and cross-validation strategies. The RFR model demonstrated a remarkable predictive performance of 0.98. Both QSAR and RFR models pinpointed Egap as a key descriptor significantly affecting oxidation efficiency, with the RFR model presenting lower root mean square errors, establishing it as a more reliable predictive tool. The application of the RFR model in this study significantly improved the model's stability and the intuitive display of key influencing factors, introducing promising advanced analytical tools to the field of environmental chemistry.

New Species and Cytotoxicity Mechanism of Halohydroxybenzonitrile Disinfection Byproducts in Drinking Water

Recently, seven dihalohydroxybenzonitriles (diHHBNs) have been determined as concerning nitrogenous aromatic disinfection byproducts (DBPs) in drinking water. Herein, eight new monohalohydroxybenzonitriles (monoHHBNs), including 3-chloro-2-hydroxybenzonitrile, 5-chloro-2-hydroxybenzonitrile, 3-chloro-4-hydroxybenzonitrile, 3-bromo-2-hydroxybenzonitrile, 5-bromo-2-hydroxybenzonitrile, 3-bromo-4-hydroxybenzonitrile, 5-iodo-2-hydroxybenzonitrile, and 3-iodo-4-hydroxybenzonitrile, were detected and identified in drinking water for the first time. Thereafter, the relative concentration-cytotoxicity contribution of each HHBN was calculated based on the acquired occurrence level and cytotoxicity data in this study, the genome-scale cytotoxicity mechanism was explored, and a quantitative structure-activity relationship (QSAR) model was developed. Results indicated that new monoHHBNs were present in drinking water at concentrations of 0.04-1.83 ng/L and exhibited higher cytotoxicity than some other monohalogenated aromatic DBPs. Notably, monoHHBNs showed concentration-cytotoxicity contribution comparable to diHHBNs, which have been previously identified as potential toxicity drivers in drinking water. Transcriptomic analysis revealed immunotoxicity and genotoxicity as dominant cytotoxicity mechanisms for HHBNs in Chinese hamster ovary (CHO-K1) cells, with potential carcinogenic effects. The QSAR model suggested oxidative stress and cellular uptake efficiency as important factors for their cytotoxicity, highlighting the importance of potential iodinated HHBNs in drinking water, such as 3,5-diiodo-2-hydroxybenzonitrile, for future studies. These findings are meaningful for better understanding the health risk and toxicological significance of HHBNs in drinking water.

Quantum Topological Atomic Properties of 44K molecules

We present a data set of quantum topological properties of atoms of 44K randomly selected molecules from the GDB-9 data set. These atomic properties were obtained as defined by the quantum theory of atoms in molecules (QTAIM) within an atomic basin, a region of real space bounded by zero-flux surfaces in the electron density gradient vector field. The wave function files were generated through DFT static calculations (B3LYP/6-31G), and the atomic properties were calculated using QTAIM. The calculated atomic properties include the energy of the atomic basin, the electronic population, the magnitude of the total dipole moment, and the magnitude of the total quadrupole moment. The atomic properties allow one to understand the chemical structure, reactivity, and molecular recognition. They can be incorporated into force fields for molecular dynamics or for predicting reactive sites. We believe that this data set could facilitate new studies in chemical informatics, machine learning applied to chemistry, and computational molecular design.

Efficient degradation of haloacetic acids by vacuum ultraviolet-activated peroxymonosulfate: Kinetics, mechanisms and theoretical calculations

Efficient degradation of haloacetic acids (HAAs) is crucial due to their potential risks. This study firstly proposed vacuum ultraviolet - activated peroxymonosulfate (VUV/PMS) to remove HAAs (i.e., monochloroacetic acid (MCAA), monobromoacetic acid (MBAA), dichloroacetic acid (DCAA), etc). VUV/PMS achieved 99.51 % MCAA and 63.29 % TOC removal within 10 min. Electron paramagnetic resonance (EPR), quenching and probe experiments demonstrated that •OH was responsible for MCAA degradation. MCAA degradation followed pathways of dehalogenation (major) and decarboxylation (minor). VUV/PMS showed application potential under various reaction parameters. Broad spectrum of VUV/PMS on various HAAs was further explored. Chlorinated HAAs (Cl-HAAs) were primarily degraded by oxidation reactions, while brominated HAAs (Br-HAAs) by direct VUV photolysis. The density functional theory-based calculations (DFT) revealed that reaction rates of HAAs correlated with the highest occupied molecular orbital (HOMO) and energy gap (ΔE), indicating that HAAs degradation depends on their chemical structures. The Fukui function (f0 values) and bond length showed vulnerability of the halogen atom in Cl-HAAs and C-Br bond in Br-HAAs. Overall, this study provides an in-depth perspective on the oxidation performance and mechanism of HAAs using VUV/PMS. It not only demonstrates a green and efficient method but also inspires new strategies for HAAs remediation.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

Crab shell-based hierarchical micro/meso-porous carbon as an efficient nano-adsorbent for CO2/CH4 separation: experiments and DFT modeling

In this study, we prepared a range of nanoporous carbon nano-adsorbents from crab shells (CSs) using KOH activation and evaluated their suitability for selective adsorption of CO2/CH4 gas mixtures. We employed various characterization techniques, including XRD, FT-IR, SEM, Raman, TGA, and BET analysis, to assess the properties of these nano-adsorbents. Our investigation includes the systematic study of various parameters, such as activation time, activation temperature, and the KOH to CS activating agent ratio. The nanoporous carbons were evaluated for their CO2 adsorption capabilities at 1-10 bar and 25 ℃ condition. The results demonstrated that the CS-2-2-900 sample, activated for 1 h at 900 ℃ with a 2:1 ratio of KOH to CS, exhibited the highest gas adsorption capacity, reaching 7.217 mmol/g at a pressure of 10 bar under room temperature conditions. Additionally, the synthesized CS-2-2-900 sample displayed excellent surface area (914.85 m2/g), a pore volume of 1.1 cm3/g, and an average pore diameter of 4.82 nm. Furthermore, we functionalized the CSs to enhance their selectivity for ammonia adsorption. Using the Myers and Pravnitz theory, we calculated that the FCS-2-2-900 sample exhibited the highest selectivity, reaching 18.99 at 25 ℃ under pressures of up to 10 bar. To gain a more comprehensive understanding of the interactions between the adsorbents and the adsorbed molecules, as well as to identify the active sites involved in the adsorption process, we employed density functional theory (DFT). Our DFT calculations revealed that pyrrolic nitrogen and carboxylic sites played a significant role in enhancing the separation of CO2 in binary mixtures. In summary, nanoporous carbons derived from crab shells outperformed those derived from other waste materials. These functionalized porous nanocarbons represent promising adsorbents for the selective adsorption of CO2 gas in CO2/CH4 mixtures due to their nitrogen content, high porosity, stability, and economic efficiency.

