Understanding the role of land use for urban stormwater management in coastal waterways.

A holistic understanding of the quality and quantity of stormwater in the context of catchment land use plays a crucial role in stormwater management. This study investigated the quality and quantity of stormwater from forested, residential, industrial, and mixed land use areas. Water samples were collected from seven sites over two years at different stages of the runoff hydrograph using fixed sampling stations. Analysis of physicochemical and hydrological variables showed different patterns across the four land use types at various flow conditions highlighting the complex nature of stormwater quality influenced by catchment and rainfall characteristics. Mean concentrations of dissolved organic and oxidised nitrogen (DON and NOx-N) and dissolved organic and filterable reactive phosphorus (DOP and FRP) in stormwater from industrial, mixed-use and residential catchment types were statistically different from stormwater originating from a forested catchment. On average, residential, mixed-use and industrial catchments transported over 50 times more NOx-N to the receiving waters compared to forested catchments. Under high flow conditions, total phosphorus, FRP and total suspended solids (TSS) were mobilised, indicating that phosphorous export is directly related to sediment export regardless of the land use. The study outcomes contribute to the formulation of more effective stormwater management strategies to deal with the drivers of nutrients and TSS inputs resulting from modified land use types to minimise the urbanisation impacts on aquatic biota. In particular, the elevated dissolved nitrogen fractions from all the catchment types other than the forested catchment is a concern for receiving waters, as these can potentially impair water quality and impact the ecosystem health of downstream water bodies such as Intermittently Closed and Open Lakes or Lagoons (ICOLL). The stochastic nature of hydrology and corresponding nutrient loads should be prioritised in stormwater management action plans. However, as space limitations hinder the expansion of vegetation cover and retrofitting stormwater management devices, a paradigm shift in stormwater management is required to achieve the desired outcomes. The study outcomes further indicate that a one-size-fits-all approach to stormwater management may not deliver the desired outcomes, and a suite of tailor-made approaches targeting various flow conditions and catchment surface types is needed.

Novel D2/5-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 (D2Rs) are the main target in the treatment of schizophrenia, in particular for mitigation of the positive symptoms. Due to the high expression of 5-hydroxytryptamine type 3 receptors (5-HT3Rs) in human brain areas responsible for emotional behavior, motivation, and cognitive function, 5-HT3Rs represent a potential target for modulating the cognitive and negative symptoms of schizophrenia. Here we present the design, synthesis, and both in vitro and in vivo biological evaluation of 1,4-disubstituted aromatic piperazines. Screening of in vitro properties revealed the two most promising drug candidates (21 and 24) which were found to be potent D2Rs and moderate 5-HT3R antagonists, and which were forwarded to in vivo studies in Wistar rats. Considering toxicity, administration of the maximal feasible dose of 21 (2 mg/kg) did not produce any side effects. By contrast, the higher solubility of 24 led to revelation of mild and temporary side effects at the dose of 20 mg/kg. Importantly, both 21 and 24 showed facile crossing of the blood-brain barrier, even exerting higher levels in the brain in comparison to plasma. In a behavioral study using the acute amphetamine model of psychosis, we showed that compound 24 ameliorated both positive and negative effects of amphetamine including hyperlocomotion, social impairments, and disruption of prepulse inhibition. The effect of the highest dose (10 mg/kg) was comparable to the effect of the reference dose of aripiprazole (1 mg/kg).

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. To predict the immunogenicity risk, the human-likeness of sequences can be evaluated using existing humanness scores, but these lack diversity, granularity or interpretability. Meanwhile, immune repertoire sequencing has generated rich antibody libraries such as the Observed Antibody Space (OAS) that offer augmented diversity not yet exploited for antibody engineering. Here we present BioPhi, an open-source platform featuring novel methods for humanization (Sapiens) and humanness evaluation (OASis). Sapiens is a deep learning humanization method trained on the OAS using language modeling. Based on an in silico humanization benchmark of 177 antibodies, Sapiens produced sequences at scale while achieving results comparable to that of human experts. OASis is a granular, interpretable and diverse humanness score based on 9-mer peptide search in the OAS. OASis separated human and non-human sequences with high accuracy, and correlated with clinical immunogenicity. BioPhi thus offers an antibody design interface with automated methods that capture the richness of natural antibody repertoires to produce therapeutics with desired properties and accelerate antibody discovery campaigns. The BioPhi platform is accessible at https://biophi.dichlab.org and https://github.com/Merck/BioPhi.

