The Halogen Bond

The halogen bond occurs when there is evidence of a net attractive interaction between an electrophilic region associated with a halogen atom in a molecular entity and a nucleophilic region in another, or the same, molecular entity. In this fairly extensive review, after a brief history of the interaction, we will provide the reader with a snapshot of where the research on the halogen bond is now, and, perhaps, where it is going. The specific advantages brought up by a design based on the use of the halogen bond will be demonstrated in quite different fields spanning from material sciences to biomolecular recognition and drug design

Development of In Silico Tools to Predict the Behavior of Per- and Polyfluoroalkyl Substances (PFAS) in Biological Systems

Per and polyfluoroalkyl substances (PFAS) are a group of chemicals that have been widely used in industrial and consumer products for decades. Recent estimates suggest there are over 4000 PFAS on the global market. However, many of these have very little information available about their potential hazards. Given the vast number of PFAS, a three-level hierarchical framework that includes permeability-limited physiologically based toxicokinetic (PBTK) model, molecular dynamics (MD) based workflow and machine learning (ML) based quantitative structure-activity relationships (QSAR) was proposed to inform the toxicokinetics, bioaccumulation and toxicity of PFAS. The PBTK model was developed to estimate the toxicokinetic and tissue distribution of perfluorooctanoic acid (PFOA) in male rats; the hierarchical Bayesian analysis was used to reduce the uncertainty of parameters and improve the robustness of the PBTK model. By comparing with different experimental studies, most of the predicted plasma toxicokinetic (e.g., half-life) and tissue distribution fell well within a factor of 2.0 of the measured data. Moreover, a modeling workflow that combines molecular docking and MD simulation techniques was developed to estimate the binding affinity of PFAS for liver-type fatty acid binding protein (LFABP). The results suggest that EEA and ADONA are at least as strongly bound to rat LFABP as perfluoroheptanoic acid (PFHpA), and to human LFABP as PFOA; both F-53 and F-53B have similar or stronger binding affinities than perfluorooctane sulfonate (PFOS). In addition, human, rat, chicken, and rainbow trout had similar binding affinities to one another for each tested PFAS, whereas Japanese medaka and fathead minnow had significantly weaker LFABP binding affinity for some PFAS. Finally, the ML-based QSAR model was developed to predict the bioactivity of around 4000 PFAS from the OECD report. Based on the collected PFAS dataset, a total of 5 different machine learning models were trained and validated that cover a variety of conventional models (i.e., logistic regression, random forest and multitask neural network) and advanced graph-based models (i.e., graph convolutional network and weave model). The model indicated that most of the biologically active PFAS have perfluoroalkyl chain lengths less than 12 and are categorized into fluorotelomer-related compounds and perfluoroalkyl acids

Applications and Development of the MMPBSA Method for Rational Drug Design

The growing cost of new drugs has become a concern for the biopharmaceutical industry, which depends on innovation to sustain itself. As a result, computational methods have been increasingly implemented into the drug design workflow in an effort to reduce the cost of finding new lead candidates. Here, we focus on several applications and the development of the Molecular Mechanics Poisson-Boltzmann (MMPBSA) method for its use in rational drug design efforts. Chapter 1 demonstrates the use of computational methods in the analysis and design of anti-A?? antibodies for their prospective use in the treatment of Alzheimer’s Disease. Chapter 2 applies our single dielectric implicit membrane model to MMPBSA calculations of the membrane-bound human purinergic platelet receptor, a prominent target for treating myocardial infarction and stroke. Chapter 3 documents the development, implementation, and application of a new heterogeneous dielectric implicit membrane model for MMPBSA calculations. Chapter 4 shows the validity of our method to parameterize the non-polar terms in a depth dependent manner within our implicit membrane model. This work as a whole demonstrates both the present utility and ongoing improvement of the MMPBSA method for its use in the rational design of new drugs