Estimation of stability constants of copper(II) and nickel(II) chelates with dipeptides by using topological indices

Evaluation of the Key Structural Features of Various Butyrylcholinesterase Inhibitors Using Simple Molecular Descriptors

In this study, we developed several QSAR models based on simple descriptors (such as topological and constitutional) to estimate butyrylcholinesterase (BChE) inhibition potency, pK_i (or pIC₅₀), of a set of 297 (289 after exclusion of outliers) structurally different compounds. The models were similar to the best model that we obtained previously for acetylcholinesterase AChE and were based on the valence molecular connectivity indices of second and third order (²χ^v and ³χ^v), the number of aliphatic hydroxyl groups (nOH), AlogP Ghose-Crippen octanol-water partition coeff. (logP), and O-060-atom-centred fragments (Al-O-Ar, Ar-O-Ar, R..O..R and R-O-C=X). The best models with two and three descriptors yielded r = 0.787 and S.E. = 0.89, and r = 0.827 and S.E. = 0.81, respectively. We also correlated nine scoring functions, calculated for 20 ligands whose complexes with BChE we found in the Protein Data Bank as crystal structures to pK_i (or pIC₅₀). The best correlations yielded PLP1 and PLP2 (Piecewise Linear Pairwise potential functions) with r = 0.619 and 0.689, respectively. Correlation with certain simple topological and constitutional descriptors yielded better results, e.g., ³χ^v (r = 0.730), on the same set of compounds (N = 20).

Prediction models on biomass and yield of rice affected by metal (oxide) nanoparticles using nano-specific descriptors

The use of in silico tools to investigate the interactions between metal (oxide) nanoparticles (NPs) and plant biological responses is preferred because it allows us to understand molecular mechanisms and improve prediction efficiency by saving time, labor, and cost. In this study, four models (C5.0 decision tree, discriminant function analysis, random forest, and stepwise multiple linear regression analysis) were applied to predict the effect of NPs on rice biomass and yield. Nano-specific descriptors (size-dependent molecular descriptors and image-based descriptors) were introduced to estimate the behavior of NPs in plants to appropriately represent the wide space of NPs. The results showed that size-dependent molecular descriptors (e.g., E-state and connectivity indices) and image-based descriptors (e.g., extension, area, and minimum ferret diameter) were associated with the behavior of NPs in rice. The performance of the constructed models was within acceptable ranges (correlation coefficient ranged from 0.752 to 0.847 for biomass and from 0.803 to 0.905 for yield, while the accuracy ranged from 64% to 77% for biomass and 81% to 89% for yield). The developed model can be used to quickly and efficiently evaluate the impact of NPs under a wide range of experimental conditions and sufficient training data.

Irving-Williams order in the framework of connectivity index ³χv enables simultaneous prediction of stability constants of bivalent transition metal complexes

Logarithms of stability constants, log K₁ and log β₂, of the first transition series metal mono- and bis-complexes with any of four aliphatic amino acids (glycine, alanine, valine and leucine) decrease monotonously with third order valence connectivity index, ³χ^v, from Cu²⁺ to Mn²⁺. While stability of the complexes with the same metal is linearly dependent on ³χ^v, stability constants of Mn²⁺, Fe²⁺, Co²⁺, and Ni²⁺complexes with the same ligand show a quadratic dependence on ³χ^v. As Cu²⁺ complexes deviate significantly from quadratic functions, models for the simultaneous estimation of the stability constants, yielding r = 0.999 (S.E. = 0.05) and r = 0.998 (S.E. = 0.11), for log K₁ and log β₂, respectively, were developed only for Mn²⁺, Fe²⁺, Co²⁺, and Ni²⁺ complexes with amino acids.