Data-science-guided calibration curve prediction of an MLCT-based ee determination assay for chiral amines

Circular dichroism (CD) based enantiomeric excess (ee) determination assays are optical alternatives to chromatographic ee determination in high-throughput screening (HTS) applications. However, the implementation of these assays requires calibration experiments using enantioenriched materials. We present a data-driven approach that circumvents the need for chiral resolution and calibration experiments for an octahedral Fe(II) complex (1) used for the ee determination of α-chiral primary amines. By computationally parameterizing the imine ligands formed in the assay conditions, a model of the circular dichroism (CD) response of the Fe(II) assembly was developed. Using this model, calibration curves were generated for four analytes and compared to experimentally generated curves. In a single-blind ee determination study, the ee values of unknown samples were determined within 9% mean absolute error, which rivals the error using experimentally generated calibration curves.

Quantum Computational and Spectroscopic Investigation of 3-aminosalicylic Acid

Quantum chemical calculations of 3-aminosalicylic acid (3ASA) (monomer and dimer forms) have been performed using DFT and TD-DFT theories with B3LYP/6-311 G(d,p) functional level in the ground and excited states. Using TD-DFT with IEF-PCM model, the electronic spectra of 3ASA in solvents were computed and correlated with the experimental data. The theoretically calculated absorption and emission maxima of 3ASA (monomer) are observed in the range of 343 - 347 nm (S0 → S1 transition) and 429 - 448 nm (S1 → S0 transition), respectively. The natural bond orbital (NBO) analysis shows that charge transfer interaction contributes significantly to stabilize the molecular system. In the case of dimer, hydrogen bonding plays a dominant role in stabilizing the molecular framework. Additionally, the obtained nonlinear optical (NLO) properties: polarizability (13.86 × 10-24 e.s.u for monomer and 29.46 × 10-24 e.s.u. for dimer), first-order hyperpolarizability (4.21 × 10-30 e.s.u for monomer and 0.18 × 10-30 e.s.u for dimer) and second-order hyperpolarizability (7.44 × 10-36 e.s.u. for monomer and 14.32 × 10-36 e.s.u. for dimer) were found to be larger than those of standard organic compounds suggesting that 3ASA has a significant NLO character for optoelectronic applications. The NLO properties of dimer may differ from monomer due to dimerization. Further, the radiative lifetime, light harvesting efficiency and band gap energy were calculated, and proposed that 3ASA may be useful in photovoltaics and wide bandgap power devices. HIGHLIGHTS: • DFT and TD-DFT theories were employed to calculate structural and molecular properties of 3ASA (monomer and dimer) in ground and excited states. • HOMO-LUMO study shows monomer and dimer of 3ASA are good reactive. • NBO analysis reflects that charge transfer interactions stabilized the 3ASA molecule. • Electronic absorption/emission spectra in solvents calculated by IEF-PCM/TD-DFT method correlate with experimental results. • Calculated NLO parameters suggested that 3ASA is a potential candidate for NLO material.

4D-QSAR, ADMET properties, and molecular dynamics simulations for designing N-substituted urea/thioureas as human glutaminyl cyclase inhibitors

Human glutaminyl cyclase (hQC) inhibitors have great potential to be used as anti- Alzheimer's disease (AD) agents by reducing the toxic pyroform of β-amyloid in the brains of AD patients. The four-dimensional quantitative structure activity relationship (4D-QSAR) model of N-substituted urea/thioureas was established with satisfying predictive ability and statistical reliability (Q2 = 0.521, R2 = 0.933, R2prep = 0.619). By utilizing the developed 4D-QSAR model, a set of new N-substituted urea/thioureas was designed and evaluated for their Absorption Distribution Metabolism Excretion and Toxicity (ADMET) properties. The results of molecular dynamics (MD) simulations, Principal component analysis (PCA), free energy landscape (FEL), dynamic cross-correlation matrix (DCCM) and molecular mechanics generalized Born Poisson-Boltzmann surface area (MM-PBSA) free energy calculations, revealed that the designed compounds were remained stable in protein binding pocket and compounds b ∼ f (-35.1 to -44.55 kcal/mol) showed higher binding free energy than that of compound 14 (-33.51 kcal/mol). The findings of this work will be a theoretical foundation for further research and experimental validation of urea/thiourea derivatives as hQC inhibitors.

Comparative optimization of polysaccharide-based nanoformulations for cardiac RNAi therapy

Ionotropic gelation is widely used to fabricate targeting nanoparticles (NPs) with polysaccharides, leveraging their recognition by specific lectins. Despite the fabrication scheme simply involves self-assembly of differently charged components in a straightforward manner, the identification of a potent combinatory formulation is usually limited by structural diversity in compound collections and trivial screen process, imposing crucial challenges for efficient formulation design and optimization. Herein, we report a diversity-oriented combinatory formulation screen scheme to identify potent gene delivery cargo in the context of precision cardiac therapy. Distinct categories of cationic compounds are tested to construct RNA delivery system with an ionic polysaccharide framework, utilizing a high-throughput microfluidics workstation coupled with streamlined NPs characterization system in an automatic, step-wise manner. Sequential computational aided interpretation provides insights in formulation optimization in a broader scenario, highlighting the usefulness of compound library diversity. As a result, the out-of-bag NPs, termed as GluCARDIA NPs, are utilized for loading therapeutic RNA to ameliorate cardiac reperfusion damages and promote the long-term prognosis. Overall, this work presents a generalizable formulation design strategy for polysaccharides, offering design principles for combinatory formulation screen and insights for efficient formulation identification and optimization.

Cytotoxicity of emerging halophenylacetamide disinfection byproducts in drinking water: Mechanism and prediction

Halophenylacetamides (HPAcAms) have been identified as a new group of nitrogenous aromatic disinfection byproducts (DBPs) in drinking water, but the toxicity mechanisms associated with HPAcAms remain almost completely unknown. In this work, the cytotoxicity of HPAcAms in human hepatoma (HepG2) cells was evaluated, intracellular oxidative stress/damage levels were analyzed, their binding interactions with antioxidative enzyme were explored, and a quantitative structure-activity relationship (QSAR) model was established. Results indicated that the EC50 values of HPAcAms ranged from 2353 μM to 9780 μM, and the isomeric structure as well as the type and number of halogen substitutions could obviously induce the change in the cytotoxicity of HPAcAms. Upon exposure to 2-(3,4-dichlorophenyl)acetamide (3,4-DCPAcAm), various important biomarkers linked to oxidative stress and damage, such as reactive oxygen species, 8‑hydroxy-2-deoxyguanosine, and cell apoptosis, exhibited a significant increase in a dose-dependent manner. Moreover, 3,4-DCPAcAm could directly bind with Cu/Zn-superoxide dismutase and induce the alterations in the structure and activity, and the formation of complexes was predominantly influenced by the van der Waals force and hydrogen bonding. The QSAR model supported that the nucleophilic reactivity as well as the molecular compactness might be highly important in their cytotoxicity mechanisms in HepG2 cells, and 2-(2,4-dibromophenyl)acetamide and 2-(3,4-dibromophenyl)acetamide deserved particular attention in future studies due to the relatively higher predicted cytotoxicity. This study provided the first comprehensive investigation on the cytotoxicity mechanisms of HPAcAm DBPs.

