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.

Confirmed diagnosis of classic Wiskott-Aldrich syndrome in East Africa: a case report.

Introduction: Wiskott-Aldrich syndrome is a rare X-linked primary immunodeficiency that mostly presents with a classic triad of eczema, microthrombocytopenia, recurrent infections, and increased risk of autoimmunity/malignancies.

Case Presentation: We present an 8-month-old African male, born from nonconsanguineous parents and who presented with a history of eczematous skin rash since day 9 of life, with recurrent sinus infections, otitis media, and skin abscesses. An elder male sibling who had similar symptoms passed away during infancy.

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).