Kunal Roy

Jadavpur University | JU · Department of Pharmaceutical Technology

A bibliometric analysis of the Cheminformatics/QSAR articles published in the present century (2000–2023) is presented based on a SCOPUS search made in October 2024 using a given set of search criteria. The obtained results of 52,415 documents against the specific query are analyzed based on the number of documents per year, contributions of differ...

A total of 16 organic sunscreens and over 160 products of their degradation in biotic and abiotic conditions were investigated in the context of their safety during pregnancy. Drug-likeness and the ability of the studied compounds to be absorbed from the gastrointestinal tract and cross the human placenta were predicted in silico using the SwissADM...

The concept of similarity is an important aspect in various in silico-based prediction approaches. Most of these approaches follow the basic similarity property principle that states that two or more compounds having a high level of similarity are expected to exert similar biological activity or physicochemical property. Although in some cases this...

Organic semiconductors (OSCs), being light in weight, decomposable, cheap, and flexible, can be an excellent replacement for inorganic semiconductors. Reorganization energy (RE) is an essential parameter that can help determine charge carriers' mobility. Understanding and controlling the reorganization energy (RE) of OSCs is necessary for the desig...

With the exponential progress in the field of cheminformatics, the conventional modeling approaches have so far been to employ supervised and unsupervised machine learning (ML) and deep learning models, utilizing the standard molecular descriptors, which represent the structural, physicochemical, and electronic properties of a particular compound....

This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure–activity relationships (RASAR). Molecular similarity-based computational tool...

Cheminformatics and Machine Learning (ML) have seen exponential progress in the last decade, in the field of chemical risk assessment, due to their efficiency, accuracy, and reliability. The constant evolution of New Approach Methodologies (NAM) has inspired researchers around the globe to deviate from conventional approaches and adopt or develop n...

An external chemical substance (which may be a medicinal drug or an exposome), after ingestion, undergoes a series of dynamic movements and metabolic alterations known as pharmacokinetic events while exerting different physiological actions on the body (pharmacodynamics events). Plasma protein binding and hepatocyte intrinsic clearance are crucial...

In this study, experimental data on the adsorption of organic pollutants onto microplastics in different aqueous environments were used to develop QSPR and q-RASPR models.

With the exponential progress in the field of cheminformatics, the conventional modeling approaches have so far been to employ supervised and unsupervised machine learning (ML) and deep learning models, utilizing the standard molecular descriptors, which represent the structural, physicochemical, and electronic properties of a particular compound....

The demand for novel flavors and fragrance (F&F) compounds has increased, highlighting the need for a systematic design approach. Currently, the F&F industry relies heavily on experimental approaches without considering the potential consequences of altering the features that contribute to the fragrance of the compound. In silico approaches have gr...

A key step in building regulatory acceptance of alternative or non-animal test methods has long been the use of interlaboratory comparisons or Round Robins (RR), in which a common test material and standard operating procedure is provided to all participants, who measure the specific endpoint and return their data for statistical comparison to demo...

In the current research, we have unveiled an advanced technique termed the quantitative Read-Across Structure-Activity Relationship (q-RASAR) framework to harness the power of machine learning (ML) for significantly enhancing the precision of predictions related to blood-brain barrier (BBB) permeability. It is important to emphasize that the centra...

The performance and stability are the two major areas of concern related to energetic materials (EMs). Balancing both the performance and stability simultaneously can result in the development of new advanced compounds that will not only perform better but at the same time be highly stable to physical/chemical/thermal stress. In this study, we aime...

Source: Environmental Science Advances Report number: ISSN 2754-7000 Publisher: Royal Society of Chemistry

Due to the lack of experimental toxicity data of environmental chemicals, there arises a need to fill data gaps by in silico approaches. One of the most commonly used in silico approaches for toxicity assessment of small datasets is the Quantitative Structure-Activity Relationship (QSAR), which generates predictive models for the efficient predicti...