Machine learning models to predict ligand binding affinity for the orexin 1 receptor

The orexin 1 receptor (OX1R) is a G-protein coupled receptor that regulates a variety of physiological processes through interactions with the neuropeptides orexin A and B. Selective OX1R antagonists exhibit therapeutic effects in preclinical models of several behavioral disorders, including drug seeking and overeating. However, currently there are no selective OX1R antagonists approved for clinical use, fueling demand for novel compounds that act at this target. In this study, we meticulously curated a dataset comprising over 1300 OX1R ligands using a stringent filter and criteria cascade. Subsequently, we developed highly predictive quantitative structure-activity relationship (QSAR) models employing the optimized hyper-parameters for the random forest machine learning algorithm and twelve 2D molecular descriptors selected by recursive feature elimination with a 5-fold cross-validation process. The predictive capacity of the QSAR model was further assessed using an external test set and enrichment study, confirming its high predictivity. The practical applicability of our final QSAR model was demonstrated through virtual screening of the DrugBank database. This revealed two FDA-approved drugs (isavuconazole and cabozantinib) as potential OX1R ligands, confirmed by radiolabeled OX1R binding assays. To our best knowledge, this study represents the first report of highly predictive QSAR models on a large comprehensive dataset of diverse OX1R ligands, which should prove useful for the discovery and design of new compounds targeting this receptor.

Gram matrix: an efficient representation of molecular conformation and learning objective for molecular pretraining

Accurate prediction of molecular properties is fundamental in drug discovery and development, providing crucial guidance for effective drug design. A critical factor in achieving accurate molecular property prediction lies in the appropriate representation of molecular structures. Presently, prevalent deep learning-based molecular representations rely on 2D structure information as the primary molecular representation, often overlooking essential three-dimensional (3D) conformational information due to the inherent limitations of 2D structures in conveying atomic spatial relationships. In this study, we propose employing the Gram matrix as a condensed representation of 3D molecular structures and for efficient pretraining objectives. Subsequently, we leverage this matrix to construct a novel molecular representation model, Pre-GTM, which inherently encapsulates 3D information. The model accurately predicts the 3D structure of a molecule by estimating the Gram matrix. Our findings demonstrate that Pre-GTM model outperforms the baseline Graphormer model and other pretrained models in the QM9 and MoleculeNet quantitative property prediction task. The integration of the Gram matrix as a condensed representation of 3D molecular structure, incorporated into the Pre-GTM model, opens up promising avenues for its potential application across various domains of molecular research, including drug design, materials science, and chemical engineering.

Direct Determination of Absolute Radical Quantum Yields in Hydroxyl and Sulfate Radical-Based Treatment Processes

The absolute radical quantum yield (Φ ) is a critical parameter to evaluate the efficiency of radical-based processes in engineered water treatment. However, measuring Φ is fraught with challenges, as current quantification methods lack selectivity, specificity, and anti-interference capabilities, resulting in significant error propagation. Herein, we report a direct and reliable time-resolved technique to determine Φ at pH 7.0 for commonly used radical precursors in advanced oxidation processes. For H2O2 and peroxydisulfate (PDS), the values of Φ •OH and Φ SO 4 • - at 266 nm were measured to be 1.10 ± 0.01 and 1.46 ± 0.05, respectively. For peroxymonosulfate (PMS), we developed a new approach to determine Φ • OH PMS with terephthalic acid as a trap-and-trigger probe in the nonsteady state system. For the first time, the Φ • OH PMS value was measured to be 0.56 by the direct method, which is stoichiometrically equal to Φ SO 4 • - PMS (0.57 ± 0.02). Additionally, radical formation mechanisms were elucidated by density functional theory (DFT) calculations. The theoretical results showed that the highest occupied molecular orbitals of the radical precursors are O-O antibonding orbitals, facilitating the destabilization of the peroxy bond for radical formation. Electronic structures of these precursors were compared, aiming to rationalize the tendency of the Φ values we observed. Overall, this time-resolved technique with specific probes can be used as a reliable tool to determine Φ , serving as a scientific basis for the accurate performance evaluation of diverse radical-based treatment processes.

Ionization Patterns and Chemical Reactivity of Cytosine-Guanine Watson-Crick Pairs

Gas-phase and aqueous vertical ionization potentials, vIPgas and vIPaq respectively and measurements of the molecular electrostatic and local ionization maps calculated at the DFT/B3LYP-D3/ 6-311+G** level of theory and the C-PCM reaction field model for single- and double-stranded CpG and 5MeCpG pairs show that the vIPaq for single- and double-stranded pairs of C-G and 5MeC-G are practically the same, in the range of 5.79 to 5.81 eV. The aqueous adiabatic ionization potentials for single-stranded CpG and 5MeCpG are 5.52 eV and 5.51 eV respectively and they reflect the nuclear reorganization that takes place after the abstraction of the electron. The aqueous adiabatic ionization energy values that correspond to the CpG+. radical cation and the hydrated electron, e-,, being at infinite distance, adIPaq+Vo, are 3.92 eV and 3.91 eV respectively with (Vo=-1.6 eV) Analysis of data suggest that the HOMO-LUMO energy gap in the hard/soft-acid/base (HSAB) concept cannot be used a priori to determine the effect of cytosine methylation on the guanine enhanced oxidative damage in DNA.

QuanDB: a quantum chemical property database towards enhancing 3D molecular representation learning

Previous studies have shown that the three-dimensional (3D) geometric and electronic structure of molecules play a crucial role in determining their key properties and intermolecular interactions. Therefore, it is necessary to establish a quantum chemical (QC) property database containing the most stable 3D geometric conformations and electronic structures of molecules. In this study, a high-quality QC property database, called QuanDB, was developed, which included structurally diverse molecular entities and featured a user-friendly interface. Currently, QuanDB contains 154,610 compounds sourced from public databases and scientific literature, with 10,125 scaffolds. The elemental composition comprises nine elements: H, C, O, N, P, S, F, Cl, and Br. For each molecule, QuanDB provides 53 global and 5 local QC properties and the most stable 3D conformation. These properties are divided into three categories: geometric structure, electronic structure, and thermodynamics. Geometric structure optimization and single point energy calculation at the theoretical level of B3LYP-D3(BJ)/6-311G(d)/SMD/water and B3LYP-D3(BJ)/def2-TZVP/SMD/water, respectively, were applied to ensure highly accurate calculations of QC properties, with the computational cost exceeding 107 core-hours. QuanDB provides high-value geometric and electronic structure information for use in molecular representation models, which are critical for machine-learning-based molecular design, thereby contributing to a comprehensive description of the chemical compound space. As a new high-quality dataset for QC properties, QuanDB is expected to become a benchmark tool for the training and optimization of machine learning models, thus further advancing the development of novel drugs and materials. QuanDB is freely available, without registration, at https://quandb.cmdrg.com/ .

