Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis - Gaur, A. S.; Bhardwaj, A.; Sharma, A.; John, L.; Vivek, M. R.; Tripathi, N.; Bharatam, P. V.; Kumar, R.; Janardhan, S.; Mori, A.; Banerji, A.; Lynn, A. M.; et al. (Journal of Chemical Sciences, 2017)
Abstract: Molecular Property Diagnostic Suite (MPDS TB) is a web tool (http://mpds.osdd.net) designed to assist the in silico drug discovery attempts towards Mycobacterium tuberculosis (Mtb). MPDS TB tool has nine modules which are classified into data library (1–3), data processing (4–5) and data analysis (6–9). Module 1 is a repository of literature and related information available on the Mtb. Module 2 deals with the protein target analysis of the chosen disease area. Module 3 is the compound library consisting of 110.31 million unique molecules generated from public domain databases and custom designed search tools. Module 4 contains tools for chemical file format conversions and 2D to 3D coordinate conversions. Module 5 helps in calculating the molecular descriptors. Module 6 specifically handles QSAR model development tools using descriptors generated in the Module 5. Module 7 integrates the AutoDock Vina algorithm for docking, while module 8 provides screening filters. Module 9 provides the necessary visualization tools for both small and large molecules. The workflow-based open source web portal, MPDS TB 1.0.1 can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular. Graphical Abstract: SYNOPSIS: A web-based MPDS TB Galaxy tool is developed for assessing therapeutic potential of molecules. MPDS TB is categorized into Data Library, Data Processing and Data Analysis. It can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular. [Figure not available: see fulltext.] © 2017, Indian Academy of Sciences.
Keywords: chemical analysis; Chemoinformatics; computational chemistry; Diagnosis; Drug discovery portal; Information analysis; Libraries; Molecular graphics; Molecules; Neglected diseases; Open science; Portals; tuberculosis; Web-based technology; Websites

Identification of potent chromone embedded [1,2,3]-triazoles as novel anti-tubercular agents - Nalla, Viswanadh; Shaikh, Aslam; Bapat, Sanket; Vyas, Renu; Karthikeyan, M.; Yogeeswari, P.; Sriram, D.; Muthukrishnan, M. (Royal Society Open Science, 2018)
A series of 20 novel chromone embedded [1,2,3]-triazoles derivatives were synthesized via an easy and convenient synthetic procedure starting from 2-hydroxy acetophenone. The in vitro anti-mycobacterial evaluation studies carried out in this work reveal that seven compounds exhibit significant inhibition against Mycobacterium tuberculosis H37Rv strain with MIC in the range of 1.56-12.5 mu g ml(-1). Noticeably, compound 6s was the most potent compound in vitro with a MIC value of 1.56 mu g ml(-1). Molecular docking and chemoinformatics studies revealed that compound 6s displayed drug-like properties against the enoyl-acyl carrier protein reductase of M. tuberculosis further establishing its potential as a potent inhibitor.

Transition metal free regio-selective C–H hydroxylation of chromanones towards the synthesis of hydroxyl-chromanones using PhI(OAc)2 as the oxidant - Viswanadh, N. (Chemical Communications, 2018)
The chromanone scaffold is considered as a privileged structure in drug discovery. Herein, we report a highly efficient PhI(OAc)2 mediated regioselective, direct C–H hydroxylation of chromanones. This method offers easy access to substituted 6-hydroxy chromanones in moderate to good isolated yields, thus paving the way for their pharmaceutical studies.