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.c.) administration of DCK (5, 10, and 30 mg·kg-1 ) and its enantiomers S-DCK (10 mg·kg-1 ) and R-DCK (10 mg·kg-1 ). Additionally, activity at human N-methyl-d-aspartate (NMDA) receptors was also evaluated. KEY RESULTS: DCK rapidly crossed the blood brain barrier, with maximum brain levels achieved at 30 min and remaining high at 2 h after administration. Its antagonist activity at NMDA receptors is comparable to that of ketamine with S-DCK being more potent. DCK had stimulatory effects on locomotion, induced place preference, and robustly disrupted PPI. Locomotor stimulant effects tended to disappear more quickly than disruptive effects on PPI. S-DCK had more pronounced stimulatory properties than its R-enantiomer. However, the potency in disrupting PPI was comparable in both enantiomers. CONCLUSION AND IMPLICATIONS: DCK showed similar behavioural and addictive profiles and pharmacodynamics to ketamine, with S-DCK being in general more active. It has a slightly slower pharmacokinetic profile than ketamine, which is consistent with its reported longer duration of action. These findings have implications and significance for understanding the risks associated with illicit use of DCK.

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. All transactions were documented from the initial drug deal solicitation to the final qualitative analysis of all received samples. From the selected vendors, a total of nine NPS samples was obtained, all of which were analyzed by NMR, HRMS, LC-UV, and two also by x-ray diffraction. According to our analyses, four of the five substances offered under already known NPS names contained a different NPS. The selected vendors therefore either did not know about their product, or deliberately deceived the buyers. Furthermore, two of three obtained samples of purportedly novel NPS were identified as already documented substances sold under a different name. However, the third characterized substance sold as "MPF-47700" was a novel, yet uncharacterized, NPS. Finally, we received a single undeclared substance, later identified as 5F-ADB. In addition to chemical analysis of the nine obtained NPS samples, the methodology used also yielded contextual information about the accessibility of NPS on the dark web, the associated purchase process, and the modus operandi of three NPS vendors. Direct participation in dark web marketplaces seems to provide additional layers of information useful for forensic studies.

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. Prolonged swimming speed performance was assessed for 15 individuals from each of three river and two reservoir populations of R. semoni using the critical swimming speed test (Ucrit ). Body shape was characterized using geometric morphometrics, which was combined with fin aspect ratios and standard length to assess morphological divergence among the five populations. Best subsets model-selection was used to identify the morphological traits that best explain Ucrit variation among individuals. Our results indicate R. semoni from river populations had significantly higher prolonged swimming speed performance (Ucrit = 46.61 ± 0.98 cm s-1 ) than reservoir conspecifics (Ucrit = 35.57 ± 0.83 cm s-1 ; F1,74 = 58.624, Z = 35.938, P < 0.001). Similarly, R. semoni sampled from river populations had significantly higher fin aspect ratios (ARcaudal = 1.71 ± 0.04 and 1.29 ± 0.02 respectively; F(1,74) = 56.247, Z = 40.107, P < 0.001; ARpectoral = 1.85 ± 0.03 and 1.33 ± 0.02 respectively; F(1,74) = 7.156, Z = 4.055, P < 0.01). Best-subset analyses revealed Ucrit was most strongly correlated with pectoral and caudal fin aspect ratios (R2 adj = 0.973, AICc = 41.54). Body shape, however, was subject to a three-way interaction among population, habitat and sex effects (F3,74 = 1.038. Z = 1.982; P < 0.05). Thus sexual dimorphism formed a significant component of unique and complex variation in body shape among populations from different habitat types. This study revealed profound effects of human-altered flow environments on locomotor morphology and its functional link to changes in swimming performance of a common freshwater fish. While past studies have indicated body shape may be an important axis for divergence between lotic and lentic populations of several freshwater fishes, fin aspect ratios were the most important predictor of swimming speed in our study. Differences in body morphology here were inconsistent between river and reservoir populations, suggesting this aspect of phenotype may be more strongly influenced by other factors such as predation and sexual dimorphism.

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. Infrared (IR) and Raman spectroscopies are commonly used for such detection, but they have limitations; for example, fluorescence in Raman can overlay the signal and when the sample is a mixture sometimes neither Raman nor IR is able to identify the compounds. Here, we investigate the potential of X-ray powder diffraction (XRPD) to analyse samples seized on the black market. A series of psychoactive substances (heroin, cocaine, mephedrone, ephylone, butylone, JWH-073, and naphyrone) was measured. Comparison of their diffraction patterns with those of the respective standards showed that XRPD was able to identify each of the substances. The same samples were analyzed using IR and Raman, which in both cases were not able to detect the compounds in all of the samples. These results suggest that XRPD could be a valuable addition to the range of forensic tools used to detect these compounds in illicit drug samples.