Interoperable workflows by exchanging grid-based data between quantum-chemical program packages

Quantum-chemical subsystem and embedding methods require complex workflows that may involve multiple quantum-chemical program packages. Moreover, such workflows require the exchange of voluminous data that go beyond simple quantities, such as molecular structures and energies. Here, we describe our approach for addressing this interoperability challenge by exchanging electron densities and embedding potentials as grid-based data. We describe the approach that we have implemented to this end in a dedicated code, PyEmbed, currently part of a Python scripting framework. We discuss how it has facilitated the development of quantum-chemical subsystem and embedding methods and highlight several applications that have been enabled by PyEmbed, including wave-function theory (WFT) in density-functional theory (DFT) embedding schemes mixing non-relativistic and relativistic electronic structure methods, real-time time-dependent DFT-in-DFT approaches, the density-based many-body expansion, and workflows including real-space data analysis and visualization. Our approach demonstrates, in particular, the merits of exchanging (complex) grid-based data and, in general, the potential of modular software development in quantum chemistry, which hinges upon libraries that facilitate interoperability.

Integrating molecular descriptors for enhanced prediction: Shedding light on the potential of pH to model hydrated electron reaction rates for organic compounds

Hydrated electron reaction rate constant (ke-aq) is an important parameter to determine reductive degradation efficiency and to mitigate the ecological risk of organic compounds (OCs). However, OC species morphology and the concentration of hydrated electrons (e-aq) in water vary with pH, complicating OC fate assessment. This study introduced the environmental variable of pH, to develop models for ke-aq for 701 data points using 3 descriptor types: (i) molecular descriptors (MD), (ii) quantum chemical descriptors (QCD), and (iii) the combination of both (MD + QCD). Models were screened using 2 descriptor screening methods (MLR and RF) and 14 machine learning (ML) algorithms. The introduction of QCDs that characterized the electronic structure of OCs greatly improved the performance of models while ensuring the need for fewer descriptors. The optimal model MLR-XGBoost(MD + QCD), which included pH, achieved the most satisfactory prediction: R2tra = 0.988, Q2boot = 0.861, R2test = 0.875 and Q2test = 0.873. The mechanistic interpretation using the SHAP method further revealed that QCDs, polarizability, volume, and pH had a great influence on the reductive degradation of OCs by e-aq. Overall, the electrochemical parameters (QCDs, pH) related to the solvent and solute are of significance and should be considered in any future ML modeling that assesses the fate of OCs in aquatic environment.

Unraveling the Push-Pull Effect in Acenes, Polyenes and Polyynes

Substituent effects (SEs) are fundamental for predicting molecular reactivity, while polyene, polyyne and acene derivatives are precursors to compounds with diverse applications. Computations were performed for Y-R-X systems, where reaction sites Y=NO2 and O- , substituents X=NO2 , CN, Cl, H, OH, NH2 , and spacers R=polyene, polyyne (n=1-5, 10 repeating units) and acene (up to tetracene). The cSAR (charge of the substituent active region) approach allowed to present, for the first time, quantitative relations describing the spacer's electron-donating and withdrawing properties as a function of n and the spacer type. The electronic properties of the X substituents depend on the type of spacer, its length and the Y group, which is an example of the reverse SE. To describe how the SE between Y and X weakens with n, two approaches were compared: cSAR and SESE (SE stabilization energy). The EDDB (electron density of delocalized bonds) characterize changes in electron delocalization in spacers due to the SE. A new approach - EDDB differential maps - allow to extract the effect of X substitution on the electron delocalization. The charges at spacer's C atoms correlate with cSAR; changes in the slopes confirm the charge transfer by resonance.

Theoretical and experimental studies on sulfasalazine interactions with poly (lactic acid): Impact of hydrogen bonding and charge transfer interactions on molecular structure, electronic and optical properties

The interaction between sulfasalazine (SSZ) through different functional groups and poly (lactic acid) (PLA) in the chloroform phase was investigated in this study using density functional theory (DFT) and time-dependent density functional theory (TDDFT) methods. The binding energy and thermodynamic parameters show that the hydrogen double bond interaction between SSZ and PLA in state I (-0.71 eV) is stronger than in states II (-0.64 eV) and III (-0.51 eV). The SSZ and PLA interaction results in an enhanced dipole moment, greater solubility, and more negative values for Gibbs free energy (ΔGsolv) and energy gap (Eg). Considerable changes in absorption peaks of SSZ and PLA indicate surface adsorption of the drug (SSZ) into the carrier (PLA) in UV-Vis spectra. Theoretical UV-Vis analysis demonstrates SSZ interaction with PLA happens in the ultraviolet region with a maximum absorption peak at 380 nm, which is close to experimental UV-Vis analysis. The experimental spectra showed minimal variations in the maximum absorption wavelength, with respect to theoretical calculations. The presence of SSZ was found to cause a modification in the structure of PLA, as evidenced by both experimental and theoretical Infrared (IR) spectra.

Synthesis, structural characterizations, in vitro biological evaluation and computational investigations of pyrazole derivatives as potential antidiabetic and antioxidant agents

In this study, a two pyrazole derivatives; 2-(5-methyl-1H-pyrazole-3-carbonyl)-N-phenylhydrazine-1-carboxamide (Pyz-1) and 4-amino-5-(5-methyl-1H-pyrazol-3-yl)-4H-1,2,4-triazole-3-thiol (Pyz-2) were synthesized and characterized by 13C-NMR, 1H-NMR, FT-IR, and mass spectrometry. A complete molecular structures optimization, electronic and thermodynamic properties of Pyz-1 and Pyz-2 in gas phase and aqueous solution were predicted by using hybrid B3LYP method with the 6-311++G** basis sets. Pyz-1 and Pyz-2 were evaluated in vitro for their anti-diabetic, antioxidant and xanthine oxidase inhibition activities. For anti-diabetic activity, Pyz-1 and Pyz-2 showed a potent α-glucosidase and α-amylase inhibition with IC50 values of 75.62 ± 0.56, 95.85 ± 0.92 and 119.3 ± 0.75, 120.2 ± 0.68 µM, respectively, compared to Acarbose (IC50(α-glucosidase) = 72.58 ± 0.68 µM, IC50(α-amylase) = 115.6 ± 0.574 µM). In xanthine oxidase assay, Pyz-1 and Pyz-2 exhibited remarkable inhibitory ability with IC50 values 24.32 ± 0.78 and 10.75 ± 0.54 µM, respectively. The result of antioxidant activities showed that the title compounds have considerable antioxidant and radical scavenger abilities. In addition, molecular docking simulation was used to determine the binding modes and energies between the title compounds and α-glucosidase and α-amylase enzymes.

