Typical in silico models for ecotoxicology focus on a few endpoints, but there is a need to increase the diversity of these models. This study proposes models using the NOEC for the harlequin fly (Chironomus riparius) and EC50 for swollen duckweed (Lemna gibba) for the first time. The data were derived from the EFSA OpenFoodTox database. The models were based on the correlation weights of molecular features used to calculate the 2D descriptor in CORAL software. The Monte Carlo method was used to calculate the correlation weights of the algorithms. The determination coefficients of the best models for the external validation set were 0.74 (NOAEC) and 0.85 (EC50).

Previously, we found that 5-(2,6-dimethoxybenzoylamino)-3-phenylisoxazoles (IOXs) inhibit chitin synthesis in the cultured integument of Chilo suppressalis. In this study, IOXs with various substituents at the para-position of the 3-phenyl ring were synthesized, and the concentrations required to inhibit chitin synthesis to 50% (IC50) were determined for all compounds. The introduction of halogens-such as F, Cl, and Br-and small alkyls-such as Me, Et, Pr, and n-Bu-at the 3-phenyl ring slightly enhanced the activity. However, the activity decreased drastically with the introduction of NO2, CF3, and t-Bu. The quantitative analysis of the substituent effect at the 3-phenyl ring on chitin-synthesis inhibition using the Hansch-Fujita method showed that the hydrophobic substituent with the optimum value was favored for the activity, but the bulky substituent in terms of E s was detrimental to the activity.

In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula \"w_new\", consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology.Scientific contributionWe presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced \'w_new\', a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains.

Quorum sensing inhibitors (QSIs), as a kind of ideal antibiotic substitutes, have been recommended to be used in combination with traditional antibiotics in medical and aquaculture fields. Due to the co-existence of QSIs and antibiotics in environmental media, it is necessary to evaluate their joint risk. However, there is little information about the acute toxicity of mixtures for QSIs and antibiotics. In this study, 10 QSIs and 3 sulfonamides (SAs, as the representatives for traditional antibiotics) were selected as the test chemicals, and their acute toxic effects were determined using the bioluminescence of Aliivibrio fischeri (A. fischeri) as the endpoint. The results indicated that SAs and QSIs all induced S-shaped dose-responses in A. fischeri bioluminescence. Furthermore, SAs possessed greater acute toxicity than QSIs, and luciferase (Luc) might be the target protein of test chemicals. Based on the median effective concentration (EC50) for each test chemical, QSI-SA mixtures were designed according to equitoxic (EC50(QSI):EC50(SA) = 1:1) and non-equitoxic ratios (EC50(QSI):EC50(SA) = 1:10, 1:5, 1:0.2, and 1:0.1). It could be observed that with the increase of QSI proportion, the acute toxicity of QSI-SA mixtures enhanced while the corresponding TU values decreased. Furthermore, QSIs contributed more to the acute toxicity of test binary mixtures. The joint toxic actions of QSIs and SAs were synergism for 23 mixtures, antagonism for 12 mixtures, and addition for 1 mixture. Quantitative structure-activity relationship (QSAR) models for the acute toxicity QSIs, SAs, and their binary mixtures were then constructed based on the lowest CDOCKER interaction energy (Ebind-Luc) between Luc and each chemical and the component proportion in the mixture. These models exhibited good robustness and predictive ability in evaluating the toxicity data and joint toxic actions of QSIs and SAs. This study provides reference data and applicable QSAR models for the environmental risk assessment of QSIs, and gives a new perspective for exploring the joint effects of QSI-antibiotic mixtures.

A non-structural SARS-CoV-2 protein, PLpro, is involved in post-translational modifications in cells, allowing the evasion of antiviral immune response mechanisms. In this study, potential PLpro inhibitory drugs were designed using QSAR, molecular docking, and molecular dynamics. A combined QSAR equation with physicochemical and Free-Wilson descriptors was formulated. The r2, q2, and r2test values were 0.833, 0.770, and 0.721, respectively. From the equation, it was found that the presence of an aromatic ring and a basic nitrogen atom is crucial for obtaining good antiviral activity. Then, a series of structures for the binding sites of C111, Y268, and H73 of PLpro were created. The best compounds were found to exhibit pIC50 values of 9.124 and docking scoring values of -14 kcal/mol. The stability of the compounds in the cavities was confirmed by molecular dynamics studies. A high number of stable contacts and good interactions over time were exhibited by the aryl-thiophenes Pred14 and Pred15, making them potential antiviral candidates.

