Prediction of pKa values using the PM6 semiempirical method

Automated preparation of nanoscopic structures: Graph-based sequence analysis, mismatch detection, and pH-consistent protonation with uncertainty estimates

Structure and function in nanoscale atomistic assemblies are tightly coupled, and every atom with its specific position and even every electron will have a decisive effect on the electronic structure, and hence, on the molecular properties. Molecular simulations of nanoscopic atomistic structures therefore require accurately resolved three-dimensional input structures. If extracted from experiment, these structures often suffer from severe uncertainties, of which the lack of information on hydrogen atoms is a prominent example. Hence, experimental structures require careful review and curation, which is a time-consuming and error-prone process. Here, we present a fast and robust protocol for the automated structure analysis and pH-consistent protonation, in short, ASAP. For biomolecules as a target, the ASAP protocol integrates sequence analysis and error assessment of a given input structure. ASAP allows for pK a  prediction from reference data through Gaussian process regression including uncertainty estimation and connects to system-focused atomistic modeling described in Brunken and Reiher (J. Chem. Theory Comput. 16, 2020, 1646). Although focused on biomolecules, ASAP can be extended to other nanoscopic objects, because most of its design elements rely on a general graph-based foundation guaranteeing transferability. The modular character of the underlying pipeline supports different degrees of automation, which allows for (i) efficient feedback loops for human-machine interaction with a low entrance barrier and for (ii) integration into autonomous procedures such as automated force field parametrizations. This facilitates fast switching of the pH-state through on-the-fly system-focused reparametrization during a molecular simulation at virtually no extra computational cost.

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico, which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.

Synthesis of graphene oxide grafted by diazanyl groups and its application in recovery of lead from lead-acid wastewater

Heavy metal ion (HMI) in wastewater is a kind of resource that is wrongly placed. Recovery of heavy metal from lead-acid wastewater desires efficient and reusable functional materials. In this paper, graphene oxide-like with diazanyl groups (GOLA) was synthesized by a nucleophilic substitution reaction of graphene oxide-like with hydroxyl groups (GOLH) with diazane. GOLA exhibited good stability and recyclability in wastewater. The maximal adsorption capacity (q_max) values of GOLA for Pb(II), Cd(II), Cu(II), Ni(II), and Cr(III) ions were 505.80, 401.99, 83.48, 82.29, and 147.77 mg/g, respectively. The equilibrium time of GOLA adsorbing HMIs was 20 min. GOLA was employed to recover lead ions from lead-acid wastewater to give Pb(OH)₂ and reusable water. Therefore, this paper provides a useful method of recycling lead from lead-acid wastewater.

Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow

Semi-empirical quantum chemical approaches are known to compromise accuracy for the feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screening, and data-driven machine learning has shifted the emphasis toward calculation runtimes recently. This comes with new constraints for the software implementation as many fast calculations would suffer from a large overhead of the manual setup and other procedures that are comparatively fast when studying a single molecular structure, but which become prohibitively slow for high-throughput demands. In this work, we discuss the effect of various well-established semi-empirical approximations on calculation speed and relate this to data transfer rates from the raw-data source computer to the results of the visualization front end. For the former, we consider desktop computers, local high performance computing, and remote cloud services in order to elucidate the effect on interactive calculations, for web and cloud interfaces in local applications, and in world-wide interactive virtual sessions. The models discussed in this work have been implemented into our open-source software SCINE Sparrow.

Self-driven in situ facile synthesis of CuO/Cu2O for enhanced catalytic reduction of 4-nitrophenol by acetic acid

A series of CuO, Cu2O and CuO/Cu2O catalyst structures with different morphologies are synthesized in situ by controlling the anionic species of the copper salts in the reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP).