Target analysis, occurrence and cytotoxicity of halogenated polyhydroxyphenols as emerging disinfection byproducts in drinking water

Halogenated aromatic disinfection byproducts (DBPs) in drinking water, such as halogenated phenols, have received widespread attention due to their high toxicity and ubiquitous occurrence in recent years. This study identified a group of emerging halogenated aromatic DBPs, known as halogenated polyhydroxyphenols (HPPs), and investigated their occurrence and cytotoxicity. We developed a highly sensitive solid-phase extraction ultra-performance liquid chromatography-tandem mass spectrometry (SPE-UPLC-MS/MS) method under multiple reaction monitoring (MRM) mode, with recoveries ranging from 86 to 115% and method detection limits (MDLs) ranging from 0.10 to 1.87 ng/L for the analysis of 15 HPPs. Eleven of these HPP DBPs were detected in collected drinking water samples using this method with detection frequencies ranging from 14 to 100% and a maximum concentration of 24 ng/L. The IC50 of the 15 HPPs in Chinese hamster ovary (CHO-K1) cells were ranged from 15.13 µM to 6.08×103 µM. The tested HPPs with -CHO substitution exhibited higher cytotoxicity compared to those with -COOH substitution. The TIC-Tox values of HPPs were calculated to be higher than those of HPs, indicating a potential necessity to pay attention to HPP DBPs. A quantitative structure-activity relationship (QSAR) model was developed for the cytotoxicity of HPPs, which was shown to be significantly associated with acid dissociation constant (pKa) and total valence connectivity (TVCon). To the best of our knowledge, this study reported the analysis, occurrence, and cytotoxicity of HPP DBPs in drinking water for the first time.

Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets

Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.

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

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

The Calculation of Both Electrostatic and Van der Waals Effects to Probe the Efficiency of Solvent Extraction of Heterocyclic Aromatics from Heavy Oil

Due to the complex composition and similar structure, the extraction denitrification of aromatic rich oil is faced with the contradiction problem of denitrification efficiency and aromatic loss which cannot be efficiently solved by experiments. However, the complex interactions involved can be analyzed from the perspective of calculation, and the prediction criteria and methods are proposed. Based on rigorous density functional theory calculation data, Simple models based on electrostatic potential (ESP) and Van der Waals potential (VdWP)-based calculations were established and validated. The twofold model provided the best prediction for interactions between extractants and nitrogen compounds and between extractants and aromatics, which determines denitrification efficiency and aromatic loss, respectively, due to the most complete description of both electrostatic and VdW force. This provides a powerful tool for evaluating the non-covalent interactions and thence tuning the efficiency of the separation process. Thus, high denitrification efficiency (43.2~66.3 %) and moderate aromatic loss (1.7~4.4 %) were obtained using screened deep eutectic solvents (DESs). This ideal observation provided the potential for mild hydrodesulfurization and manufacture of high-grade carbon materials.

Fragments quantum descriptors in classification of bio-accumulative compounds

The aim of the following research is to assess the applicability of calculated quantum properties of molecular fragments as molecular descriptors in machine learning classification task. The research is based on bio-concentration and QM9-extended databases. A number of compounds with results from quantum-chemical calculations conducted with Psi4 quantum chemistry package was also added to the quantum properties database. Classification results are compared with a baseline of random guesses and predictions obtained with the traditional RDKit generated molecular descriptors. Chosen classification metrics show that results obtained with fragments quantum descriptors fall between results from baseline and those provided by molecular descriptors widely applied in cheminformatics. According to the results, the implementation of principal component analysis, causes a drop in categorization metrics.

Target-ligand binding affinity from single point enthalpy calculation and elemental composition

Reliable target-ligand binding thermodynamics data are essential for successful drug design and molecular engineering projects. Besides experimental methods, a number of theoretical approaches have been introduced for the generation of binding thermodynamics data. However, available approaches often neglect electronic effects or explicit water molecules influencing target-ligand interactions. To handle electronic effects within a reasonable time frame, we introduce a fast calculator QMH-L using a single target-ligand complex structure pre-optimized at the molecular mechanics level. QMH-L is composed of the semi-empirical quantum mechanics calculation of binding enthalpy with predicted explicit water molecules at the complex interface, and a simple descriptor based on the elemental composition of the ligand. QMH-L estimates the target-ligand binding free energy with a root mean square error (RMSE) of 0.94 kcal mol-1. The calculations also provide binding enthalpy values and they were compared with experimental binding thermodynamics data collected from the most reliable isothermal titration calorimetry studies of systems including various protein targets and challenging, large peptide ligands with a molecular weight of up to 2-3 thousand. The single point enthalpy calculations of QMH-L require modest computational resources and are based on short runs with open source and/or free software like Gromacs, Mopac, MobyWat, and Fragmenter. QMH-L can be applied for fast, automated scoring of drug candidates during a virtual screen, enthalpic engineering of new ligands or thermodynamic explanation of complex interactions.

Molecular modelling studies of substituted indole derivatives as novel influenza a virus inhibitors

The emergence of drug-resistant strains motivate researchers to find new innovative anti-IAV candidates with a different mode of action. In this work, molecular modelling strategies, such as 2D-QSAR, 3D-QSAR, molecular docking, molecular dynamics, FMOs, and ADMET were applied to some substituted indoles as IAV inhibitors. The best-developed 2D-QSAR models, MLR (Q2 = 0.7634, R2train = 0.8666) and ANN[4-3-1] (Q2 = 0.8699, R2train = 0.8705) revealed good statistical validation for the inhibitory response predictions. The 3D-QSAR models, CoMFA (Q2 = 0.504, R2train = 0.805) and CoMSIA/SEDHA (Q2 = 0.619, R2train = 0.813) are selected as the best 3D models following the global thresholds. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrate the relationship between the molecular fields and the inhibitory effects of the studied molecules. The results of the studies led to the design of five new molecules (24a-e) with enhanced anti-IAV activities and binding potentials using the most active molecule (24) as the template scaffold. The conformational stability of the best-designed molecules with the NA protein showed hydrophobic and H-bonds with the key residues from the molecular dynamics simulations of 100 ns. Furthermore, the global reactivity indices from the DFT calculations portrayed the relevance of 24c in view of its smaller band gap as also justified by our QSAR and molecular simulation studies.Communicated by Ramaswamy H. Sarma.

