Forgotten fish habitats: Developing a conceptual framework for evaluating intermittent waterways and flood channels as native fish habitats: Insights from two freshwater catchments in Australia.

The ability to correctly identify Potential Key Fish Habitats (PKFH) before undertaking development assessments or stream restoration projects is a critical step for determining appropriate management interventions and offset strategies to minimize damage to all life stages of fish. However, there are no rapid, low-cost tools that can be universally utilised to identify PKFH in perennial streams and ephemeral streams in particular. We propose a conceptual framework for developing a rapid field appraisal based on a range of physical fish habitat-supporting features.

Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library.

Computational exploration of chemical space is crucial in modern cheminformatics research for accelerating the discovery of new biologically active compounds. In this study, we present a detailed analysis of the chemical library of potential glucocorticoid receptor (GR) ligands generated by the molecular generator, Molpher. To generate the targeted GR library and construct the classification models, structures from the ChEMBL database as well as from the internal IMG library, which was experimentally screened for biological activity in the primary luciferase reporter cell assay, were utilized.

Novel D/5-HT receptor modulators related to cariprazine with potential implication to schizophrenia treatment.

Schizophrenia is a serious mental disorder without a fully understood pathomechanism, but which involves dysregulation of neurotransmitters and their receptors. The best option for the management of schizophrenia comprises so-called multi-target ligands, similar to the third generation of neuroleptics. Dopamine type 2 receptors (DRs) are the main target in the treatment of schizophrenia, in particular for mitigation of the positive symptoms.

BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning.

Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains one of the main routes for therapeutic antibody development. Traditionally, humanization is manual, laborious, and requires expert knowledge. Although automation efforts are advancing, existing methods are either demonstrated on a small scale or are entirely proprietary.

Pharmacokinetic, pharmacodynamic, and behavioural studies of deschloroketamine in Wistar rats.

Background And Purpose: Deschloroketamine (DCK), a structural analogue of ketamine, has recently emerged on the illicit drug market as a recreational drug with a modestly long duration of action. Despite it being widely used by recreational users, no systematic research on its effects has been performed to date.

Experimental Approach: Pharmacokinetics, acute effects, and addictive potential in a series of behavioural tests in Wistar rats were performed following subcutaneous (s.

QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction.

Affinity fingerprints report the activity of small molecules across a set of assays, and thus permit to gather information about the bioactivities of structurally dissimilar compounds, where models based on chemical structure alone are often limited, and model complex biological endpoints, such as human toxicity and in vitro cancer cell line sensitivity. Here, we propose to model in vitro compound activity using computationally predicted bioactivity profiles as compound descriptors. To this aim, we apply and validate a framework for the calculation of QSAR-derived affinity fingerprints (QAFFP) using a set of 1360 QSAR models generated using K, K, IC and EC data from ChEMBL database.

SYBA: Bayesian estimation of synthetic accessibility of organic compounds.

SYBA (SYnthetic Bayesian Accessibility) is a fragment-based method for the rapid classification of organic compounds as easy- (ES) or hard-to-synthesize (HS). It is based on a Bernoulli naïve Bayes classifier that is used to assign SYBA score contributions to individual fragments based on their frequencies in the database of ES and HS molecules. SYBA was trained on ES molecules available in the ZINC15 database and on HS molecules generated by the Nonpher methodology.

Morphological predictors of swimming speed performance in river and reservoir populations of Australian smelt Retropinna semoni.

Dam construction is a major driver of ecological change in freshwater ecosystems. Fish populations have been shown to diverge in response to different flow velocity habitats, yet adaptations of fish populations to river and reservoir habitats created by dams remains poorly understood. We aimed to evaluate divergence of morphological traits and prolonged swimming speed performance between lotic and lentic populations of Australian smelt Retropinna semoni and quantify the relationship between prolonged swimming speed performance and morphology.

New psychoactive substances on dark web markets: From deal solicitation to forensic analysis of purchased substances.

The dark web scene has been drawing the attention of law enforcement agencies and researchers alike. To date, most of the published works on the dark web are based on data gained by passive observation. To gain a more contextualized perspective, a study was conducted in which three vendors were selected on the "Dream Market" dark web marketplace, from whom subsequently several new psychoactive substances (NPS) were ordered.

Can X-Ray Powder Diffraction Be a Suitable Forensic Method for Illicit Drug Identification?

New psychoactive substances (NPSs) are associated with a significant number of intoxications. With the number of readily available forms of these drugs rising every year, there are even risks for the general public. Consequently, there is a high demand for methods sufficiently sensitive to detect NPSs in samples found at the crime scene.

NERDD: a web portal providing access to in silico tools for drug discovery.

Summary: The New E-Resource for Drug Discovery (NERDD) is a quickly expanding web portal focused on the provision of peer-reviewed in silico tools for drug discovery. NERDD currently hosts tools for predicting the sites of metabolism (FAME) and metabolites (GLORY) of small organic molecules, for flagging compounds that are likely to interfere with biological assays (Hit Dexter), and for identifying natural products and natural product derivatives in large compound collections (NP-Scout). Several additional models and components are currently in development.

FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes.