Metabolomics has gained much attention due to its potential to reveal molecular disease mechanisms and present viable biomarkers. This work uses a panel of untargeted serum metabolomes from 602 children from the COPSAC2010 mother-child cohort. The annotated part of the metabolome consists of 517 chemical compounds curated using automated procedures. We created a filtering method for the quantified metabolites using predicted quantitative structure-bioactivity relationships for the Tox21 database on nuclear receptors and stress response in cell lines. The metabolites measured in the children\'s serums are predicted to affect specific targeted models, known for their significance in inflammation, immune function, and health outcomes. The targets from Tox21 have been used as targets with quantitative structure-activity relationships (QSARs). They were trained for ~7000 structures, saved as models, and then applied to the annotated metabolites to predict their potential bioactivities. The models were selected based on strict accuracy criteria surpassing random effects. After application, 52 metabolites showed potential bioactivity based on structural similarity with known active compounds from the Tox21 set. The filtered compounds were subsequently used and weighted by their bioactive potential to show an association with early childhood hs-CRP levels at six months in a linear model supporting a physiological adverse effect on systemic low-grade inflammation.

Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of drug mechanism. The cysteine trapping assay is a method for detecting reactive metabolites that bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silico classification model for predicting a positive/negative outcome in the cysteine trapping assay. We collected 475 compounds (436 in-house compounds and 39 publicly available drugs) based on experimental data performed in this study, and the composition of the results showed 248 positives and 227 negatives. Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. In the time-split dataset, AUC-ROC of MPNN and RF were 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that the in silico MPNN classification model for the cysteine trapping assay has a better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous results.

The structure-activity analysis (SAR) and machine learning were used to investigate potential anti-S. aureus agents in a faster method. In this study, 24 oxygenated benzene ring components with S. aureus inhibition capacity were confirmed by literature exploring and in-house experiments, and the SAR analysis suggested that the hydroxyl group position may affect the anti-S. aureus activity. The 2D-MLR-QSAR model with 9 descriptors was further evaluated as the best model among the 21 models. After that, hesperetic acid and 2-HTPA were further explored and evaluated as the potential anti-S. aureus agents screening in the natural product clustering library through the best QSAR model calculation. The antibacterial capacities of hesperetic acid and 2-HTPA had been investigated and proved the similar predictive pMIC value resulting from the QSAR model. Besides, the two novel components were able to inhibit the growth of S. aureus by disrupting the cell membrane through the molecular dynamics simulation (MD), which further evidenced by scanning electron microscopy (SEM) test and PI dye results. Overall, these results are highly suggested that QSAR can be used to predict the antibacterial agents targeting S. aureus, which provides a new paradigm to research the molecular structure-antibacterial capacity relationship.

As the world grapples with the relentless challenges posed by diseases like malaria, the advent of sophisticated computational tools has emerged as a beacon of hope in the quest for effective treatments. In this study we delve into the strategies behind computational tools encompassing virtual screening, molecular docking, artificial intelligence (AI), and machine learning (ML). We assess their effectiveness and contribution to the progress of malaria treatment. The convergence of these computational strategies, coupled with the ever-increasing power of computing systems, has ushered in a new era of drug discovery, holding immense promise for the eradication of malaria. SCIENTIFIC CONTRIBUTION: Computational tools remain pivotal in drug design and development. They provide a platform for researchers to explore various treatment options and save both time and money in the drug development pipeline. It is imperative to assess computational techniques and monitor their effectiveness in disease control. In this study we examine renown computational tools that have been employed in the battle against malaria, the benefits and challenges these tools have presented, and the potential they hold in the future eradication of the disease.

Drug discovery is a challenging process, with many compounds failing to progress due to unmet pharmacokinetic criteria. Lipophilicity is an important physicochemical parameter that affects various pharmacokinetic processes, including absorption, metabolism, and excretion. This study evaluated the lipophilic properties of a library of ipsapirone derivatives that were previously synthesized to affect dopamine and serotonin receptors. Lipophilicity indices were determined using computational and chromatographic approaches. In addition, the affinity to human serum albumin (HSA) and phospholipids was assessed using biomimetic chromatography protocols. Quantitative Structure-Retention Relationship (QSRR) methodologies were used to determine the impact of theoretical descriptors on experimentally determined properties. A multiple linear regression (MLR) model was calculated to identify the most important features, and genetic algorithms (GAs) were used to assist in the selection of features. The resultant models showed commendable predictive accuracy, minimal error, and good concordance correlation coefficient values of 0.876, 0.149, and 0.930 for the validation group, respectively.