Quinoxalines against Leishmania amazonensis: SAR study, proposition of a new derivative, QSAR prediction, synthesis, and biological evaluation

Neglected tropical diseases, such as leishmaniasis, lead to serious limitations to the affected societies. In this work, a structure-activity relationship (SAR) study was developed with a series of quinoxaline derivatives, active against the promastigote forms of Leishmania amazonensis. As a result, a new quinoxaline derivative was designed and synthesized. In addition, a quantitative structure-activity relationship (QSAR) model was obtained [pIC50 = - 1.51 - 0.96 (EHOMO) + 0.02 (PSA); N = 17, R2 = 0.980, R2Adj = 0.977, s = 0.103, and LOO-cv-R2 (Q2) = 0.971]. The activity of the new synthesized compound was estimated (pIC50 = 5.88) and compared with the experimental result (pIC50 = 5.70), which allowed to evaluate the good predictive capacity of the model.

Machine learning prediction of empirical polarity using SMILES encoding of organic solvents

Machine learning based statistical models have played a significant role in increasing the speed and accuracy with which the chemical and physical properties of chemical compounds can be predicted as compared to the experimental, and traditional ab initio and quantum mechanical approaches. The transformative impact that these techniques have, in the field of chemical sciences has completely changed the way experiments are designed. The last decade has seen the prominence of computer-aided molecular design based on machine learning algorithms. The major challenge has been the generation of machine-readable data in the form of descriptors and observations for training the model, which can again be time-consuming and computationally expensive if atomic coordinates based molecular encoding approach is used. In this study, we have tried to solve this problem using SMILES representation of molecules for generating various topological, physicochemical, electronic and steric descriptors using open-source cheminformatics packages. With the aid of the data generated using these packages, we have been able to develop a simple and explainable quantitative structure property relationship model using artificial neural network based on 7 numerical descriptors and 1 categorical descriptor for predicting the empirical polarity of a wide diversity of organic solvents. Since polarity is the representation of various solute-solvent and solvent-solvent interactions taking place in an organic transformation, its intuition beforehand will definitely help a chemist in a better experimental design. An ANN algorithm based on 8 descriptors was successfully employed to predict the ET(30) values of organic solvents.

Reliable and accurate prediction of basic pK[Formula: see text] values in nitrogen compounds: the pK[Formula: see text] shift in supramolecular systems as a case study

This article presents a quantitative structure-activity relationship (QSAR) approach for predicting the acid dissociation constant (pK[Formula: see text]) of nitrogenous compounds, including those within supramolecular complexes based on cucurbiturils. The model combines low-cost quantum mechanical calculations with QSAR methodology and linear regressions to achieve accurate predictions for a broad range of nitrogen-containing compounds. The model was developed using a diverse dataset of 130 nitrogenous compounds and exhibits excellent predictive performance, with a high coefficient of determination (R[Formula: see text]) of 0.9905, low standard error (s) of 0.3066, and high Fisher statistic (F) of 2142. The model outperforms existing methods, such as Chemaxon software and previous studies, in terms of accuracy and its ability to handle heterogeneous datasets. External validation on pharmaceutical ingredients, dyes, and supramolecular complexes based on cucurbiturils confirms the reliability of the model. To enhance usability, a script-like tool has been developed, providing a streamlined process for users to access the model. This study represents a significant advancement in pK[Formula: see text] prediction, offering valuable insights for drug design and supramolecular system optimization.

PubChemQC B3LYP/6-31G*//PM6 Data Set: The Electronic Structures of 86 Million Molecules Using B3LYP/6-31G* Calculations

The presented "PubChemQC B3LYP/6-31G*//PM6" data set is composed of the electronic properties of 85,938,443 molecules, encompassing a broad spectrum of molecules from essential compounds to biomolecules with a molecular weight up to 1000. These molecules account for 94.0% of the original PubChem Compound catalog as of August 29, 2016. The electronic properties, including orbitals, orbital energies, total energies, dipole moments, and other pertinent properties, were computed by using the B3LYP/6-31G* and PM6 methods. The data set, available in three formats, namely, GAMESS quantum chemistry program files, selected JSON output files, and a PostgreSQL database, provides researchers with the ability to query molecular properties. It is further subdivided into five subdata sets for more specific data. The first two subsets encompass molecules with carbon, hydrogen, oxygen, and nitrogen with molecular weights under 300 and 500, respectively. The third and fourth subsets incorporate molecules with carbon, hydrogen, nitrogen, oxygen, phosphorus, sulfur, fluorine, and chlorine, with molecular weights under 300 and 500, respectively. The fifth subset comprises molecules with carbon, hydrogen, nitrogen, oxygen, phosphorus, sulfur, fluorine, chlorine, sodium, potassium, magnesium, and calcium, with a molecular weight of under 500. The coefficients of determination for the highest occupied molecular orbital-lowest unoccupied molecular orbital energy gap range from 0.892 (for CHON500) to 0.803 (for the whole data set). These comprehensive results pave the way for applications in drug discovery and materials science, among others. The data sets can be accessed under the Creative Commons Attribution 4.0 International license at the following web address: https://nakatamaho.riken.jp/pubchemqc.riken.jp/b3lyp_pm6_datasets.html.

Antimicrobial Peptides (AMP) in the Cell-Free Culture Media of Xenorhabdus budapestensis and X. szentirmaii Exert Anti-Protist Activity against Eukaryotic Vertebrate Pathogens including Histomonas meleagridis and Leishmania donovani Species

Anti-microbial peptides provide a powerful toolkit for combating multidrug resistance. Combating eukaryotic pathogens is complicated because the intracellular drug targets in the eukaryotic pathogen are frequently homologs of cellular structures of vital importance in the host organism. The entomopathogenic bacteria (EPB), symbionts of entomopathogenic-nematode species, release a series of non-ribosomal templated anti-microbial peptides. Some may be potential drug candidates. The ability of an entomopathogenic-nematode/entomopathogenic bacterium symbiotic complex to survive in a given polyxenic milieu is a coevolutionary product. This explains that those gene complexes that are responsible for the biosynthesis of different non-ribosomal templated anti-microbial protective peptides (including those that are potently capable of inactivating the protist mammalian pathogen Leishmania donovanii and the gallinaceous bird pathogen Histomonas meleagridis) are co-regulated. Our approach is based on comparative anti-microbial bioassays of the culture media of the wild-type and regulatory mutant strains. We concluded that Xenorhabdus budapestensis and X. szentirmaii are excellent sources of non-ribosomal templated anti-microbial peptides that are efficient antagonists of the mentioned pathogens. Data on selective cytotoxicity of different cell-free culture media encourage us to forecast that the recently discovered "easy-PACId" research strategy is suitable for constructing entomopathogenic-bacterium (EPB) strains producing and releasing single, harmless, non-ribosomal templated anti-microbial peptides with considerable drug, (probiotic)-candidate potential.

