Similarity Analysis of Computer-Generated and Commercial Libraries for Targeted Biocompatible Coded Amino Acid Replacement.

Many non-natural amino acids can be incorporated by biological systems into coded functional peptides and proteins. For such incorporations to be effective, they must not only be compatible with the desired function but also evade various biochemical error-checking mechanisms. The underlying molecular mechanisms are complex, and this problem has been approached previously largely by expert perception of isomer compatibility, followed by empirical study.

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR).

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material's capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure-property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR).

Machine learning analysis of a large set of homopolymers to predict glass transition temperatures.

Glass transition temperature of polymers, Tg, is an important thermophysical property, which sometimes can be difficult to measure experimentally. In this regard, data-driven machine learning approaches are important alternatives to assess Tg values, in a high-throughput way. In this study, a large dataset of more than 900 polymers with reported glass transition temperature (T) was assembled from various public sources in order to develop a predictive model depicting the structure-property relationships.

NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study.

Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system.

Design and evaluation of nanoscale materials with programmed responsivity towards epigenetic enzymes.

Self-assembled materials capable of modulating their assembly properties in response to specific enzymes play a pivotal role in advancing 'intelligent' encapsulation platforms for biotechnological applications. Here, we introduce a previously unreported class of synthetic nanomaterials that programmatically interact with histone deacetylase (HDAC) as the triggering stimulus for disassembly. These nanomaterials consist of co-polypeptides comprising poly(acetyl L-lysine) and poly(ethylene glycol) blocks.

Phenolic Acid-β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR Study.

The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds.

From RNA sequence to its three-dimensional structure: geometrical structure, stability and dynamics of selected fragments of SARS-CoV-2 RNA.

In this computational study, we explore the folding of a particular sequence using various computational tools to produce two-dimensional structures, which are then transformed into three-dimensional structures. We then study the geometry, energetics and dynamics of these structures using full electron quantum-chemical and classical molecular dynamics calculations. Our study focuses on the SARS-CoV-2 RNA fragment GGaGGaGGuguugcaGG and its various structures, including a G-quadruplex and five different hairpins.

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling.

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model.

Design and Evaluation of Nanoscale Materials with Programmed Responsivity towards Epigenetic Enzymes.

Self-assembled materials capable of modulating their assembly properties in response to specific enzymes play a pivotal role in advancing 'intelligent' encapsulation platforms for biotechnological applications. Here, we introduce a previously unreported class of synthetic nanomaterials that programmatically interact with histone deacetylase (HDAC) as the triggering stimulus for disassembly. These nanomaterials consist of co-polypeptides comprising poly (acetyl L-lysine) and poly(ethylene glycol) blocks.

Quantitative Structure-Permittivity Relationship Study of a Series of Polymers.

Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure-property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model.

Relationships between the Photodegradation Reaction Rate and Structural Properties of Polymer Systems.

The development of reusable polymeric materials inspires an attempt to combine renewable biomass with upcycling to form a biorenewable closed system. It has been reported that 2,5-furandicarboxylic acid (FDCA) can be recovered for recycling when incorporated as monomers into photodegradable polymeric systems. Here, we develop a procedure to better understand the photodegradation reactions combining density functional theory (DFT) based time-dependent excited-state molecular dynamics (TDESMD) studies with machine learning-based quantitative structure-activity relationships (QSAR) methodology.

Combined Machine Learning, Computational, and Experimental Analysis of the Iridium(III) Complexes with Red to Near-Infrared Emission.

Various coordination complexes have been the subject of experimental and theoretical studies in recent decades because of their fascinating photophysical properties. In this work, a combined experimental and computational approach was applied to investigate the optical properties of monocationic Ir(III) complexes. An interpretative machine learning-based quantitative structure-property relationship (ML/QSPR) model was successfully developed that could reliably predict the emission wavelength of the Ir(III) complexes and provide a foundation for the theoretical evaluation of the optical properties of Ir(III) complexes.

Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives.

Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K, 1GOS, 1GS4, 1H82, 1OG5, 1UOM, 2F9Q, 2J0D, 3ERT) obtained from the Protein Data Bank (PDB) and showing high similarity to proteins from aquatic species.

Stability Constant and Potentiometric Sensitivity of Heavy Metal-Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands.

Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters.

Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease.

Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and -site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database.

Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches.

Ubiquitin-proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several researchers have applied cheminformatics and artificial intelligence methods to study the inhibition of proteasomes, including the prediction of UPP inhibitors.

Structural Features Promoting Photocatalytic Degradation of Contaminants of Emerging Concern: Insights into Degradation Mechanism Employing QSA/PR Modeling.

Although heterogeneous photocatalysis has shown promising results in degradation of contaminants of emerging concern (CECs), the mechanistic implications related to structural diversity of chemicals, affecting oxidative (by HO•) or reductive (by O•) degradation pathways are still scarce. In this study, the degradation extents and rates of selected organics in the absence and presence of common scavengers for reactive oxygen species (ROS) generated during photocatalytic treatment were determined. The obtained values were then brought into correlation as coefficients (MHO•/MO2•-), denoting the ratio of organics degraded by two occurring mechanisms: oxidation and reduction via HO• and O•.

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach.

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR).

The Hydrolysis Rate of Paraoxonase-1 Q and R Isoenzymes: An In Silico Study Based on In Vitro Data.

Human serum paraoxonase-1 (PON1) is an important hydrolase-type enzyme found in numerous tissues. Notably, it can exist in two isozyme-forms, Q and R, that exhibit different activities. This study presents an in silico (QSAR, Docking, MD and QM/MM) study of a set of compounds on the activity towards the PON1 isoenzymes (QPON1 and RPON1).

A Fuzzy System Classification Approach for QSAR Modeling of α- Amylase and α-Glucosidase Inhibitors.

Introduction: This report proposes the application of a new Machine Learning algorithm called Fuzzy Unordered Rules Induction Algorithm (FURIA)-C in the classification of druglike compounds with antidiabetic inhibitory ability toward the main two pharmacological targets: α-amylase and α-glucosidase.

Methods: The two obtained QSAR models were tested for classification capability, achieving satisfactory accuracy scores of 94.5% and 96.

Chemical constituents, anti-inflammatory and analgesic activities of iridoids preparation from bunge.

This study reports the isolation of iridoids and cycloartane glycosides from the aerial parts of Bunge. Six compounds were isolated and the chemical structures were identified as phlorigidoside С (), 8--acetylharpagide (), shanzhiside methyl ester (), cyclosiversioside A (), cyclosiversioside E (), and cyclosiversioside C (). Compounds 4-6 are reported for the first time in this plant.

Machine Learning Study of Metabolic Networks ChEMBL Data of Antibacterial Compounds.

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays >40 MNs of multiple bacteria species.

Molecular Dynamics Study of the Photodegradation of Polymeric Chains.

The development of reusable polymeric materials inspires an attempt to combine renewable biomass with upcycling to form a biorenewable closed system. It has been reported that 2,5-furandicarboxylic acid (FDCA) can be recovered for recycling when incorporated as monomers into photodegradable polymeric systems. Here, we conduct density functional theory (DFT) studies with periodic boundary conditions on microscopic structures involved in the photodegradation of polymeric chains incorporating FDCA and 2-nitro-1,3-benzenedimethanol.