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. AVAILABILITY AND IMPLEMENTATION: The NERDD web server is available at https://nerdd.zbh.uni-hamburg.de. Most tools are also available as software packages for local installation.

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. SYBA was compared with a random forest, that was utilized as a baseline method, as well as with other two methods for synthetic accessibility assessment: SAScore and SCScore. When used with their suggested thresholds, SYBA improves over random forest classification, albeit marginally, and outperforms SAScore and SCScore. However, upon the optimization of SAScore threshold (that changes from 6.0 to - 4.5), SAScore yields similar results as SYBA. Because SYBA is based merely on fragment contributions, it can be used for the analysis of the contribution of individual molecular parts to compound synthetic accessibility. SYBA is publicly available at https://github.com/lich-uct/syba under the GNU General Public License.

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 Ki, Kd, IC50 and EC50 data from ChEMBL database. QAFFP thus represent a method to encode and relate compounds on the basis of their similarity in bioactivity space. To benchmark the predictive power of QAFFP we assembled IC50 data from ChEMBL database for 18 diverse cancer cell lines widely used in preclinical drug discovery, and 25 diverse protein target data sets. This study complements part 1 where the performance of QAFFP in similarity searching, scaffold hopping, and bioactivity classification is evaluated. Despite being inherently noisy, we show that using QAFFP as descriptors leads to errors in prediction on the test set in the ~ 0.65-0.95 pIC50 units range, which are comparable to the estimated uncertainty of bioactivity data in ChEMBL (0.76-1.00 pIC50 units). We find that the predictive power of QAFFP is slightly worse than that of Morgan2 fingerprints and 1D and 2D physicochemical descriptors, with an effect size in the 0.02-0.08 pIC50 units range. Including QSAR models with low predictive power in the generation of QAFFP does not lead to improved predictive power. Given that the QSAR models we used to compute the QAFFP were selected on the basis of data availability alone, we anticipate better modeling results for QAFFP generated using more diverse and biologically meaningful targets. Data sets and Python code are publicly available at https://github.com/isidroc/QAFFP_regression .

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. J. Med. Chem. 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. In tests with holdout data, FAME 3 models reached competitive performance, with Matthews correlation coefficients (MCCs) ranging from 0.50 for a global model covering phase 1 and phase 2 metabolism, to 0.75 for a focused model for phase 2 metabolism. A model focused on cytochrome P450 metabolism yielded an MCC of 0.57. Results from case studies with several synthetic compounds, natural products, and natural product derivatives demonstrate the agreement between model predictions and literature data even for molecules with structural patterns clearly distinct from those present in the training data. The applicability domains of the individual models were estimated by a new, atom-based distance measure (FAMEscore) that is based on a nearest-neighbor search in the space of atom environments. FAME 3 is available via a public web service at https://nerdd.zbh.uni-hamburg.de/ and as a self-contained Java software package, free for academic and noncommercial research.

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. To this end, we have developed a new tool, GLORY, which combines SoM prediction with FAME 2 and a new collection of rules for metabolic reactions mediated by the cytochrome P450 enzyme family. GLORY has two modes: MaxEfficiency and MaxCoverage. For MaxEfficiency mode, the use of predicted SoMs to restrict the locations in the molecule at which the reaction rules could be applied was explored. For MaxCoverage mode, the predicted SoM probabilities were instead used to develop a new scoring approach for the predicted metabolites. With this scoring approach, GLORY achieves a recall of 0.83 and can predict at least one known metabolite within the top three ranked positions for 76% of the molecules of a new, manually curated test set. GLORY is freely available as a web server at https://acm.zbh.uni-hamburg.de/glory/, and the datasets and reaction rules are provided in the Supplementary Material.

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. The NtC classification was used to define a validation score called confal, which quantifies the conformity between an analyzed structure and the geometries of NtC. NtC and CANA assignment were applied to analyze the structural properties of typical DNA structures such as Dickerson-Drew dodecamers, guanine quadruplexes and structural models based on fibre diffraction. NtC, CANA and confal assignment, which is accessible at the website https://dnatco.org, allows the quantitative assessment and validation of DNA structures and their subsequent analysis by means of pseudo-sequence alignment. An animated Interactive 3D Complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:Acta_Cryst_D:2.

