Predictions of Colloidal Molecular Aggregation Using AI/ML Models.

To facilitate the triage of hits from small molecule screens, we have used various AI/ML techniques and experimentally observed data sets to build models aimed at predicting colloidal aggregation of small organic molecules in aqueous solution. We have found that Naïve Bayesian and deep neural networks outperform logistic regression, recursive partitioning tree, support vector machine, and random forest techniques by having the lowest balanced error rate (BER) for the test set. Derived predictive classification models consistently and successfully discriminated aggregator molecules from nonaggregator hits.

Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences.

We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed previously, which encodes the intrinsic dynamic properties of the naturally occurring amino acids. We Fourier analyze the resulting sequences.

Identification and pharmacological characterization of 3,6-diazabicyclo[3.1.1]heptane-3-carboxamides as novel ligands for the α4β2 and α6/α3β2β3 nicotinic acetylcholine receptors (nAChRs).

We have synthesized a novel series of compounds, 3,6-diazabicyclo[3.1.1]heptane-3-carboxamides, targeting both the α4β2 and α6/α3β2β3 nAChRs.

Comparative study on the use of docking and Bayesian categorization to predict ligand binding to nicotinic acetylcholine receptors (nAChRs) subtypes.

We have carried out a comparative study between docking into homology models and Bayesian categorization, as applied to virtual screening of nicotinic ligands for binding at various nAChRs subtypes (human and rat α4β2, α7, α3β4, and α6β2β3). We found that although results vary with receptor subtype, Bayesian categorization exhibits higher accuracy and enrichment than unconstrained docking into homology models. However, docking accuracy is improved when one sets up a hydrogen-bond (HB) constraint between the cationic center of the ligand and the main-chain carbonyl group of the conserved Trp-149 or its homologue (a residue involved in cation-π interactions with the ligand basic nitrogen atom).

Computational studies of novel carbonyl-containing diazabicyclic ligands interacting with α4β2 nicotinic acetylcholine receptor (nAChR) reveal alternative binding modes.

We have carried out computational studies on interactions of diazabicyclic amide analogs with α4β2 nAChR using homology modeling, docking and pharmacophore elucidation techniques. We have found alternative ligand binding modes in most cases. All these diverse poses exhibit the quintessential hydrogen-bonding interaction between the ligand basic nitrogen and the backbone carbonyl oxygen atom of the highly conserved Trp-149.

QM-polarized ligand docking accurately predicts the trend in binding affinity of a series of arylmethylene quinuclidine-like derivatives at the α4β2 and α3β4 nicotinic acetylcholine receptors (nAChRs).

Compounds containing a quinuclidine scaffold are promising drug candidates for pharmacological management of the central nervous system (CNS) pathologies implicating nAChRs. We have carried out binding affinity and in-silico docking studies of arylmethylene quinuclidine-like derivatives at the α4β2 receptor using in-vitro receptor binding assay and comparative modeling, respectively. We found that introducing a hydrogen-bond acceptor into the 3-benzylidene quinuclidine derivative resulted in a 266-fold increase in binding affinity and confers agonism properties.

Novel nicotinic acetylcholine receptor agonists containing carbonyl moiety as a hydrogen bond acceptor.

A novel series of α4β2 nAChR agonists lacking common pyridine or its bioisosteric heterocycle have been disclosed. Essential pharmacophoric elements of the series are exocyclic carbonyl moiety as a hydrogen bond acceptor and secondary amino group within diaza- or azabicyclic scaffold. Computer modeling studies suggested that molecular shape of the ligand also contributes to promotion of agonism.

Pharmacological properties and predicted binding mode of arylmethylene quinuclidine-like derivatives at the α3β4 nicotinic acetylcholine receptor (nAChR).

We have carried out a pharmacological evaluation of arylmethylene quinuclidine derivatives interactions with human α3β4 nAChRs subtype, using cell-based receptor binding, calcium-influx, electrophysiological patch-clamp assays and molecular modeling techniques. We have found that the compounds bind competitively to the α3β4 receptor with micromolar affinities and some of the compounds behave as non-competitive antagonists (compounds 1, 2 and 3), displaying submicromolar IC(50) values. These evidences suggest a mixed mode of action for these compounds, having interactions at the orthosteric site and more pronounced interactions at an allosteric site to block agonist effects.

3D molecular descriptors important for clinical success.

The pharmacokinetic and safety profiles of clinical drug candidates are greatly influenced by their requisite physicochemical properties. In particular, it has been shown that 2D molecular descriptors such as fraction of Sp3 carbon atoms (Fsp3) and number of stereo centers correlate with clinical success. Using the proteomic off-target hit rate of nicotinic ligands, we found that shape-based 3D descriptors such as the radius of gyration and shadow indices discriminate off-target promiscuity better than do Fsp3 and the number of stereo centers.

Discovery of (2S,3R)-N-[2-(pyridin-3-ylmethyl)-1-azabicyclo[2.2.2]oct-3-yl]benzo[b]furan-2-carboxamide (TC-5619), a selective α7 nicotinic acetylcholine receptor agonist, for the treatment of cognitive disorders.

(2S,3R)-N-[2-(Pyridin-3-ylmethyl)-1-azabicyclo[2.2.2]oct-3-yl]benzo[b]furan-2-carboxamide (7a, TC-5619), a novel selective agonist of the α7 neuronal nicotinic acetylcholine receptor, has been identified as a promising drug candidate for the treatment of cognitive impairment associated with neurological disorders.

Comparison of acetylcholine receptor interactions of the marine toxins, 13-desmethylspirolide C and gymnodimine.

The interaction of 13-desmethylspirolide C (SPX-desMe-C) and gymnodimine with several nicotinic and muscarinic acetylcholine receptors was investigated. Interaction at the muscarinic receptors was minimal. At nicotinic receptors, both SPX-desMe-C and gymnodimine displayed greatest affinity for the α7 receptor.

Discovery of novel α7 nicotinic acetylcholine receptor ligands via pharmacophoric and docking studies of benzylidene anabaseine analogs.

Based on pharmacophore elucidation and docking studies on interactions of benzylidene anabaseine analogs with AChBPs and α7 nAChR, novel spirodiazepine and spiroimidazoline quinuclidine series have been designed. Binding studies revealed that some of hydrogen-bond donor containing compounds exhibit improved affinity and selectivity for the α7 nAChR subtype in comparison with most potent metabolite of GTS-21, 3-(4-hydroxy-2-methoxybenzylidene)-anabaseine. Hydrophobicity and rigidity of the ligand also contribute into its binding affinity.

Docking studies of benzylidene anabaseine interactions with α7 nicotinic acetylcholine receptor (nAChR) and acetylcholine binding proteins (AChBPs): application to the design of related α7 selective ligands.

AChBPs isolated from Lymnaea stagnalis (Ls), Aplysia californica (Ac) and Bulinus truncatus (Bt) have been extensively used as structural prototypes to understand the molecular mechanisms that underlie ligand-interactions with nAChRs [1]. Here, we describe docking studies on interactions of benzylidene anabaseine analogs with AChBPs and α7 nAChR. Results reveal that docking of these compounds using Glide software accurately reproduces experimentally-observed binding modes of DMXBA and of its active metabolite, in the binding pocket of Ac.