Efficient Quantum-Chemical Calculations of Acid Dissociation Constants from Free-Energy Relationships

The calculation of acid dissociation constants (pK_a) is an important task in computational chemistry and chemoinformatics. Theoretically and with minimal empiricism, this is possible from computed acid dissociation free energies via so-called linear free-energy relationships. In this study some modifications are introduced to the latter, providing a straightforward, broadly applicable protocol with an adjustable degree of sophistication for quantum chemistry-based calculations of pK_a in water. It targets a wide pK_a range (∼70 units) and medium-sized, flexible molecules. Herein, a focus is set on the recently published r²SCAN-3c and related efficient composite density functionals and the semiempirical GFN2-xTB method, including a newly introduced energy correction for heterolytic dissociation, both in combination with implicit solvation models. The performance is evaluated in comparison with experimental data, showing mean errors often smaller than a targeted 1 pK_a unit accuracy. Larger deviations are observed only upon inclusion of challenging highly negative (<-5) or positive (>15) pK_a values. Among all those tested, it is found that B97-3c is the best performing functional, although rather independently of the density functional theory (DFT) method used; low root-mean-square errors of 0.8-1.0 pK_a units for typical drugs are obtained. For optimal performance, it is recommended to employ DFT functional specific free-energy relationship parameters. Additionally, a significant conformational dependence of the pK_a values is revealed and quantified for some nonrigid drug molecules.

A new release of MOPAC incorporating the INDO/S semiempirical model with CI excited states

The semiempirical INDO/S Hamiltonian is incorporated into a new release of MOPAC2016. The MOPAC2016 software package has long been at the forefront of semiempirical quantum chemical methods (SEQMs) for small molecules, proteins, and solids and until this release has included only NDDO-type SEQMs. The new code enables the calculation of excited states using the INDO/S Hamiltonian combined with a configuration interaction (CI) approach using single excitations (CIS), single and double excitations (CISD), or multiple reference determinants (MRCI) where reference determinants are generated using a complete active space (CAS) approach. The capacity to perform excited-state calculations beyond the CIS level makes INDO/CI one of the few low-cost computational methods capable of accurately modeling states with substantial double-excitation character. Solvent corrections to the ground-state and excited-state energies can be computed using the COSMO implicit solvent model, incorporating state-specific corrections to the excited states based on the solvent refractive index. This code produces physically reasonable electronic structures, absorption spectra, and solvatochromic shifts at low computational costs for systems up to hundreds of atoms, and for both organic molecules and metal clusters.

On the Accuracy of the Direct Method to Calculate pKa from Electronic Structure Calculations

The direct method (HA_(soln) ⇌ A_(soln)^– + H_(soln)⁺) for calculating pK_a of monoprotic acids is as efficient as thermodynamic cycles. A selective adjustment of proton free energy in solution was used with experimental pK_a data. The procedure was analyzed at different levels of theory. The solvent was described by the solvation model density (SMD) model, including or not explicit water molecules, and three training sets were tested. The best performance under any condition was obtained by the G4CEP method with a mean absolute error close to 0.5 units of pK_a and an uncertainty around ±1 unit of pK_a for any training set including or excluding explicit solvent molecules. PM6 and AM1 performed very well with average absolute errors below 0.75 units of pK_a but with uncertainties up to ±2 units of pK_a, using only the SMD solvent model. Density functional theory (DFT) results were highly dependent on the basis functions and explicit water molecules. The best performance was observed for the local spin density approximation (LSDA) functional in almost all calculations and under certain conditions, as high as those obtained by G4CEP. Basis set complexity and explicit solvent molecules were important factors to control DFT calculations. The training set molecules should consider the diversity of compounds.

Legionella pneumophila sg1-sensing signal enhancement using a novel electrochemical immunosensor in dynamic detection mode

This work presents a comparison between static and dynamic modes of biosensing using a novel microfluidic assay for continuous and quantitative detection of Legionella pneumophila sg1 in artificial water samples. A self-assembled monolayer of 16-amino-1-hexadecanethiol (16-AHT) was covalently linked to a gold substrate, and the resulting modified surface was used to immobilize an anti-Legionella pneumophila monoclonal antibody (mAb). The modified surfaces formed during the biosensor functionalization steps were characterized using electrochemical measurements and microscopic imaging techniques. Under static conditions, the biosensor exhibited a wide linear response range from 10 to 10⁸ CFU/mL and a detection limit of 10 CFU/mL. Using a microfluidic system, the biosensor responses exhibited a linear relationship for low bacterial concentrations ranging from 10 to 10³ CFU/mL under dynamic conditions and an enhancement of sensing signals by a factor of 4.5 compared to the sensing signals obtained under static conditions with the same biosensor for the detection of Legionella cells in artificially contaminated samples.

