Interplay between Ionization and Tautomerism in Bioactive β-Enamino Ester-Containing Cyclic Compounds: Study of Annulated 1,2,3,6-Tetrahydroazocine Derivatives

Curutchet

et al. 2021

J Comput Aided Mol Des

Self Cite

Within the scope of SAMPL7 challenge for predicting physical properties, the Integral Equation Formalism of the Miertus-Scrocco-Tomasi (IEFPCM/MST) continuum solvation model has been used for the blind prediction of n-octanol/water partition coefficients and acidity constants of a set of 22 and 20 sulfonamide-containing compounds, respectively. The log P and pKa were computed using the B3LPYP/6-31G(d) parametrized version of the IEFPCM/MST model. The performance of our method for partition coefficients yielded a root-mean square error of 1.03 (log P units), placing this method among the most accurate theoretical approaches in the comparison with both globally (rank 8th) and physical (rank 2nd) methods. On the other hand, the deviation between predicted and experimental pKa values was 1.32 log units, obtaining the second best-ranked submission. Though this highlights the reliability of the IEFPCM/MST model for predicting the partitioning and the acid dissociation constant of drug-like compounds compound, the results are discussed to identify potential weaknesses and improve the performance of the method.

Section: Pk a Predictionsupporting

confidence: 89%

Section: Pk a Computationmentioning

confidence: 99%

Prediction of n-octanol/water partition coefficients and acidity constants (pKa) in the SAMPL7 blind challenge with the IEFPCM-MST model

Viayna

Curutchet

et al. 2021

J Comput Aided Mol Des

Self Cite

“…Therefore, the apparent ionic partition became more significant for entries with higher delta values (see Figures 6a and Figure 7a). This result is very promising, because despite being an experimental descriptor, there are computational methods to determine the pKa that include first principles models [116][117][118][119] as well as machine learning tools 120,121 , so the descriptor delta can be automated and easily used to classify molecules according to the lipophilicity formalisms analyzed here. In fact, the root-mean-square error (RMSE) between predicted pKa values using the software ChemAxon and experimental data in our database is just 0.58 log units and the squared coefficient of determination (R 2 ) of 0.95 (see yet a small amount of water can dissolve in octanol at room temperature (~ 2.9 mol/kg).…”

Section: Resultsmentioning

confidence: 99%

Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?

Aguilar

Sánchez

et al. 2023

Preprint

Lipophilicity is a physicochemical property with wide relevance in drug design and also applied in areas such as food chemistry, environmental chemistry, and computational biology. This descriptor strongly influences the absorption, distribution, permeability, bioaccumulation, protein-binding, and biological activity of bioorganic compounds. Lipophilicity is commonly expressed as the n-octanol/water partition coefficient (PN) for neutral molecules, whereas for molecules with ionizable groups, the distribution coefficient (D) at a given pH is used. The logDpH is usually predicted using a pH correction over the logPN using the pKa of ionizable molecules, while often ignoring the ionic partition (PI) because of the challenge of predicting the partitioning of the charged species. In this work, we studied the impact of PI on the prediction of lipophilicity of small drug-like molecules by modeling 225 logDpH of a set of experimental values using both the formalism that takes into account a pH correction (see Eq. 1) and the one considering the partition of ionic species (see Eq. 2). Our findings show that a better calculation is obtained by considering the ionic partition while ignoring its contribution can lead to inadequate computational predictions. In this context, we developed machine learning algorithms to determine in which cases the PI should be considered. The results indicate that small, compact, and hydrophobic molecules with a higher likelihood of being in their ionic state at specific pH values, were better modeled using Eq. 2. Finally, we validated our findings using a test and external set where the logistic regressions, random forest classifications, and support vector machine models predicted the better formalism to determine the logDpH for each molecule with high accuracies, sensitivities, and specificities.

“…Therefore, the apparent ionic partition becomes more significant for entries with higher delta values (Figures 11a and 12a). This result is very promising, because despite being an experimental descriptor, there are computational methods to determine pKa that include first-principles models [133][134][135][136] as well as machine learning tools 137,138 .Thus, the descriptor delta can be automated and easily used to classify molecules according to the lipophilicity formalisms analyzed here. In fact, the root-mean-square error (RMSE) between predicted pKa values using the software ChemAxon and experimental data in our database is just 0.58 log units and the squared coefficient of determination (R 2 ) of 0.95 (see Figure S9)…”

Section: Machine Learning Models To Guide the Choice Of Ph-dependent ...mentioning

confidence: 99%

Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?

Aguilar

Sánchez

et al. 2023

Preprint

Lipophilicity is a physicochemical property with wide relevance in drug design and is applied in areas such as food chemistry, environmental chemistry, and computational biology. This descriptor strongly influences the absorption, distribution, permeability, bioaccumulation, protein-binding, and biological activity of bioorganic compounds. Lipophilicity is commonly expressed as the n-octanol/water partition coefficient (PN) for neutral molecules, whereas for molecules with ionizable groups, the distribution coefficient (D) at a given pH is used. The logDpH is usually predicted using a pH correction over the logPN using the pKa of ionizable molecules, while often ignoring the apparent ionic partition (PIapp) because of the challenge of predicting the partitioning of the charged species and/or related species (e.g., ion-pairs, counterions, molecular aggregates). In this work, we studied the impact of "P" _"I" ^"app" on the prediction of both the experimental lipophilicity of small molecules and experimental lipophilicity-based applications and metrics such as lipophilic efficiency (LipE), distribution of spiked drugs in milk products, and pH-dependent partition of water contaminants in synthetic passive samples such as silicones. Our findings show that better predictions are obtained by considering the apparent ionic partition, whereas ignoring its contribution can lead to inadequate experimental simplifications and/or computational predictions. In this context, we developed machine learning algorithms to determine the cases that "PIapp" should be considered. The results indicate that small, rigid, and unsaturated molecules with logPN close to zero, which present a significant proportion of ionic species in the aqueous phase, were better modeled using the apparent ionic partition (PIapp). In addition, we validated our findings using a test and two external sets, which included small molecules and amino acid analogs, where the logistic regressions, random forest classifications, and support vector machine models predicted better formalism to determine the logDpH for each molecule with high accuracies, sensitivities, and specificities. Finally, our findings can serve as guidance to the scientific community working in early-stage drug design, food, and environmental chemistry who deal with ionizable molecules, to determine a priori which pH-dependent lipophilicity profile should be used in their research and applications depending on the structure of a substance.