The intricate nature of the blood–brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure–activity relationship (c-RASAR) framework, that leverages m...

Direct or indirect consumption of pesticides and their related products by humans and other living organisms without safe dosing may pose a health risk. The risk may arise after a...

Computational techniques, such as quantitative structure–property relationships (QSPRs), can play a significant role in exploring the important chemical features essential for the degree of sorption or sludge/water partition coefficient (Kd) towards sewage sludge of wastewater treatment process to evaluate the environmental consequence and risk of...

Toxicity assessment of environmental chemicals is an integral aspect of assessing the sustainability of flora and fauna constituting the aquatic and terrestrial ecosystems. A wide variety of living organisms are constantly being exposed to these chemicals, most of which generate toxic effects. Due to the lack of experimental toxicity data of enviro...

A comprehensive knowledge of the physical and chemical properties of nanomaterials (NMs) is necessary to design them effectively for regulated use. Although NMs are utilized in therapeutics, their cytotoxicity has attracted great attention. Nanoscale quantitative structure–property relationship (nano-QSPR) models can help in understanding the relat...

Humans and other living species of the ecosystem are constantly exposed to a wide range of chemicals of natural as well as synthetic origin. A multitude of compounds exert profound long-term detrimental health effects. The chronic toxicity profile of chemicals is of utmost importance for long-term risk assessment. Experimental testing of the chroni...

The application of various in‐silico ‐based approaches for the prediction of various properties of materials has been an effective alternative to experimental methods. Recently, the concepts of Quantitative structure‐property relationship (QSPR) and read‐across (RA) methods were merged to develop a new emerging chemoinformatic tool: read‐across str...

The novel approaches of q-RA and q-RASAR appear to have much promise in quantitative predictions and data gap-filling with applications in drug design, materials science, and predictive toxicology. The similarity metrics and error considerations may be further refined, possibly with the application of sophistical machine learning approaches, for fu...

Java-based tools for quantitative read-across (Quantitative Read-Across v 4.2.1) and q-RASAR (RASAR v 3.0.2) have been developed and made available from the DTC Laboratory websites. The application of q-RA has been done for several nanotoxicity and ecotoxicity endpoints while q-RASAR has been successfully applied for the predictions of several endp...

Molecular structures are determinants of molecular properties including physicochemical properties, biological activities, and toxicities. The atom types, bond types, functionalities, interatomic distances, arrangements of functionality within a molecular skeleton, branching, cyclicity, hydrogen bonding propensity, molecular size, etc. are critical...

Recently the concept of read-across has been applied to machine-learning-based supervised predictions for quantitative-read-across (q-RA) which have shown superior performance over QSAR-derived predictions in several examples. This was further extended to the generation of QSAR-like statistical models, i.e., quantitative read-across structure-activ...

Read-across is originally a non-statistical grouping approach for data gap filling. The grouping of chemicals may be done based on similarities in structural features, physicochemical properties, absorption/metabolism/distribution properties, etc. Based on the similarities to the source compounds with a known target property, predictions of the pro...

This brief offers an introduction to the fascinating new field of quantitative read-across structure-activity relationships (q-RASAR) as a cheminformatics modeling approach in the background of quantitative structure-activity relationships (QSAR) and read-across (RA) as data gap-filling methods. It discusses the genesis and model development of q-R...

Mutagenicity is considered an important endpoint from the regulatory, environmental and medical points of view. Due to the wide number of compounds that may be of concern and the enormous expenses (in terms of time, money, and animals) associated with rodent mutagenicity bioassays, this endpoint is a major target for the development of alternative...

All sorts of chemicals get degraded under various environmental stresses, and the degradates coexist with the parent compounds as mixtures in the environment. Antibiotics emerge as an additional concern due to the bioactive nature of both the parent compound and degradation products and their combined exposure to the environment. Therefore, environ...