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

Equivariant Graph Neural Networks for Toxicity Prediction

Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural networks with well-known molecular representations such as fingerprints or 2D graphs. But the more natural, accurate representation of molecules is expected to be defined in physical 3D space like in ab initio methods. Recent studies successfully used equivariant graph neural networks (EGNNs) for representation learning based on 3D structures to predict quantum-mechanical properties of molecules. Inspired by this, we investigated the performance of EGNNs to construct reliable ML models for toxicity prediction. We used the equivariant transformer (ET) model in TorchMD-NET for this. Eleven toxicity data sets taken from MoleculeNet, TDCommons, and ToxBenchmark have been considered to evaluate the capability of ET for toxicity prediction. Our results show that ET adequately learns 3D representations of molecules that can successfully correlate with toxicity activity, achieving good accuracies on most data sets comparable to state-of-the-art models. We also test a physicochemical property, namely, the total energy of a molecule, to inform the toxicity prediction with a physical prior. However, our work suggests that these two properties can not be related. We also provide an attention weight analysis for helping to understand the toxicity prediction in 3D space and thus increase the explainability of the ML model. In summary, our findings offer promising insights considering 3D geometry information via EGNNs and provide a straightforward way to integrate molecular conformers into ML-based pipelines for predicting and investigating toxicity prediction in physical space. We expect that in the future, especially for larger, more diverse data sets, EGNNs will be an essential tool in this domain.

Development of substituted benzylidene derivatives as novel dual cholinesterase inhibitors for Alzheimer's treatment

Leading pathological markers of Alzheimer's disease (AD) include Acetylcholinesterase (AChE), Butyrylcholinesterase (BuChE), Amyloid beta (Aβ) and reactive oxygen species (ROS). Indole derivatives were identified and optimized to improve the potency against AChE, BuChE, Aβ and ROS. The lead molecule IND-30 was found to be selective for AChE (selectivity ratio: 22.92) in comparison to BuChE and showed maximum inhibition potential for human AChE (IC50: 4.16 ± 0.063 μM). IND-30 was found to be safe on the SH-SY5Y cell line until the dose of 30 mM. Further, molecule IND-30 was evaluated for its ability to inhibit AChE-induced Aβ aggregation at 0.5, 10 and 20 μM doses. Approximately, 50% of AChE-induced Aβ aggregation was inhibited by IND-30. Thus, IND-30 was found to be multitargeting for AD.

Human Endocrine-Disrupting Effects of Phthalate Esters through Adverse Outcome Pathways: A Comprehensive Mechanism Analysis

Phthalate esters (PAEs) are widely exposed in the environment as plasticizers in plastics, and they have been found to cause significant environmental and health hazards, especially in terms of endocrine disruption in humans. In order to investigate the processes underlying the endocrine disruption effects of PAEs, three machine learning techniques were used in this study to build an adverse outcome pathway (AOP) for those effects on people. According to the results of the three machine learning techniques, the random forest and XGBoost models performed well in terms of prediction. Subsequently, sensitivity analysis was conducted to identify the initial events, key events, and key features influencing the endocrine disruption effects of PAEs on humans. Key features, such as Mol.Wt, Q+, QH+, ELUMO, minHCsats, MEDC-33, and EG, were found to be closely related to the molecular structure. Therefore, a 3D-QSAR model for PAEs was constructed, and, based on the three-dimensional potential energy surface information, it was discovered that the hydrophobic, steric, and electrostatic fields of PAEs significantly influence their endocrine disruption effects on humans. Lastly, an analysis of the contributions of amino acid residues and binding energy (BE) was performed, identifying and confirming that hydrogen bonding, hydrophobic interactions, and van der Waals forces are important factors affecting the AOP of PAEs' molecular endocrine disruption effects. This study defined and constructed a comprehensive AOP for the endocrine disruption effects of PAEs on humans and developed a method based on theoretical simulation to characterize the AOP, providing theoretical guidance for studying the mechanisms of toxicity caused by other pollutants.

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

Recent Advances in Toxicity Prediction: Applications of Deep Graph Learning

The development of new drugs is time-consuming and expensive, and as such, accurately predicting the potential toxicity of a drug candidate is crucial in ensuring its safety and efficacy. Recently, deep graph learning has become prevalent in this field due to its computational power and cost efficiency. Many novel deep graph learning methods aid toxicity prediction and further prompt drug development. This review aims to connect fundamental knowledge with burgeoning deep graph learning methods. We first summarize the essential components of deep graph learning models for toxicity prediction, including molecular descriptors, molecular representations, evaluation metrics, validation methods, and data sets. Furthermore, based on various graph-related representations of molecules, we introduce several representative studies and methods for toxicity prediction from the perspective of GNN architectures and graph pretrained models. Compared to other types of models, deep graph models not only advance in higher accuracy and efficiency but also provide more intuitive insights, which is significant in the development of model interpretation and generalization ability. The graph pretrained models are emerging as they can extract prominent features from large-scale unlabeled molecular graph data and improve the performance of downstream toxicity prediction tasks. We hope this survey can serve as a handbook for individuals interested in exploring deep graph learning for toxicity prediction.

Ni2+ and Cu2+ complexes of N-(2,6-dichlorophenyl)-N-mesityl formamidine dithiocarbamate structural and functional properties as CYP3A4 potential substrates

Metal compounds continued to attract diverse applications due to their malleability in several capacities. In this study, we present our findings on the crystal structures and functional properties of Ni2+ and Cu2+ complexes of N'-(2,6-dichlorophenyl)-N-mesitylformamidine dithiocarbamate (L) comprising [Ni-(L)2] (1) and [Cu-(L)2] (2) with a four-coordinate metal center. We established the two complex structures through 1H and 13C nuclear magnetic resonance (NMR), elemental, and single-crystal X-ray analysis. The analyses showed that the two complexes are isomorphous, having P21/c as a space group and a unit-cell similarity index (π) of 0.002. The two complexes conform to a distorted square planar geometry around the metal centers. The calculated and experimental data, including bond lengths, angles, and NMR values, are similar. Hirshfeld surface analysis revealed the variational contribution of the different types of intermolecular contacts driven by the crystal lattice of the two solvated complexes. Our knowledge of the potential biological implication of these structures enabled us to probe the compounds as prospective CYP3A4 inhibitors. This approach mimics current trends in pharmaceutical design and biomedicine by incorporating potentially active molecules into various media to predict their biological efficacies. The simulations show appreciable binding of compounds 1 and 2 to CYP3A4 with average interaction energies of -97 and -87 kcal/mol, respectively. The protein attains at least five conformational states in the three studied models using a Gaussian Mixture Model-based clustering and free energy prediction. Electric field analysis shows the crucial residues to substrate binding at the active site, enabling CYP3A4 structure to function prediction. The predicted inhibition with these Ni2+ and Cu2+ complexes indicates that CYP3A4 overexpression in a diseased state like cancer would reduce, thereby increasing the chemotherapeutic compounds' shelf-lives for adsorption. This multidimensional study addresses various aspects of molecular metal electronics, including their application as substrate-mimicking inhibitors. The outcome would enable further research on bio-metal compounds of critical potential.

