Solv-ccCA: Implicit Solvation and the Correlation Consistent Composite Approach for the Determination of pKa

SCINE-Software for chemical interaction networks

The software for chemical interaction networks (SCINE) project aims at pushing the frontier of quantum chemical calculations on molecular structures to a new level. While calculations on individual structures as well as on simple relations between them have become routine in chemistry, new developments have pushed the frontier in the field to high-throughput calculations. Chemical relations may be created by a search for specific molecular properties in a molecular design attempt, or they can be defined by a set of elementary reaction steps that form a chemical reaction network. The software modules of SCINE have been designed to facilitate such studies. The features of the modules are (i) general applicability of the applied methodologies ranging from electronic structure (no restriction to specific elements of the periodic table) to microkinetic modeling (with little restrictions on molecularity), full modularity so that SCINE modules can also be applied as stand-alone programs or be exchanged for external software packages that fulfill a similar purpose (to increase options for computational campaigns and to provide alternatives in case of tasks that are hard or impossible to accomplish with certain programs), (ii) high stability and autonomous operations so that control and steering by an operator are as easy as possible, and (iii) easy embedding into complex heterogeneous environments for molecular structures taken individually or in the context of a reaction network. A graphical user interface unites all modules and ensures interoperability. All components of the software have been made available as open source and free of charge.

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.

Easy to Use DFT Approach for Computational pKa Determination of Carboxylic Acids

In pKa computational determination, the challenge in exploring and fostering new methodologies and approaches goes in parallel with the amelioration of computational performances. In this paper a "ready to use methodology" has been compared to other strategies, such as the re-shaping in solvation cavity (Bondi radius re-shaping), wanting to assess its reliability in predicting the pKa of a broad list of carboxylic acids. Thus, the functionals B3LYP and CAM-B3LYP have been selected, using SMD as continuum solvation model. Exploiting our previous results, two water molecules were made explicit on the reaction centre. Data show that our model (CAM-B3LYP/2H2 O) is capable to accurately predict pKa, leading to mean absolute error (MAE) values lower than 0.5. Noteworthy, good results were achieved in computing the pKa of substituents bearing nitro and cyano groups. Focusing on B3LYP, eventually remarkable outputs were obtained only when Bondi correction was applied to the complex with two water molecules. Hence, massive outcomes were obtained in foreseeing the trichloro and trifluoro acetic acid pKa. These findings demonstrated that no complex level of theory nor external factor is required to accurately predict carboxylic acids pKa, with MAE well below 0.5 units.

Predicting pKa Values of Quinols and Related Aromatic Compounds with Multiple OH Groups

Quinonoid compounds play central roles as redox-active agents in photosynthesis and respiration and are also promising replacements for inorganic materials currently used in batteries. To design new quinonoid compounds and predict their state of protonation and redox behavior under various conditions, their pK_a values must be known. Methods that can predict the pK_a values of simple phenols cannot reliably handle complex analogues in which multiple OH groups are present and may form intramolecular hydrogen bonds. We have therefore developed a straightforward method based on a linear relationship between experimental pK_a values and calculated differences in energy between quinols and their deprotonated forms. Simple adjustments allow reliable predictions of pK_a values when intramolecular hydrogen bonds are present. Our approach has been validated by showing that predicted and experimental values for over 100 quinols and related compounds differ by an average of only 0.3 units. This accuracy makes it possible to select proper pK_a values when experimental data vary, predict the acidity of quinols and related compounds before they are made, and determine the sites and orders of deprotonation in complex structures with multiple OH groups.

SAMPL6 logP challenge: machine learning and quantum mechanical approaches

Two different types of approaches: (a) approaches that combine quantitative structure activity relationships, quantum mechanical electronic structure methods, and machine-learning and, (b) electronic structure vertical solvation approaches, were used to predict the logP coefficients of 11 molecules as part of the SAMPL6 logP blind prediction challenge. Using electronic structures optimized with density functional theory (DFT), several molecular descriptors were calculated for each molecule, including van der Waals areas and volumes, HOMO/LUMO energies, dipole moments, polarizabilities, and electrophilic and nucleophilic superdelocalizabilities. A multilinear regression model and a partial least squares model were used to train a set of 97 molecules. As well, descriptors were generated using the molecular operating environment and used to create additional machine learning models. Electronic structure vertical solvation approaches considered include DFT and the domain-based local pair natural orbital methods combined with the solvated variant of the correlation consistent composite approach.