We have developed quantitative toxicity prediction models for organic pesticides of agricultural importance considering different fish species using a novel quantitative Read-across structure-activity relationship (q-RASAR) approach. The current study uses experimental (Log 1/LC50) data of organic pesticides to various fish species, including Rainb...

The quantitative Read-Across Structure–Property Relationship (q-RASPR) is a novel method for the property predictions derived from the integrated concept of both similarity-based predictions (i.e., Read-Across or RA) and statistical modelling-based predictions (i.e., Quantitative Structure–Property Relationship or QSPR). The main performance index...

Given the rapid growth of nanotechnology, it is essential to know the hazardous effects of metal oxide nanoparticles (MeOx NPs) posed to the living organisms within the ecosystem. With the...

Nanoparticles with their unique features have attracted researchers over the past decades. Heavy metals, upon release and emission, may interact with different environmental components, which may lead to co-exposure to living organisms. Nanoscale titanium dioxide (nano-TiO 2 ) can adsorb heavy metals. The current idea is that nanoparticles (NPs) ma...

Environmental chemicals and contaminants cause a wide array of harmful implications to terrestrial and aquatic life which ranges from skin sensitization to acute oral toxicity. The current study aims to assess the quantitative skin sensitization potential of a large set of industrial and environmental chemicals acting through different mechanisms u...

We have reported here a quantitative read-across structure-activity relationship (q-RASAR) model for the prediction of binary mixture toxicity (acute contact toxicity) in honey bees. Both the quantitative structure-activity relationship (QSAR) and the similarity-based read-across algorithms are used simultaneously for enhancing the predictability o...

The advancements in the field of cheminformatics have led to a reduction in animal testing to estimate the activity, property, and toxicity of query chemicals. Read-across structure-activity relationship (RASAR) is an emerging concept that utilizes various similarity functions derived from chemical information to develop highly predictive models. U...

Alzheimer’s disease (AD) is a neurological ailment that affects older people and causes a steady decline in their cognitive function. The cognitive impairments found are presumed to be the result of synapse disruption and neurochemical deficits. Several neurochemical abnormalities have been found throughout progressive aging, and these have been co...

The use of biomarkers in the detection of early and preclinical Alzheimer’s disease (AD) has become of central importance. The use of in vivo amyloid and tau imaging agents can detect early AD pathological processes and subsequent neurodegeneration. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are effici...

In silico approaches for activity/toxicity predictions have gained attention recently, and these are accepted by various regulations like EU-REACH. Aspects like reproducibility, less ethical complications, no animal use and reduced time are some of the reasons why researchers nowadays are shifting toward the in silico approaches for prediction. Qua...

Alzheimer’s disease (AD) is one of the major public health concerns. Phosphodiesterases (PDEs) are a major class of enzymes which hydrolyze two second messengers: cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP). Due to the high expression of various PDE subfamilies in the human brain, PDE inhibition has a substantial...

Alzheimer’s disease (AD) is a progressive neurodegenerative disease distinguished by memory loss, cognitive dysfunction, impaired functional abilities, and behavioral changes. Being the most common form of senile dementia, AD can be characterized by the presence of two types of neuropathological hallmarks: neurofibrillary tangles (NFTs) and senile...

Antibiotics are often found in the environment as pollutants. They are usually found as mixtures in the environment and may produce toxicity against different ecological species due to joint exposure in the sub-optimal range. Sometimes the degradation products of parent chemicals also interact with it and cause mixture toxicity. In this study, we h...

Metal oxide nanoparticles (MeOxNPs) can be made safer by understanding the interaction between the immune system and nanoparticles. A nano-quantitative structure-activity relationship (nano-QSAR) model can be used to evaluate nanoparticle risk quickly and conveniently. The present work attempts to develop nano-QSAR models to determine the inflammat...

Different computational tools are now popularly used as an alternative to experiments for predicting several property endpoints of industrial importance. Recently, read-across and Quantitative structure-property relationship (QSPR) have been merged to develop a new modeling technique read-across structure-property relationship (RASPR) which appears...

