Leveraging environmental microbial indicators in wastewater for data-driven disease diagnostics.

Introduction: Wastewater-based surveillance (WBS) is an emerging tool for monitoring the spread of infectious diseases, such as SARS-CoV-2, in community settings. Environmental factors, including water quality parameters and seasonal variations, may influence the prevalence of viral particles in wastewater. This study aims to explore the relationships between these factors and the incidence of SARS-CoV-2 across 28 monitoring sites, spanning different seasons and water strata.

Integrating (deep) machine learning and cheminformatics for predicting human intestinal absorption of small molecules.

The oral route is the most preferred route for drug delivery, due to which the largest share of the pharmaceutical market is represented by oral drugs. Human intestinal absorption (HIA) is closely related to oral bioavailability making it an important factor in predicting drug absorption. In this study, we focus on predicting drug permeability at HIA as a marker for oral bioavailability.

Molecular Property Diagnostic Suite for COVID-19 (MPDS): an open-source disease-specific drug discovery portal.

Unlabelled: Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDS was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server.

An interpretative review of the wastewater-based surveillance of the SARS-CoV-2: where do we stand on its presence and concern?

Wastewater-based epidemiology (WBE) has been used for monitoring infectious diseases like polio, hepatitis, etc. since the 1940s. It is also being used for tracking the SARS-CoV-2 at the population level.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL): a structure-based classification of the chemical space.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL) is an open-source Galaxy-based cheminformatics web portal which presents a structure-based classification of the molecules. A structure-based classification of nearly 150 million unique compounds, obtained from 42 publicly available databases and curated for redundancy removal through 97 hierarchically well-defined atom composition-based portions, has been done. These are further subjected to 56-bit fingerprint-based classification algorithm which led to the formation of 56 structurally well-defined classes.

Analyzing the aromatic-aromatic interactions in proteins: AID 2.0.

The Aromatic-Aromatic Interactions Database (AID) is a comprehensive repository dedicated to documenting aromatic-aromatic (π-π) networks observed in experimentally determined protein structures. The first version of AID was reported in 2011 [Int J Biol Macromol, 2011, 48, 540]. It has undergone a series of significant updates, leading to its current version, which focuses on the identification and analysis of 3,444,619 π-π networks from proteins.

Analyzing the cation-aromatic interactions in proteins: Cation-aromatic database V2.0.

The cation-aromatic database (CAD) is a comprehensive repository of cation-aromatic motifs found in experimentally determined protein structures, first reported in 2007 [Proteins, 2007, 67, 1179]. The present article is an update of CAD that contains information of approximately 27.26 million cation-aromatic motifs.

Machine learning based dynamic consensus model for predicting blood-brain barrier permeability.

The blood-brain barrier (BBB) is an important defence mechanism that restricts disease-causing pathogens and toxins to enter the brain from the bloodstream. In recent years, many in silico methods were proposed for predicting BBB permeability, however, the reliability of these models is questionable due to the smaller and class-imbalance dataset which subsequently leads to a very high false positive rate. In this study, machine learning and deep learning-based predictive models were built using XGboost, Random Forest, Extra-tree classifiers and deep neural network.

Galaxy for open-source computational drug discovery solutions.

Introduction: Drug discovery in academia and industry poses contrasting challenges. While academia focuses on producing new knowledge, industry is keen on product development and success in clinical trials. Galaxy is a web-based open-source computational workbench which is used to analyze large datasets and is customized to integrate analysis and visualization tools in a single framework.

Assessing machine learning approaches for predicting failures of investigational drug candidates during clinical trials.

One of the major challenges in drug development is having acceptable levels of efficacy and safety throughout all the phases of clinical trials followed by the successful launch in the market. While there are many factors such as molecular properties, toxicity parameters, mechanism of action at the target site, etc. that regulates the therapeutic action of a compound, a holistic approach directed towards data-driven studies will invariably strengthen the predictive toxicological sciences.

OSADHI - An online structural and analytics based database for herbs of India.

The current study aims to develop a PAN India database of medicinal plants along with their phytochemicals and geographical availability. The database consists of 6959 unique medicinal plants belonging to 348 families which are available across 28 states and 8 union territories of India. The database sources the information on four different sections - traditional knowledge, geographical indications, phytochemicals, and chemoinformatics.

Seroepidemiological and genomic investigation of SARS-CoV-2 spread in North East region of India.

Purpose: Seroepidemiology and genomic surveillance are valuable tools to investigate infection transmission during a pandemic. North East (NE) India is a strategically important region being the gateway connecting the country with Southeast Asia. Here, we examined the spread of SARS-CoV-2 in NE India during the first and second waves of COVID-19 using serological and whole genome sequencing approaches.

North East India medicinal plants database (NEI-MPDB).

The rich biodiversity of North East India is one of the recognized biodiversity hotspots of the world. This region comprises of eight states (Assam, Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura) with diverse ethnic communities having invaluable traditional knowledge/practices, passed through genesis. The medicinal plants in this region are rich in natural products/phytochemicals and have been used extensively by pharmaceutical industries.

Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite.

A fragment-based drug discovery (FBDD) approach has traditionally been of utmost significance in drug design studies. It allows the exploration of large chemical space to find novel scaffolds and chemotypes which can be improved into selective inhibitors with good affinity. In the current work, several public domain chemical libraries (ChEMBL, DrugCentral, PDB ligands, COCONUT, and SAVI) comprising bioactive and virtual molecules were retrieved to develop a fragment library.

Chemoinformatics and Machine Learning Approaches for Identifying Antiviral Compounds.

Current pandemics propelled research efforts in unprecedented fashion, primarily triggering computational efforts towards new vaccine and drug development as well as drug repurposing. There is an urgent need to design novel drugs with targeted biological activity and minimum adverse reactions that may be useful to manage viral outbreaks. Hence an attempt has been made to develop Machine Learning based predictive models that can be used to assess whether a compound has the potency to be antiviral or not.