Application of waste iron in wet flue gas desulfurization (WFGD) wastewater treatment.

The wet flue gas desulfurization (WFGD) procedure results in wastewater containing a complex mixture of pollutants, including heavy metals and organic compounds, which are hardly degradable and pose significant environmental challenges. Addressing this issue, the proposed approach, incorporating waste iron shavings as a heterocatalyst within a modified Fenton process, represents a sustainable and effective solution for contaminants degrading in WFGD wastewater. Furthermore, this study aligns with the Best Available Techniques (BAT) regulations by meeting the requirement for compound oxidation-replacing the chlorine utilization with the generation of highly reactive radicals-and coagulation, which completes the treatment process.

The Role of Water Networks in Phosphodiesterase Inhibitor Dissociation and Kinetic Selectivity.

In search of new opportunities to develop Trypanosoma brucei phosphodiesterase B1 (TbrPDEB1) inhibitors that have selectivity over the off-target human PDE4 (hPDE4), different stages of a fragment-growing campaign were studied using a variety of biochemical, structural, thermodynamic, and kinetic binding assays. Remarkable differences in binding kinetics were identified and this kinetic selectivity was explored with computational methods, including molecular dynamics and interaction fingerprint analyses. These studies indicate that a key hydrogen bond between Gln and the inhibitors is exposed to a water channel in TbrPDEB1, leading to fast unbinding.

ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery.

In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance between capabilities, flexibility, reliability, and efficiency remains challenging due to the complexity of advanced RL algorithms and the significant reliance on specialized code. In this work, we introduce ACEGEN, a comprehensive and streamlined toolkit tailored for generative drug design, built using TorchRL, a modern RL library that offers thoroughly tested reusable components.

Top-Down Machine Learning of Coarse-Grained Protein Force Fields.

Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended time scales. Our methodology involves simulating proteins with molecular dynamics and utilizing the resulting trajectories to train a neural network potential through differentiable trajectory reweighting. Remarkably, this method requires only the native conformation of proteins, eliminating the need for labeled data derived from extensive simulations or memory-intensive end-to-end differentiable simulations.

Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series.

The accurate prediction of protein-ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool for this purpose. However, the accuracy and reliability of these methods can vary depending on the methodology.

Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series.

The accurate prediction of protein-ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool for this purpose. However, the accuracy and reliability of these methods can vary depending on the methodology.

Traumatic dental injuries - practical management guide.

Traumatic dental injuries (TDIs) with a prevalence of 25% among school children and 33% among adults are a public health problem and can have a negative influence on the quality of life. The treatment prognosis of some teeth injuries is heavily dependent on the actions taken at the place of injury. The objective was to summarize evidence-based knowledge on the topic of TDIs and present a practical management guide for first aid in an accessible way.

PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks.

Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented K, a convolutional neural network that predicted the binding affinity of a given protein-ligand complex while reaching state-of-the-art performance. However, it was unclear what this model was learning.

Worm Gear Drives with Improved Kinematic Accuracy.

This paper presents the fundamentals of the design and applications of new worm gear drive solutions, which enable the minimisation of backlash and are characterised by higher kinematic accuracy. Different types of worm surfaces are briefly outlined. Technological problems concerning the principles of achieving a high degree of precision in machining are also described.

Fragment-to-lead tailored in silico design.

Fragment-based drug discovery (FBDD) emerged as a disruptive technology and became established during the last two decades. Its rationality and low entry costs make it appealing, and the numerous examples of approved drugs discovered through FBDD validate the approach. However, FBDD still faces numerous challenges.

Simulation and Optimization Studies of the LHCb Beetle Readout ASIC and Machine Learning Approach for Pulse Shape Reconstruction.

The optimization of the Beetle readout ASIC and the performance of the software for the signal processing based on machine learning methods are presented. The Beetle readout chip was developed for the LHCb (Large Hadron Collider beauty) tracking detectors and was used in the VELO (Vertex Locator) during Run 1 and 2 of LHC data taking. The VELO, surrounding the LHC beam crossing region, was a leading part of the LHCb tracking system.

Nucleotide-decorated AuNPs as probes for nucleotide-binding proteins.

Gold nanoparticles (AuNPs) decorated with biologically relevant molecules have variety of applications in optical sensing of bioanalytes. Coating AuNPs with small nucleotides produces particles with high stability in water, but functionality-compatible strategies are needed to uncover the full potential of this type of conjugates. Here, we demonstrate that lipoic acid-modified dinucleotides can be used to modify AuNPs surfaces in a controllable manner to produce conjugates that are stable in aqueous buffers and biological mixtures and capable of interacting with nucleotide-binding proteins.

TorchMD: A Deep Learning Framework for Molecular Simulations.

Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials.

Coarse graining molecular dynamics with graph neural networks.

Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at an atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it are consistent with the conclusions we would draw from a model at a finer level of detail. It has been proved that a force matching scheme defines a thermodynamically consistent coarse-grained model for an atomistic system in the variational limit.

SkeleDock: A Web Application for Scaffold Docking in PlayMolecule.

SkeleDock is a scaffold docking algorithm which uses the structure of a protein-ligand complex as a template to model the binding mode of a chemically similar system. This algorithm was evaluated in the D3R Grand Challenge 4 pose prediction challenge, where it achieved competitive performance. Furthermore, we show that if crystallized fragments of the target ligand are available then SkeleDock can outperform rDock docking software at predicting the binding mode.

Structural Stability Predicts the Binding Mode of Protein-Ligand Complexes.

The prediction of a ligand's binding mode into its macromolecular target is essential in structure-based drug discovery. Even though tremendous effort has been made to address this problem, most of the developed tools work similarly, trying to predict the binding free energy associated with each particular binding mode. In this study, we decided to abandon this criterion, following structural stability instead.

Dynamic Undocking: A Novel Method for Structure-Based Drug Discovery.

Computer-aided methods have been broadly used in pharmaceutical research to identify potential ligands and design effective therapeutics. Most of the approaches rely on the binding affinity prediction and approximate thermodynamic properties of the system. Our alternative approach focuses on structural stability, provided by native protein-ligand interactions, in particular hydrogen bonds.

[Consumption of psychoactive substances--awareness of health consequences among the students of university schools in Poznan].

The goal of this paper is to estimate the phenomenon of consuming psychoactive substances such as: alcohol, nicotine, caffeine and narcotics among the students of Poznan's universities, and evaluating the level of consciousness of the dangers resulting from using those substances. The authors wanted to check, whether the consumption of psychoactive substances depends on such traits as: sex, place of living, subjective evaluation of one's health, the type of university they attend, and whether the respondents think that the knowledge passed onto them on the universities about the dangers resulting from consuming such substances is sufficient, and whether they know how to help an addicted person. The research, done with the use of a survey, was conducted among 504 students from six universities in Poznan: Medical University (16.