Chemical similarity of molecules with physiological response

Measuring the similarity among molecules is an important task in various chemically oriented problems. This elusive concept is hard to define and quantify. Moreover, the complexity of the problem is elevated by bifurcating the notion of molecular similarity to structural and chemical similarity. While the structural similarity of molecules is being extensively researched, the so-called chemical similarity is being mentioned scarcely. Here, we propose a way of converting the physico-chemical properties into molecular fingerprints. Then, using the apparatus of measuring the structural similarity, the chemical similarity can be assessed. The proof of a concept is demonstrated on a set of molecules that induce diverse physiological responses.

PyL3dMD: Python LAMMPS 3D molecular descriptors package

Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure-property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies.

Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge

Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling of synthetic transformation with the required extrapolative ability and chemical interpretability. To meet the gap between the rich domain knowledge of chemistry and the advanced molecular graph model, herein we report a knowledge-based graph model that embeds the digitalized steric and electronic information. In addition, a molecular interaction module is developed to enable the learning of the synergistic influence of reaction components. In this study, we demonstrate that this knowledge-based graph model achieves excellent predictions of reaction yield and stereoselectivity, whose extrapolative ability is corroborated by additional scaffold-based data splittings and experimental verifications with new catalysts. Because of the embedding of local environment, the model allows the atomic level of interpretation of the steric and electronic influence on the overall synthetic performance, which serves as a useful guide for the molecular engineering towards the target synthetic function. This model offers an extrapolative and interpretable approach for reaction performance prediction, pointing out the importance of chemical knowledge-constrained reaction modelling for synthetic purpose.

DFT and QSAR studies of PTFE/ZnO/SiO2 nanocomposite

Polytetrafluoroethylene (PTFE) is one of the most significant fluoropolymers, and one of the most recent initiatives is to increase its performance by using metal oxides (MOs). Consequently, the surface modifications of PTFE with two metal oxides (MOs), SiO₂ and ZnO, individually and as a mixture of the two MOs, were modeled using density functional theory (DFT). The B3LYPL/LANL2DZ model was used in the studies conducted to follow up the changes in electronic properties. The total dipole moment (TDM) and HOMO/LUMO band gap energy (∆E) of PTFE, which were 0.000 Debye and 8.517 eV respectively, were enhanced to 13.008 Debye and 0.690 eV in the case of PTFE/4ZnO/4SiO₂. Moreover, with increasing nano filler (PTFE/8ZnO/8SiO₂), TDM changed to 10.605 Debye and ∆E decreased to 0.273 eV leading to further improvement in the electronic properties. The molecular electrostatic potential (MESP) and quantitative structure activity relationship (QSAR) studies revealed that surface modification of PTFE with ZnO and SiO₂ increased its electrical and thermal stability. The improved PTFE/ZnO/SiO₂ composite can, therefore, be used as a self-cleaning layer for astronaut suits based on the findings of relatively high mobility, minimal reactivity to the surrounding environment, and thermal stability.

Quantitative structure-activity relationship models for the reaction rate coefficients between dissolved organic matter and PPCPs

To predict PPCPs' photolysis rate in natural aquatic environment, it is essential to grasp the reaction rates between DOM and PPCPs, yet there are few measured data and no prediction models for this important photochemical parameter. To address this, a reaction rate coefficient (α_DOM) was defined to describe the apparent rate of DOM-involved photoreaction for PPCPs. The measured α_DOM values for 40 PPCPs in 9 DOM samples varied dramatically, ranging from (-2.1 ± 0.1)× 10¹⁰ to (2.2 ± 0.1)× 10¹¹ M^-1 s^-1. Then the quantitative structure-activity relationship (QSAR) models were developed using chemical and water quality descriptors via the random forest method. We initially separated positive and negative values by a classifier with an AUC value of 0.965, followed by the construction of regression models for positive and negative values, respectively, using a regressor. Positive models achieved satisfactory goodness-of-fit and predictive ability (R²_adj=0.92 and Q²_ext=0.86), while negative models demonstrated acceptable performance (R²_adj=0.71 and Q²_ext=0.70). Finally, a comprehensive photolysis model that incorporates the QSAR models for α_DOM was established and the significance of water quality parameters was emphasized through sensitive analysis. This model enables more elaborate predictions of PPCPs' photolysis rates in various water samples, providing valuable assistance for forecasting PPCPs' environmental fate.

Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures

Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, ranging from molecular models over equations of state to models of excess properties. These well-established methods are currently being complemented by new methods from the field of machine learning (ML). This review focuses on the rapidly developing interface between these two approaches and gives a structured overview of how physical modeling and ML can be combined to yield hybrid models. We illustrate the different options with examples from recent research and give an outlook on future developments.

Discovery of ANO1 Inhibitors based on Machine learning and molecule docking simulation approaches

Calcium-activated chloride channels (CaCCs) are chloride channels that are regulated according to intracellular calcium ion concentrations. The channel protein ANO1 is widely present in cells and is involved in physiological activities including cellular secretion, signaling, cell proliferation and vasoconstriction and diastole. In this study, the ANO1 inhibitors were investigated with machine learning and molecular simulation. Two-dimensional structure-activity relationship (2D-SAR) and three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for the qualitative and quantitative prediction of ANO1 inhibitors. The results showed that the prediction accuracies of the model were 85.9% and 87.8% for the training and test sets, respectively, and 85.9% and 87.8% for the rotating forest (RF) in the 2D-SAR model. The CoMFA and CoMSIA methods were then used for 3D QSAR modeling of ANO1 inhibitors, respectively. The q² coefficients for model cross-validation were all greater than 0.5, implying that we were able to obtain a stable model for drug activity prediction. Molecular docking was further used to simulate the interactions between the five most promising compounds predicted by the model and the ANO1 protein. The total score for the docking results between all five compounds and the target protein was greater than 6, indicating that they interacted strongly in the form of hydrogen bonds. Finally, simulations of amino acid mutations around the docking cavity of the target proteins showed that each molecule had two or more sites of reduced affinity following a single mutation, indicating outstanding specificity of the screened drug molecules and their protein ligands.

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

This manuscript provides an overview of the current state of the art in terms of the molecular modelling of the thermophysical properties of fluids. It is intended to manage the expectations and serve as guidance to practising physical chemists, chemical physicists and engineers in terms of the scope and accuracy of the more commonly available intermolecular potentials along with the peculiarities of the software and methods employed in molecular simulations while providing insights on the gaps and opportunities available in this field. The discussion is focused around case studies which showcase both the precision and the limitations of frequently used workflows.