Prediction of pKa s of Late Transition-Metal Hydrides via a QM/QM Approach

Three implicit solvation models, the conductor-like polarizable continuum model (C-PCM), the conductor-like screening model (COSMO), and universal implicit solvent model (SMD), combined with a hybrid two layer QM/QM approach (ONIOM), were utilized to calculate the pK_a values, using a direct thermodynamic scheme, of a set of Group 10 transition metal (TM) hydrides in acetonitrile. To obtain the optimal combination of quantum methods for ONIOM calculations with implicit solvation models, the influence of factors, such as the choice of density functional and basis set, the atomic radii used to build a cavity in the solvent, and the size of the model system in an ONIOM scheme, was examined. Additionally, the impact of Grimme's empirical dispersion correction and exact exchange was also investigated. The results were calibrated by experimental data. This investigation provides insight about effective models for the prediction of thermodynamic properties of TM-containing complexes with bulky ligands. © 2019 Wiley Periodicals, Inc.

Quantum chemical predictions of water-octanol partition coefficients applied to the SAMPL6 logP blind challenge

Theoretical approaches for predicting physicochemical properties are valuable tools for accelerating the drug discovery process. In this work, quantum chemical methods are used to predict water-octanol partition coefficients as a part of the SAMPL6 blind challenge. The SMD continuum solvent model was employed with MP2 and eight DFT functionals in conjunction with correlation consistent basis sets to determine the water-octanol transfer free energy. Several tactics towards improving the predictions of the partition coefficient were examined, including increasing the quality of basis sets, considering tautomerization, and accounting for inhomogeneities in the water and n-octanol phases. Evaluation of these various schemes highlights the impact of modeling approaches across different methods. With the inclusion of tautomers and adjustments to the permittivity constants, the best predictions were obtained with smaller basis sets and the O3LYP functional, which yielded an RMSE of 0.79 logP units. The results presented correspond to the SAMPL6 logP submission IDs: DYXBT, O7DJK, and AHMTF.

Domain-based local pair natural orbital methods within the correlation consistent composite approach

Ab initio composite approaches have been utilized to model and predict main group thermochemistry within 1 kcal mol^-1 , on average, from well-established reliable experiments, primarily for molecules with less than 30 atoms. For molecules of increasing size and complexity, such as biomolecular complexes, composite methodologies have been limited in their application. Therefore, the domain-based local pair natural orbital (DLPNO) methods have been implemented within the correlation consistent composite approach (ccCA) framework, namely DLPNO-ccCA, to reduce the computational cost (disk space, CPU (central processing unit) time, memory) and predict energetic properties such as enthalpies of formation, noncovalent interactions, and conformation energies for organic biomolecular complexes including one of the largest molecules examined via composite strategies, within 1 kcal mol^-1 , after calibration with 119 molecules and a set of linear alkanes. © 2019 Wiley Periodicals, Inc.

Assessment of pKa Determination for Monocarboxylic Acids with an Accurate Theoretical Composite Method: G4CEP

For 22 monoprotic acids, pK_a values were calculated using the G4CEP composite theory. The solvation effect was included considering the continuous SMD solvation model; SMD and one explicit water molecule; and SMD, one water molecule, and linear correction with respect to the experimental pK_a values. The three tests provided mean absolute errors equal to 0.83, 0.51, and 0.30 pK_a units, respectively, indicating excellent performance of the G4CEP method. Comparison with density functional theory at the B3LYP and BMK levels showed that these results are quickly obtained but with a significant error. The best performance of the functionals was obtained from the combination of SMD, one explicit water molecule, linear regression correction, and basis set including diffuse functions. However, the dispersion of the results with DFT can lead to deviations of up to two pK_a units, whereas for G4CEP the largest deviations seldom exceed one pK_a unit.