The retention time (log tR) of pesticidal compounds in a reverse-phase high-performance liquid chromatography (HPLC) analysis has a direct relationship with lipophilicity, which could be related to the ecotoxicity potential of the compounds. The novel quantitative read-across structure-property relationship (q-RASPR) modeling approach uses similari...

The advancements in the field of cheminformatics have led to a reduction in animal testing to estimate the activity/property/toxicity of query chemicals. Read-Across Structure-Activity Relationship (RASAR) is an emerging concept that utilizes various similarity functions derived from chemical information to develop highly predictive models. Unlike...

The neurotransmitter acetylcholine (ACh) plays a ubiquitous role in cognitive functions including learning and memory with widespread innervation in the cortex, subcortical structures, and the cerebellum. Cholinergic receptors, transporters, or enzymes associated with many neurodegenerative diseases, including Alzheimer’s disease (AD) and Parkinson...

Soil invertebrates serve as great biological indicators of soil quality. However, there are very few in silico models developed so far on the soil toxicity of chemicals against soil invertebrates due to paucity of data. In this study, three available soil ecotoxicity data (pLC50, pLOEL and pNOEL) against the soil invertebrate Folsomia candida were...

The availability of experimental nanotoxicity data is in general limited which warrants both the use of in silico methods for data gap filling and exploring novel methods for effective modeling. Read-Across Structure-Activity Relationship (RASAR) is an emerging cheminformatic approach that combines the usefulness of a QSAR model and similarity-base...

The novel quantitative read-across structure-activity relationship (q-RASAR) approach uses read-across-derived similarity functions in the quantitative structure-activity relationship (QSAR) modeling framework in a unique way for supervised model generation. The aim of this study is to explore how this workflow enhances the external (test set) pred...

To fight COVID-19 with uncountable medications and bioproducts throughout the world has taken us to another challenge of ecotoxicity. The indiscriminate usage followed by improper disposal of unused antibacterials, antivirals, antimalarials, immunomodulators, angiotensin II receptor blockers, corticosteroids, anthelmintics, anticoagulants etc. can...

Polychlorinated naphthalenes (PCNs) are produced from a variety of industrial sources, and they reach the aquatic ecosystems by the dry-wet deposition from the atmosphere and also by the drainage from the land surfaces. Then the PCNs can be transmitted through the food chain to humans and show toxic effects on different aquatic animals as well as h...

In this study, the specific surface area of various perovskites was modeled using a novel quantitative read‐across structure‐property relationship (q‐RASPR) approach, which clubs both Read‐Across (RA) and quantitative structure‐property relationship (QSPR) together. After optimization of the hyper‐parameters, certain similarity‐based error measures...

Predictive toxicology is a non-animal testing approach that includes in silico prediction methods like quantitative structure-activity relationship (QSAR) and Read-Across to assess the toxicity of query chemicals and bridge data gaps. Recently, a cheminformatic concept known as Read-Across Structure-Activity Relationship (RASAR) has emerged, and th...

Bioconcentration factors (BCFs) are markers of chemical substance accumulation in organisms, and they play a significant role in determining the environmental risk of various chemicals. Experiments to obtain BCFs are expensive and time-consuming; therefore, it is better to estimate BCF early in the chemical development process. The current research...

In this study, the specific surface area of various perovskites was modeled using a novel quantitative read-across structure-property relationship (q-RASPR) approach, which clubs both Read-Across (RA) and quantitative structure-property relationship (QSPR) together. After optimization of the hyper-parameters, certain similarity-based error measures...

The rate and extent of biodegradation of petroleum hydrocarbons in the different aquatic environments is an important element to address. The major avenue for removing petroleum hydrocarbons from the environment is thought to be biodegradation. The present study involves the development of predictive quantitative structure–property relationship (QS...

Read-Across Structure-Activity Relationship (RASAR) is an emerging cheminformatic approach that combines the usefulness of a QSAR model and similarity-based Read-Across predictions. In this work, we have generated a simple, interpretable, and transferable quantitative-RASAR (q-RASAR) model which can efficiently predict the cytotoxicity of TiO2-base...