Automated exploration of the low-energy chemical space with fast quantum chemical methods

We propose and discuss an efficient scheme for the in silico sampling for parts of the molecular chemical space by semiempirical tight-binding methods combined with a meta-dynamics driven search algorithm. The focus of this work is set on the generation of proper thermodynamic ensembles at a quantum chemical level for conformers, but similar procedures for protonation states, tautomerism and non-covalent complex geometries are also discussed. The conformational ensembles consisting of all significantly populated minimum energy structures normally form the basis of further, mostly DFT computational work, such as the calculation of spectra or macroscopic properties. By using basic quantum chemical methods, electronic effects or possible bond breaking/formation are accounted for and a very reasonable initial energetic ranking of the candidate structures is obtained. Due to the huge computational speedup gained by the fast low-cost quantum chemical methods, overall short computation times even for systems with hundreds of atoms (typically drug-sized molecules) are achieved. Furthermore, specialized applications, such as sampling with implicit solvation models or constrained conformational sampling for transition-states, metal-, surface-, or noncovalently bound complexes are discussed, opening many possible applications in modern computational chemistry and drug discovery. The procedures have been implemented in a freely available computer code called CREST, that makes use of the fast and reliable GFNn-xTB methods.

Semiempirical Quantum-Chemical Methods with Orthogonalization and Dispersion Corrections

We present two new semiempirical quantum-chemical methods with orthogonalization and dispersion corrections: ODM2 and ODM3 (ODM x). They employ the same electronic structure model as the OM2 and OM3 (OM x) methods, respectively. In addition, they include Grimme's dispersion correction D3 with Becke-Johnson damping and three-body corrections E ABC for Axilrod-Teller-Muto dispersion interactions as integral parts. Heats of formation are determined by adding explicitly computed zero-point vibrational energy and thermal corrections, in contrast to standard MNDO-type and OM x methods. We report ODM x parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine that are optimized with regard to a wide range of carefully chosen state-of-the-art reference data. Extensive benchmarks show that the ODM x methods generally perform better than the available MNDO-type and OM x methods for ground-state and excited-state properties, while they describe noncovalent interactions with similar accuracy as OM x methods with a posteriori dispersion corrections.

High accuracy quantum-chemistry-based calculation and blind prediction of macroscopic pKa values in the context of the SAMPL6 challenge

Recent advances in the development of low-cost quantum chemical methods have made the prediction of conformational preferences and physicochemical properties of medium-sized drug-like molecules routinely feasible, with significant potential to advance drug discovery. In the context of the SAMPL6 challenge, macroscopic pKa values were blindly predicted for a set of 24 of such molecules. In this paper we present two similar quantum chemical based approaches based on the high accuracy calculation of standard reaction free energies and the subsequent determination of those pKa values via a linear free energy relationship. Both approaches use extensive conformational sampling and apply hybrid and double-hybrid density functional theory with continuum solvation to calculate free energies. The blindly calculated macroscopic pKa values were in excellent agreement with the experiment.

Absolute and relative pKa predictions via a DFT approach applied to the SAMPL6 blind challenge

In this work, quantum mechanical methods were used to predict the microscopic and macroscopic pK_a values for a set of 24 molecules as a part of the SAMPL6 blind challenge. The SMD solvation model was employed with M06-2X and different basis sets to evaluate three pK_a calculation schemes (direct, vertical, and adiabatic). The adiabatic scheme is the most accurate approach (RMSE = 1.40 pK_a units) and has high correlation (R² = 0.93), with respect to experiment. This approach can be improved by applying a linear correction to yield an RMSE of 0.73 pK_a units. Additionally, we consider including explicit solvent representation and multiple lower-energy conformations to improve the predictions for outliers. Adding three water molecules explicitly can reduce the error by 2-4 pK_a units, with respect to experiment, whereas including multiple local minima conformations does not necessarily improve the pK_a prediction.