Synthesis, biological evaluation and molecular modeling studies of novel chromone/Aza-Chromone fused alpha-aminophosphonates as src kinase inhibitors - Bapat, S.; Viswanadh, N.; Mujahid, M.; Shirazi, A. N.; Tiwari, R. K.; Parang, K.; Karthikeyan, M.; Muthukrishnan, M.; Vyas, Renu (Journal of Scientific & Industrial Research, 2019)
A series of novel chromone/aza-chromone fused alpha-aminophosphonate derivatives were synthesized in good yields using silica chloride as the catalyst. All the synthesized compounds were tested for their c-Src kinase inhibitory activity. Aza-chromone compound showed Src kinase inhibition with an IC50 value of 15.8 mu M. The compounds were subjected to molecular docking and dynamics simulations to study the atomic level interactions with an unphosphorylated proto-oncogenic tyrosine protein kinase Src (PDB code 1Y57) as well as phosphorylated tyrosine protein kinase Src (PDB code 2H8H). Docking and molecular dynamic results revealed phosphorylated Src tyrosine kinase protein better results than unphosphorylated tyrosine Src kinase protein. Chemoinformatics study revealed the compounds had lead like properties. Machine learning (SVR) models were built to study the structure activity correlations. A CC of 0.835 was obtained when the SVR model was applied to the 17 synthesized compounds. It is envisaged that the work will provide guidelines for future drug design efforts for Src kinase inhibitors.

An explainable AI-assisted web application in cancer drug value prediction - Kothari, Sonali; Sharma, Shivanandana; Shejwal, Sanskruti; Kazi, Aqsa; D'Silva, Michela; Karthikeyan, M. (MethodsX, 2024)
<div class="u-margin-s-bottom" id="spara003" style="box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-left: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px; margin-bottom: 16px !important;"> In recent years, there has been an increase in the interest in adopting Explainable Artificial Intelligence (XAI) for healthcare. The proposed system includes <ul class="list" style="box-sizing: border-box; margin: 16px 0px; padding-right: 0px; padding-left: 0px; list-style: none; display: grid; grid-template-columns: fit-content(15%) fit-content(85%); gap: 0px 16px;"> • An XAI model for cancer drug value prediction. The model provides data that is easy to understand and explain, which is critical for medical decision-making. It also produces accurate projections. • A model outperformed existing models due to extensive training and evaluation on a large cancer medication chemical compounds dataset. • Insights into the causation and correlation between the dependent and independent actors in the chemical composition of the cancer cell. <div class="u-margin-s-bottom" id="spara007" style="box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-left: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px; margin-bottom: 16px !important;"> While the model is evaluated on Lung Cancer data, the architecture offered in the proposed solution is cancer agnostic. It may be scaled out to other cancer cell data if the properties are similar. The work presents a viable route for customizing treatments and improving patient outcomes in oncology by combining XAI with a large dataset. This research attempts to create a framework where a user can upload a test case and receive forecasts with explanations, all in a portable PDF report.

Explainable AI-assisted web application in cancer drug value prediction - Kothari, Sonali; Sharma, Shivanandana; Shejwal, Sanskruti; Kazi, Aqsa; D'Silva, Michela; Karthikeyan, M. (MethodsX, 2024)
<div class="u-margin-s-bottom" id="spara003" style="box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-left: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px; margin-bottom: 16px !important;"> In recent years, there has been an increase in the interest in adopting Explainable Artificial Intelligence (XAI) for healthcare. The proposed system includes <ul class="list" style="box-sizing: border-box; margin: 16px 0px; padding-right: 0px; padding-left: 0px; list-style: none; display: grid; grid-template-columns: fit-content(15%) fit-content(85%); gap: 0px 16px;"> • An XAI model for cancer drug value prediction. The model provides data that is easy to understand and explain, which is critical for medical decision-making. It also produces accurate projections. • A model outperformed existing models due to extensive training and evaluation on a large cancer medication chemical compounds dataset. • Insights into the causation and correlation between the dependent and independent actors in the chemical composition of the cancer cell. <div class="u-margin-s-bottom" id="spara007" style="box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-left: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px; margin-bottom: 16px !important;"> While the model is evaluated on Lung Cancer data, the architecture offered in the proposed solution is cancer agnostic. It may be scaled out to other cancer cell data if the properties are similar. The work presents a viable route for customizing treatments and improving patient outcomes in oncology by combining XAI with a large dataset. This research attempts to create a framework where a user can upload a test case and receive forecasts with explanations, all in a portable PDF report.