Quantitative structure−activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software "QSAR-Co" (available to download at https://sites. google.com/view/qsar-co) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This userfriendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments.

Because of the increasing demand of greener solvents, deep eutectic solvents (DES) have just emerged as low-cost alternative solvents for a broad range of applications. However, recent toxicity assay studies showed a non-negligible toxic behavior for these solvents and their components. Alternative in silico-based approaches such as the one proposed here, multitasking-Quantitative Structure Toxicity Relationships (mtk-QSTR), are increasingly used for risk assessment of chemicals to speed up policy decisions. This work reports a mtk-QSTR modeling of 572 DES and their components under multiple experimental conditions. To set up a reliable model from such data, we examined here the use of 0D–2D descriptors along with classification analysis, and the Box–Jenkins approach. This procedure led to a final mtk-QSTR model with high overall accuracy and predictivity (ca. 90%). The model highlights also the crucial role that polarizability, electronegativity, hydrogen-bond donor (HBD), and topological properties play into the DES toxicity. Furthermore, with the help of the derived mtk-QSTR model, 30 different HBD components were ranked on the basis of their toxic contributions to DES. More importantly, the proposed in silico modeling approach is shown to be a valuable tool to mine relevant STR information, therefore guiding the rational design of potentially safe DES.

The present work aims at establishing multi-target chemometric models using the recently launched quantitative structure–activity relationship (QSAR)-Co tool for predicting the activity of inhibitor compounds against different isoforms of phosphoinositide 3-kinase (PI3K) under various experimental conditions. The inhibitors of class I phosphoinositide 3-kinase (PI3K) isoforms have emerged as potential therapeutic agents for the treatment of various disorders, especially cancer. The cell-based enzyme inhibition assay results of PI3K inhibitors were curated from the CHEMBL database. Factors such as the nature and mutation of cell lines that may significantly alter the assay outcomes were considered as important experimental elements for mt-QSAR model development. The models, in turn, were developed using two machine learning techniques as implemented in QSAR-Co: linear discriminant analysis (LDA) and random forest (RF). Both techniques led to models with high accuracy (ca. 90%). Several molecular fragments were extracted from the current dataset, and their quantitative contributions to the inhibitory activity against all the proteins and experimental conditions under study were calculated. This case study also demonstrates the utility of QSAR-Co tool in solving multi-factorial and complex chemometric problems. Additionally, the combination of different in silico methods employed in this work can serve as a valuable guideline to speed up early discovery of PI3K inhibitors.

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.

Recent analyses have highlighted the promotion of cancer migration and invasion, mediated through HDAC via MMP-2 and MMP-9. Since both class 1 HDACs and MMP-2/9 are involved in the migration and invasion of cancer, an attempt has been taken to design dual MMP-2/HDAC-8 inhibitors by pharmacophore mapping and molecular docking approaches. The designed molecules were synthesized and showed a range of inhibitory activity against different MMP subtypes. Most of these designed compounds were selective towards MMP-2 but less potent against anti-targets like MMP-8, -12, etc. The highly active MMP-2 inhibitors were also found to be active towards HDAC-8 but less potent against other class 1 HDACs (HDAC-1 and -2). Molecular dynamics simulations revealed that the designed compounds may be acting through a distinct mechanism of action in the 'acetate ion channel' of HDAC-8. Some potent dual MMP-2/HDAC-8 inhibitors were further explored for in vitro cellular assays against human lung carcinoma cell line A549. These analyses revealed that some of these dual inhibitors have considerable anti-migratory and anti-invasive properties. The work may help to obtain some useful dual inhibitors.

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All modelling techniques were compared to each other. The current study may help in designing novel MMP-2 inhibitors.

Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure–activity relationship (mt-QSAR) models based on the Box–Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.