Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies

Continuum solvation models: Dissecting the free energy of solvation

The most usual self-consistent reaction field (SCRF) continuum models for the description of solvation within the quantum mechanical (QM) framework are reviewed, trying to emphasize their common roots as well as the inherent approximations assumed in the calculation of the free energy of solvation. Particular attention is also paid to the specific features involved in the development of current state-of-the-art QM SCRF continuum models. This is used to discuss the need to maintain a close correspondence between each SCRF formalism and the specific details entailing its parametrization, as well as the need to be cautious in analyzing the balance between electrostatic and non-electrostatic contributions to the solvation free energy between different SCRF models. Finally, special emphasis is given to the post-processing of the free energy of solvation to derive parameters providing a compact picture of the ability of a molecule to interact with different solvents, which can be of particular interest in biopharmaceutical studies

The Impact of theoretical chemistry on biology

Els avenços en les bases dels mètodes teòrics i l'espectacular desenvolupament de la potència de càlcul han fet possible progressar enormement en el somni dels fundadors de la química, és a dir, ser capaços d'estudiar amb mètodes computacionals el conjunt de processos químics. Actualment, la química teòrica està completant el darrer avenç: intentar esdevenir l'eina més recent per a comprendre la naturalesa química dels éssers vius. Aquesta revisió pretén mostrar com els mètodes de la química teòrica, originalment desenvolupats per a examinar molècules petites en fase gas, han evolucionat per a assolir la complexa descripció de sistemes biològics.Recent advances in theoretical formalism together with the dramatic development of computer infrastructure have allowed enormous progress in achieving the dream of the founders of chemistry: to submit the majority of chemical phenomena to calculation. Currently, theoretical chemistry is working towards reaching the next step: to become the ultimate tool to understand the chemical nature of living organisms. This review summarizes how techniques originally developed to represent small molecules in the gas phase have evolved such that they are able to describe the complex behaviors of biological systems

Force Field Optimization, Advanced Sampling, And Free Energy Methods With Gpu-Optimized Monte Carlo (gomc) Software

In this work, to address the sampling problem for systems at high densities and low temperatures, a generalized identity exchange algorithm is developed for grand canonical Monte Carlo simulations. The algorithm, referred to as Molecular Exchange Monte Carlo (MEMC), is implemented in the GPU-Optimized Monte Carlo (GOMC) software and may be applied to multicomponent systems of arbitrary molecular topology, and provides significant enhancements in the sampling of phase space over a wide range of compositions and temperatures. Three different approaches are presented for the insertion/deletion of the large molecules, and the pros and cons of each method are discussed. Next, the MEMC method is extended to Gibbs ensemble Monte Carlo (GEMC). The utility of the MEMC method is demonstrated through the calculation of the free energies of transfer of n-alkanes from vapor into liquid 1-octanol, n-hexadecane, and 2,2,4-trimethylpentane, using isobaric-isothermal GEMC simulations. Alternatively, for system with strong inter-molecular interaction (e.g. hydrogen bonds), it’s more efficient to calculate the free energies of transfer, using standard thermodynamic integration (TI) and free energy perturbation (FEP) methods. The TI and FEP free energy calculation methods are implemented in GOMC and utility of these methods are demonstrated by calculating the hydration and solvation free energies of fluorinated 1-octanol, to understand the role of fluorination on the interactions and partitioning of alcohols in aqueous and organic environments. Additionally, using GOMC, a transferable united-atom (UA) force field, based on Mie potentials, is optimized for alkynes to accurately reproduce experimental phase equilibrium properties. The performance of the optimized Mie potential parameters is assessed for 1-alkynes and 2-alkynes using grand canonical histogram-reweighting Monte Carlo simulations. For each compound, vapor-liquid coexistence curves, vapor pressures, heats of vaporization, critical properties, and normal boiling points are predicted and compared to experiment

Evaluation of the availability and applicability of computational approaches in the safety assessment of nanomaterials: Final report of the Nanocomput project