An explicit-solvent hybrid QM and MM approach for predicting pKa of small molecules in SAMPL6 challenge

In this work we have developed a hybrid QM and MM approach to predict pKa of small drug-like molecules in explicit solvent. The gas phase free energy of deprotonation is calculated using the M06-2X density functional theory level with Pople basis sets. The solvation free energy difference of the acid and its conjugate base is calculated at MD level using thermodynamic integration. We applied this method to the 24 drug-like molecules in the SAMPL6 blind pKa prediction challenge. We achieved an overall RMSE of 2.4 pKa units in our prediction. Our results show that further optimization of the protocol needs to be done before this method can be used as an alternative approach to the well established approaches of a full quantum level or empirical pKa prediction methods.

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.

Prediction of CB[8] host-guest binding free energies in SAMPL6 using the double-decoupling method

This study reports the results of binding free energy calculations for CB[8] host-guest systems in the SAMPL6 blind challenge (receipt ID 3z83m). Force-field parameters were developed specific for each of host and guest molecules to improve configurational sampling. We used quantum mechanical (QM) implicit solvent calculations and QM force matching to determine non-bonded (partial atomic charges) and bonded terms, respectively. Free energy calculations were carried out using the double-decoupling method (DDM) combined with Hamiltonian replica exchange method (HREM) and Bennett acceptance ratio (BAR) method. The root mean square error (RMSE) of the predicted values using DDM with respect to the experimental results was 4.32 kcal/mol. The coefficient of determination (R²) and Kendall rank coefficient (τ) were 0.49 and 0.52, respectively, highest of all submissions. In addition, these were compared to the results obtained by umbrella sampling (US) and weighted histogram analysis method (WHAM). Overall, DDM achieved a higher prediction accuracy than the US method. Results are discussed in terms of parameterization and free energy simulations.

Weighted Averaging Scheme and Local Atomic Descriptor for pKa Prediction Based on Density Functional Theory

As a continuation of our work on developing a density functional theory-based pK_a predictor, we present conceptual improvements to our previously published shell model, which is a hierarchical organization of pK_a training sets and which, in principle, covers all chemical space. The improvements concern the way the studied chemical compound is associated with the data points from the training sets. By introducing a new descriptor of the local atomic environment which foregoes dependence on chemical bonding and connectivity, we are able to automatically locate molecules from the training set that are most relevant to the proton dissociation equilibrium under study. This new scheme leads to the prediction of a single pK_a value weighted across multiple training sets and thus patches a defect disclosed in the formulation of our previous model. Using the new parametrization approach, the pK_a prediction gets rid of outliers reported in previous applications of our approach, eliminates ambiguity in interpreting the results, and improves the overall accuracy. Our new treatment accounts for multiple conformations both on the level of energetics and parametrization. Illustrative results are shown for several types of chemical structures containing guanidine, amidine, amine, and phenol functional groups, and which are representative of practically important large and flexible drug-like molecules. Our method's performance is compared to the performance of other previously published pK_a prediction methods. Further possible improvements to the organization of the training sets and the potential application of our new local atomic descriptor to other kinds of parametrizations are discussed.

Partition coefficients for the SAMPL5 challenge using transfer free energies

SAMPL challenges (Mobley et al. in J Comput Aided Mol Des 28:135-150, 2014; Skillman in J Comput Aided Mol Des 26:473-474, 2012; Geballe in J Comput Aided Mol Des 24:259-279, 2010; Guthrie in J Phys Chem B 113:4501-4507, 2009) provide excellent opportunities to assess theoretical approaches on new data sets with a goal of gaining greater insight towards protein and ligand modeling. In the SAMPL5 experiment, cyclohexane-water partition coefficients were determined using a vertical solvation scheme in conjunction with the SMD continuum solvent model. Several DFT functionals partnered with correlation consistent basis sets were evaluated for the prediction of the partition coefficients. The approach chosen for the competition, a B3PW91 vertical solvation scheme, yields a mean absolute deviation of 1.9 logP units and performs well at estimating the correct hydrophilicity and hydrophobicity for the full SAMPL5 molecule set.