Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage

Abstract

A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.

Concurrent Optimizations of Efficacy and Blood–Brain Barrier Permeability in New Macrocyclic LRRK2 Inhibitors for Potential Parkinson’s Disease Therapeutics

“…In the final phase of inhibitor design, the BBB permeabilities of these 27 derivatives were further assessed using a 3D-QSAR approach, grounded in quantum mechanics and proven effective for predicting diverse drug-like molecules’ pharmacological properties. , The prediction of BBB permeability involved calculating the logarithm of the brain-plasma partition coefficient (LogBB). To develop a reliable LogBB prediction model, experimental LogBB data were compiled from the literature for molecules varying in atomic composition, shape, and size .…”

Machine Learning-Assisted Computational Screening of Adhesive Molecules Derived from Dihydroxyphenyl Alanine

“…Moreover, the 3D ESP can be accurately calculated by using first-principles methods such as the DFT or Hartree–Fock (HF) theory. Previously, this QM-3D descriptor has proven to be successful for QSPR modeling of various physicochemical properties of drug candidate molecules. , …”

“…Among 2000 rotamers, the rotamer giving the highest value of E ij is taken to be optimally aligned and is further used to calculate ESP as the descriptor. This protocol of alignment proves to be useful in deriving an accurate 3D-QSPR model for various molecular properties. , All of the molecules are kept neutral in calculating E ij , in order to retain their original electronic and structural features.…”

“…Previously, this QM-3D descriptor has proven to be successful for QSPR modeling of various physicochemical properties of drug candidate molecules. 36,37 In order to implement the present QM-3D ESP descriptor, the adsorbate molecules in the present data set should be structurally aligned first. Such a structural alignment is nontrivial, however, especially in the case where molecules have a wide range of molecular weights (MWs).…”

“…This protocol of alignment proves to be useful in deriving an accurate 3D-QSPR model for various molecular properties. 36,37 All of the molecules are kept neutral in calculating E ij , in order to retain their original electronic and structural features. Figure 2 illustrates the final results for the structural alignments of the molecules in each subset.…”