In this work we present the third generation of FAst MEtabolizer (FAME 3), a collection of extra trees classifiers for the prediction of sites of metabolism (SoMs) in small molecules such as drugs, druglike compounds, natural products, agrochemicals, and cosmetics. FAME 3 was derived from the MetaQSAR database ( Pedretti et al. 2018 , 61 , 1019 ), a recently published data resource on xenobiotic metabolism that contains more than 2100 substrates annotated with more than 6300 experimentally confirmed SoMs related to redox reactions, hydrolysis and other nonredox reactions, and conjugation reactions.

GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism.

Computational prediction of xenobiotic metabolism can provide valuable information to guide the development of drugs, cosmetics, agrochemicals, and other chemical entities. We have previously developed FAME 2, an effective tool for predicting sites of metabolism (SoMs). In this work, we focus on the prediction of the chemical structures of metabolites, in particular metabolites of xenobiotics.

A DNA structural alphabet provides new insight into DNA flexibility.

DNA is a structurally plastic molecule, and its biological function is enabled by adaptation to its binding partners. To identify the DNA structural polymorphisms that are possible in such adaptations, the dinucleotide structures of 60 000 DNA steps from sequentially nonredundant crystal structures were classified and an automated protocol assigning 44 distinct structural (conformational) classes called NtC (for Nucleotide Conformers) was developed. To further facilitate understanding of the DNA structure, the NtC were assembled into the DNA structural alphabet CANA (Conformational Alphabet of Nucleic Acids) and the projection of CANA onto the graphical representation of the molecular structure was proposed.

Nonpher: computational method for design of hard-to-synthesize structures.

In cheminformatics, machine learning methods are typically used to classify chemical compounds into distinctive classes such as active/nonactive or toxic/nontoxic. To train a classifier, a training data set must consist of examples from both positive and negative classes. While a biological activity or toxicity can be experimentally measured, another important molecular property, a synthetic feasibility, is a more abstract feature that can't be easily assessed.

A DNA Structural Alphabet Distinguishes Structural Features of DNA Bound to Regulatory Proteins and in the Nucleosome Core Particle.

We analyzed the structural behavior of DNA complexed with regulatory proteins and the nucleosome core particle (NCP). The three-dimensional structures of almost 25 thousand dinucleotide steps from more than 500 sequentially non-redundant crystal structures were classified by using DNA structural alphabet CANA (Conformational Alphabet of Nucleic Acids) and associations between ten CANA letters and sixteen dinucleotide sequences were investigated. The associations showed features discriminating between specific and non-specific binding of DNA to proteins.

FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity.

Unlabelled: We report on the further development of FAst MEtabolizer (FAME; J. Chem. Inf.

Multiple 3D RNA Structure Superposition Using Neighbor Joining.

Recent advances in RNA research and the steady growth of available RNA structures call for bioinformatics methods for handling and analyzing RNA structural data. Recently, we introduced SETTER-a fast and accurate method for RNA pairwise structure alignment. In this paper, we describe MultiSETTER, SETTER extension for multiple RNA structure alignment.

MultiSETTER: web server for multiple RNA structure comparison.

Background: Understanding the architecture and function of RNA molecules requires methods for comparing and analyzing their tertiary and quaternary structures. While structural superposition of short RNAs is achievable in a reasonable time, large structures represent much bigger challenge. Therefore, we have developed a fast and accurate algorithm for RNA pairwise structure superposition called SETTER and implemented it in the SETTER web server.

InCHlib - interactive cluster heatmap for web applications.

Background: Hierarchical clustering is an exploratory data analysis method that reveals the groups (clusters) of similar objects. The result of the hierarchical clustering is a tree structure called dendrogram that shows the arrangement of individual clusters. To investigate the row/column hierarchical cluster structure of a data matrix, a visualization tool called 'cluster heatmap' is commonly employed.

Molpher: a software framework for systematic chemical space exploration.

Background: Chemical space is virtual space occupied by all chemically meaningful organic compounds. It is an important concept in contemporary chemoinformatics research, and its systematic exploration is vital to the discovery of either novel drugs or new tools for chemical biology.

Results: In this paper, we describe Molpher, an open-source framework for the systematic exploration of chemical space.

Bioinformatic analysis of the protein/DNA interface.

To investigate the principles driving recognition between proteins and DNA, we analyzed more than thousand crystal structures of protein/DNA complexes. We classified protein and DNA conformations by structural alphabets, protein blocks [de Brevern, Etchebest and Hazout (2000) (Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks. Prots.

Automatic workflow for the classification of local DNA conformations.

Background: A growing number of crystal and NMR structures reveals a considerable structural polymorphism of DNA architecture going well beyond the usual image of a double helical molecule. DNA is highly variable with dinucleotide steps exhibiting a substantial flexibility in a sequence-dependent manner. An analysis of the conformational space of the DNA backbone and the enhancement of our understanding of the conformational dependencies in DNA are therefore important for full comprehension of DNA structural polymorphism.

Benchmark quantum-chemical calculations on a complete set of rotameric families of the DNA sugar-phosphate backbone and their comparison with modern density functional theory.

The DNA sugar-phosphate backbone has a substantial influence on the DNA structural dynamics. Structural biology and bioinformatics studies revealed that the DNA backbone in experimental structures samples a wide range of distinct conformational substates, known as rotameric DNA backbone conformational families. Their correct description is essential for methods used to model nucleic acids and is known to be the Achilles heel of force field computations.