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. In the present paper, we introduce Nonpher, a computational method for the construction of a hard-to-synthesize virtual library. Nonpher is based on a molecular morphing algorithm in which new structures are iteratively generated by simple structural changes, such as the addition or removal of an atom or a bond. In Nonpher, molecular morphing was optimized so that it yields structures not overly complex, but just right hard-to-synthesize. Nonpher results were compared with SAscore and dense region (DR), other two methods for the generation of hard-to-synthesize compounds. Random forest classifier trained on Nonpher data achieves better results than models obtained using SAscore and DR data.

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. Important is the specific role of two DNA structural forms, A-DNA, and BII-DNA, represented by the CANA letters AAA and BB2: AAA structures are avoided in non-specific NCP complexes, where the wrapping of the DNA duplex is explained by the periodic occurrence of BB2 every 10.3 steps. In both regulatory and NCP complexes, the extent of bending of the DNA local helical axis does not influence proportional representation of the CANA alphabet letters, namely the relative incidences of AAA and BB2 remain constant in bent and straight duplexes.

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

We report on the further development of FAst MEtabolizer (FAME; J. Chem. Inf. MODEL: 2013, 53, 2896-2907), a collection of random forest models for the prediction of sites of metabolism (SoMs) of xenobiotics. A broad set of descriptors was explored, from simple 2D descriptors such as those used in FAME, to quantum chemical descriptors employed in some of the most accurate models for SoM prediction currently available. In line with the original FAME approach, our objective was to keep things simple and to come up with accurate and robust models that are based on a small number of 2D descriptors. We found that circular descriptions of atoms and their environments with such descriptors in combination with an extremely randomized trees algorithm can yield models that perform equally well compared to more complex approaches. Thorough evaluation experiments on an independent test set showed that the best of these models obtained a Matthews correlation coefficient, area under the receiver operating characteristic curve, and Top-2 accuracy of 0.57, 0.91 and 94.1%, respectively. Models for the prediction of isoform-specific regioselectivity of CYP 3A4, 2D6, and 2C9 were also developed and showed competitive performance. The best models have been integrated into a newly developed software package (FAME 2), which is available free of charge from the authors.

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 combines SETTER's decomposition of RNA structures into non-overlapping structural subunits with the multiple sequence alignment algorithm ClustalW adapted for the structure alignment. The accuracy of MultiSETTER was assessed by the automatic classification of RNA structures and its comparison to SCOR annotations. In addition, MultiSETTER classification was also compared to multiple sequence alignment-based and secondary structure alignment-based classifications provided by LocARNA and RNADistance tools, respectively. MultiSETTER precompiled Windows libraries, as well as the C++ source code, are freely available from http://siret.cz/multisetter.

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. However, though biological relationships can be inferred by a pairwise structure alignment, key features preserved by evolution can be identified only from a multiple structure alignment. Thus, we extended the SETTER algorithm to the alignment of multiple RNA structures and developed the MultiSETTER algorithm. RESULTS: In this paper, we present the updated version of the SETTER web server that implements a user friendly interface to the MultiSETTER algorithm. The server accepts RNA structures either as the list of PDB IDs or as user-defined PDB files. After the superposition is computed, structures are visualized in 3D and several reports and statistics are generated. CONCLUSION: To the best of our knowledge, the MultiSETTER web server is the first publicly available tool for a multiple RNA structure alignment. The MultiSETTER server offers the visual inspection of an alignment in 3D space which may reveal structural and functional relationships not captured by other multiple alignment methods based either on a sequence or on secondary structure motifs.

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. In the cluster heatmap, the data matrix is displayed as a heatmap, a 2-dimensional array in which the colour of each element corresponds to its value. The rows/columns of the matrix are ordered such that similar rows/columns are near each other. The ordering is given by the dendrogram which is displayed on the side of the heatmap. RESULTS: We developed InCHlib (Interactive Cluster Heatmap Library), a highly interactive and lightweight JavaScript library for cluster heatmap visualization and exploration. InCHlib enables the user to select individual or clustered heatmap rows, to zoom in and out of clusters or to flexibly modify heatmap appearance. The cluster heatmap can be augmented with additional metadata displayed in a different colour scale. In addition, to further enhance the visualization, the cluster heatmap can be interconnected with external data sources or analysis tools. Data clustering and the preparation of the input file for InCHlib is facilitated by the Python utility script inchlib_clust. CONCLUSIONS: The cluster heatmap is one of the most popular visualizations of large chemical and biomedical data sets originating, e.g., in high-throughput screening, genomics or transcriptomics experiments. The presented JavaScript library InCHlib is a client-side solution for cluster heatmap exploration. InCHlib can be easily deployed into any modern web application and configured to cooperate with external tools and data sources. Though InCHlib is primarily intended for the analysis of chemical or biological data, it is a versatile tool which application domain is not limited to the life sciences only.