Correlations between the 1H NMR chemical shieldings and the pKa values of organic acids and amines

The acid dissociation constants and ¹H NMR chemical shieldings of organic compounds are important properties that have attracted much research interest. However, few studies have explored the relationship between these two properties. In this work, we theoretically studied the NMR chemical shifts of a series of carboxylic acids and amines in the gas phase and in aqueous solution. It was found that the negative logarithms of the experimental acid dissociation constants (i.e., the pK_a values) of the organic acids and amines in aqueous solution correlate almost linearly with the corresponding calculated NMR chemical shieldings. Key factors that affect the theoretically predicted pK_a values are discussed in this paper. The present work provides a new way to predict the pK_a values of organic/biochemical compounds. Graphical abstract The chemical shielding values of organic acids and amines correlate near linearly with their corresponding pK_a values.

Fast and accurate prediction of the regioselectivity of electrophilic aromatic substitution reactions

While computational prediction of chemical reactivity is possible it usually requires expert knowledge and there are relatively few computational tools that can be used by a bench chemist to help guide synthesis. The RegioSQM method for predicting the regioselectivity of electrophilic aromatic substitution reactions of heteroaromatic systems is presented in this paper. RegioSQM protonates all aromatic C-H carbon atoms and identifies those with the lowest free energies in chloroform using the PM3 semiempirical method as the most nucleophilic center. These positions are found to correlate qualitatively with the regiochemical outcome in a retrospective analysis of 96% of more than 525 literature examples of electrophilic aromatic halogenation reactions. The method is automated and requires only a SMILES string of the molecule of interest, which can easily be generated using chemical drawing programs such as ChemDraw. The computational cost is 1-10 minutes per molecule depending on size, using relatively modest computational resources and the method is freely available via a web server at ; http://www.regiosqm.org. RegioSQM should therefore be of practical use in the planning of organic synthesis.

Prediction of pKa Values for Druglike Molecules Using Semiempirical Quantum Chemical Methods

Rapid yet accurate pK_a prediction for druglike molecules is a key challenge in computational chemistry. This study uses PM6-DH+/COSMO, PM6/COSMO, PM7/COSMO, PM3/COSMO, AM1/COSMO, PM3/SMD, AM1/SMD, and DFTB3/SMD to predict the pK_a values of 53 amine groups in 48 druglike compounds. The approach uses an isodesmic reaction where the pK_a value is computed relative to a chemically related reference compound for which the pK_a value has been measured experimentally or estimated using a standard empirical approach. The AM1- and PM3-based methods perform best with RMSE values of 1.4-1.6 pH units that have uncertainties of ±0.2-0.3 pH units, which make them statistically equivalent. However, for all but PM3/SMD and AM1/SMD the RMSEs are dominated by a single outlier, cefadroxil, caused by proton transfer in the zwitterionic protonation state. If this outlier is removed, the RMSE values for PM3/COSMO and AM1/COSMO drop to 1.0 ± 0.2 and 1.1 ± 0.3, whereas PM3/SMD and AM1/SMD remain at 1.5 ± 0.3 and 1.6 ± 0.3/0.4 pH units, making the COSMO-based predictions statistically better than the SMD-based predictions. For pK_a calculations where a zwitterionic state is not involved or proton transfer in a zwitterionic state is not observed, PM3/COSMO or AM1/COSMO is the best pK_a prediction method; otherwise PM3/SMD or AM1/SMD should be used. Thus, fast and relatively accurate pK_a prediction for 100-1000s of druglike amines is feasible with the current setup and relatively modest computational resources.

Prediction of pKa values using the PM6 semiempirical method

The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences (MADs) of 0.6-0.7 pH units. For carboxylic acids, the accuracy (0.7-1.0 pH units) is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5-0.6), slightly worse (0.5-1.0), and worse (1.0-2.5), provided that di- and tri-ethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug-like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit) error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result, most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2-5 min per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for high-throughput screening using on the order of 100 core processors.