This is the final report of the Nanocomput project, the main aims of which were to review the current status of computational methods that are potentially useful for predicting the properties of engineered nanomaterials, and to assess their applicability in order to provide advice on the use of these approaches for the purposes of the REACH regulation. Since computational methods cover a broad range of models and tools, emphasis was placed on Quantitative Structure-Property Relationship (QSPR) and Quantitative Structure-Activity Relationship (QSAR) models, and their potential role in predicting NM properties. In addition, the status of a diverse array of compartment-based mathematical models was assessed. These models comprised toxicokinetic (TK), toxicodynamic (TD), in vitro and in vivo dosimetry, and environmental fate models. Finally, based on systematic reviews of the scientific literature, as well as the outputs of the EU-funded research projects, recommendations for further research and development were also made. The Nanocomput project was carried out by the European Commission’s Joint Research Centre (JRC) for the Directorate-General (DG) for Internal Market, Industry, Entrepreneurship and SMEs (DG GROW) under the terms of an Administrative Arrangement between JRC and DG GROW. The project lasted 39 months, from January 2014 to March 2017, and was supported by a steering group with representatives from DG GROW, DG Environment and the European Chemicals Agency (ECHA).JRC.F.3-Chemicals Safety and Alternative Method

Functionally Relevant Macromolecular Interactions of Disordered Proteins

Disordered proteins are relatively recent newcomers in protein science. They were first described in detail by Wright and Dyson, in their J. Mol. Biol. paper in 1999. First, it was generally thought for more than a decade that disordered proteins or disordered parts of proteins have different amino acid compositions than folded proteins, and various prediction methods were developed based on this principle. These methods were suitable for distinguishing between the disordered (unstructured) and structured proteins known at that time. In addition, they could predict the site where a folded protein binds to the disordered part of a protein, shaping the latter into a well-defined 3D structure. Recently, however, evidence has emerged for a new type of disordered protein family whose members can undergo coupled folding and binding without the involvement of any folded proteins. Instead, they interact with each other, stabilizing their structure via “mutual synergistic folding” and, surprisingly, they exhibit the same residue composition as the folded protein. Increasingly more examples have been found where disordered proteins interact with non-protein macromolecules, adding to the already large variety of protein–protein interactions. There is also a very new phenomenon when proteins are involved in phase separation, which can represent a weak but functionally important macromolecular interaction. These phenomena are presented and discussed in the chapters of this book

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Biophysical and Biochemical Screening Approaches for Antimicrobial Drug Discovery Targeting S. aureus ClpP

The discovery of antibacterial drugs has been among most significant achievements of mankind in saving millions of lives across the planet from infectious diseases. With rise in resistance to almost all existing chemotypes, the design of next generation novel antibiotics has become much more challenging and difficult. The early 21st century witnessed the advancement of multiple novel chemotypes during golden age of antibiotics however the pace of antibiotic drug discovery has slowed down tremendously, contributing to life threatening antimicrobial discovery void since 1980’s. Therefore the need to develop novel antibiotics with unique mechanism of action to leverage against multi drug resistance pathogens, is paramount. In this direction the Caseinolytic Protease P (ClpP) is an emerging drug discovery target with significant potential for treatment of recalcitrant biofilm forming infections from pathogens such as Methicillin-resistant Staphylococcus aureus (MRSA) This dissertation highlights the ongoing efforts to facilitate the discovery of novel non peptidic ClpP activator compounds and improvement of pharmacological profile of existing ClpP targeting Acyldepsipeptides (ADEPs) series antibiotics. The chapter one discusses the history and synopsis of conventional antibiotics drug discovery screening approaches, and transitions to modern era structure or fragment based screening approaches. The merits and challenges of such approaches of targeting a well conserved bacterial protease (ClpP) are discussed along with dissertation aims toward development of biophysical and biochemical screening approaches. Chapter two discusses optimization of thermal shift assay as primary screening assay for ClpP and its utility toward screening of fragment collections and buffer conditions. Chapter three discussed the development of a site specific Fluorescence Polarization based FP probe based on ADEP scaffold and its utility as a robust high throughput capable primary screening assay for screening of diverse collections ranging from bioactives to fragments. Chapter four discusses development of a label free Surface Plasmon Resonance (SPR) based assay geared toward screening of fragment as well as in house small and large (ADEP analogs) series compounds in addition to determining full kinetics for lead prioritization. Chapter five discusses the results of multiple screening campaigns utilizing combination of above assays to generate multiple hits with superior ligand efficiency and chemical tractability. Chapter six concludes with analysis of the best of compounds among individual series or from screening campaigns and highlights effectiveness of above screening assays toward hit exploration along with outlook on anticipated challenges and future directions

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

Palindromic ligands targeting C-reactive protein and transthyretin

[No abstract