The demand for nutrients and new technologies has increased with population growth. The agro-technological revolution with metal oxide engineered nanoparticles (MeOx ENPs) has the potential to reform the resilient agricultural system while maintaining the security of food. When utilized extensively, MeOx ENPs may have unintended toxicological effec...

The widespread use of pharmaceuticals followed by their improper disposal measures has transformed these chemicals to become a group of potent contaminants of emerging concerns. The lack of ecotoxicity data is largely related to high-cost involvement, time-consuming processes, limited resources, and manpower requirement. In the last few decades, co...

Different classes of chemicals are present in the environment as mixtures. Among them, pharmaceuticals and pesticides are of major concern due to their improper use and disposal, and subsequent additive and non-additive effects. To assess the environmental risk posed by the mixtures of pharmaceuticals and pesticides, a quantitative structure-activi...

Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling to analyze the structural features of these chem...

Soil invertebrates serve as an outstanding biological indicator of the terrestrial ecosystem and overall soil quality, considering their high sensitivity when compared to other indicators of soil quality. In this study, the available soil ecotoxicity data (pEC50) against the soil invertebrate Folsomia candida (C. name: Springtail) (n = 45) were col...

In silico modeling new approach methodologies (NAMs) are viewed as a promising starting point for filling the existing gaps in safety and ecosafety data. Read-across is one of the most widely used alternative tools for hazard assessment, aimed at filling data gaps. However, there are no systematic studies or recommendations on the measures to ident...

Quantitative structure–activity relationship (QSAR) and read-across techniques have recently been merged into a new emerging field of read-across structure–activity relationship (RASAR) that uses the chemical similarity concepts of read-across (an unsupervised step) and finally develops a supervised learning model (like QSAR). The RASAR method has...

Chemicals used in our daily life show different toxic effects to the aquatic and terrestrial species and thus hamper the ecological balance. In the present time, amphibians are one of them, which are threatened to be extinct. Quantitative structure-activity relationship (QSAR) is an useful tool for prediction involving less time, money and manpower...

The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 5.9 million deaths documented so far since the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the e...

Purpose of Review Unabated environmental pollution has become a serious concern of this planet in recent times. The living environmental compartments including human beings are always in the exposure of multiple chemicals, and there are chances of the synergistic effect of a compound in presence of others. Therefore, for a better risk assessment of...

The quantitative structure–activity relationship (QSAR) modelling of mixtures is not as simple as that for individual chemicals, and it needs additional care to avoid overestimation of the performance. In this research, we have developed a 2D-QSAR model using only 2D interpretable and reproducible descriptors to predict the aquatic toxicity of mixt...

In silico modeling new approach methodologies (NAMs) are viewed as a promising starting point for filling the existing gaps in safety and ecosafety data. Read-across is one of the most widely used alternative tools for hazard assessment, aimed at filling data gaps. However, there are no systematic studies or recommendations on the measures to ident...

Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling to analyze the structural features of these chem...

Quantitative structure-activity relationship (QSAR) and read-across techniques have recently been merged into a new emerging field of Read-across Structure-Activity Relationship (RASAR) that uses the chemical similarity concepts of read-across (an unsupervised step) and finally develops a supervised learning model (like QSAR). The RASAR method has...

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak is posing a serious public health threat worldwide in the form of COVD-19. Herein, we have performed two-dimensional quantitative structure-activity relationship (2D-QSAR) and three-dimensional pharmacophore modelling analysis employing inhibitors of 3-chymotrypsin-like prote...

Hypoxia is the prime component of tumor microenvironment that plays a pivotal role in cancer progression. Nitroaromatic compounds are known to enhance the sensitivity of hypoxic cells to ionizing radiation. The application of computational tools like Quantitative Structure-Activity Relationship (QSAR) can be used to predict newly developed nitroaro...