<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gaur, A. S.</style></author><author><style face="normal" font="default" size="100%">Bhardwaj, A.</style></author><author><style face="normal" font="default" size="100%">Sharma, A.</style></author><author><style face="normal" font="default" size="100%">John, L.</style></author><author><style face="normal" font="default" size="100%">Vivek, M. R.</style></author><author><style face="normal" font="default" size="100%">Tripathi, N.</style></author><author><style face="normal" font="default" size="100%">Bharatam, P. V.</style></author><author><style face="normal" font="default" size="100%">Kumar, R.</style></author><author><style face="normal" font="default" size="100%">Janardhan, S.</style></author><author><style face="normal" font="default" size="100%">Mori, A.</style></author><author><style face="normal" font="default" size="100%">Banerji, A.</style></author><author><style face="normal" font="default" size="100%">Lynn, A. M.</style></author><author><style face="normal" font="default" size="100%">Hemrom, A. J.</style></author><author><style face="normal" font="default" size="100%">Passi, A.</style></author><author><style face="normal" font="default" size="100%">Singh, A.</style></author><author><style face="normal" font="default" size="100%">Kumar, A.</style></author><author><style face="normal" font="default" size="100%">Muvva, C.</style></author><author><style face="normal" font="default" size="100%">Madhuri, C.</style></author><author><style face="normal" font="default" size="100%">Choudhury, C.</style></author><author><style face="normal" font="default" size="100%">Kumar, D. A.</style></author><author><style face="normal" font="default" size="100%">Pandit, D.</style></author><author><style face="normal" font="default" size="100%">Bharti, D. R.</style></author><author><style face="normal" font="default" size="100%">Kumar, D.</style></author><author><style face="normal" font="default" size="100%">Singam, E. A.</style></author><author><style face="normal" font="default" size="100%">Raghava, G. P.</style></author><author><style face="normal" font="default" size="100%">Sailaja, H.</style></author><author><style face="normal" font="default" size="100%">Jangra, H.</style></author><author><style face="normal" font="default" size="100%">Raithatha, K.</style></author><author><style face="normal" font="default" size="100%">Tanneeru, K.</style></author><author><style face="normal" font="default" size="100%">Chaudhary, K.</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, M.</style></author><author><style face="normal" font="default" size="100%">Prasanthi, M.</style></author><author><style face="normal" font="default" size="100%">Kumar, N.</style></author><author><style face="normal" font="default" size="100%">Yedukondalu, N.</style></author><author><style face="normal" font="default" size="100%">Rajput, N. K.</style></author><author><style face="normal" font="default" size="100%">Saranya, P. S.</style></author><author><style face="normal" font="default" size="100%">Narang, P.</style></author><author><style face="normal" font="default" size="100%">Dutta, Prantu</style></author><author><style face="normal" font="default" size="100%">Krishnan, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Sciences</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">chemical analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Chemoinformatics</style></keyword><keyword><style  face="normal" font="default" size="100%">computational chemistry</style></keyword><keyword><style  face="normal" font="default" size="100%">Diagnosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug discovery portal</style></keyword><keyword><style  face="normal" font="default" size="100%">Information analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Libraries</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular graphics</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecules</style></keyword><keyword><style  face="normal" font="default" size="100%">Neglected diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Open science</style></keyword><keyword><style  face="normal" font="default" size="100%">Portals</style></keyword><keyword><style  face="normal" font="default" size="100%">tuberculosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Web-based technology</style></keyword><keyword><style  face="normal" font="default" size="100%">Websites</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">MAY</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">129</style></volume><pages><style face="normal" font="default" size="100%">515-531</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Indian</style></custom3><custom4><style face="normal" font="default" size="100%">1.254</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nalla, Viswanadh</style></author><author><style face="normal" font="default" size="100%">Shaikh, Aslam</style></author><author><style face="normal" font="default" size="100%">Bapat, Sanket</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, M.</style></author><author><style face="normal" font="default" size="100%">Yogeeswari, P.</style></author><author><style face="normal" font="default" size="100%">Sriram, D.</style></author><author><style face="normal" font="default" size="100%">Muthukrishnan, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identification of potent chromone embedded [1,2,3]-triazoles as novel anti-tubercular agents</style></title><secondary-title><style face="normal" font="default" size="100%">Royal Society Open Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">APR</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">Article Number: 171750</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">2.243</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Viswanadh, N.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ghotekar, Ganesh S.</style></author></secondary-authors><tertiary-authors><author><style face="normal" font="default" size="100%">Thoke, Mahesh B.</style></author></tertiary-authors><subsidiary-authors><author><style face="normal" font="default" size="100%">Velayudham, R.</style></author><author><style face="normal" font="default" size="100%">Shaikh, Aslam C.</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, M.</style></author><author><style face="normal" font="default" size="100%">Muthukrishnan, M.</style></author></subsidiary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Transition metal free regio-selective C–H hydroxylation of chromanones towards the synthesis of hydroxyl-chromanones using PhI(OAc)2 as the oxidant</style></title><secondary-title><style face="normal" font="default" size="100%">Chemical Communications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">54</style></volume><pages><style face="normal" font="default" size="100%">2252-2255</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">18</style></issue><work-type><style face="normal" font="default" size="100%">Journal Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;6.319&lt;/p&gt;</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bapat, S.</style></author><author><style face="normal" font="default" size="100%">Viswanadh, N.</style></author><author><style face="normal" font="default" size="100%">Mujahid, M.</style></author><author><style face="normal" font="default" size="100%">Shirazi, A. N.</style></author><author><style face="normal" font="default" size="100%">Tiwari, R. K.</style></author><author><style face="normal" font="default" size="100%">Parang, K.</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, M.</style></author><author><style face="normal" font="default" size="100%">Muthukrishnan, M.</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Synthesis, biological evaluation and molecular modeling studies of novel chromone/Aza-Chromone fused alpha-aminophosphonates as src kinase inhibitors</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Scientific &amp; Industrial Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">111-117</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A series &lt;span class=&quot;hitHilite&quot;&gt;of&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;novel&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;chromone&lt;/span&gt;/&lt;span class=&quot;hitHilite&quot;&gt;aza&lt;/span&gt;-&lt;span class=&quot;hitHilite&quot;&gt;chromone&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;fused&lt;/span&gt; alpha-aminophosphonate derivatives were synthesized in good yields using silica chloride &lt;span class=&quot;hitHilite&quot;&gt;as&lt;/span&gt; the catalyst. All the synthesized compounds were tested for their c-&lt;span class=&quot;hitHilite&quot;&gt;Src&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;kinase&lt;/span&gt; inhibitory activity. &lt;span class=&quot;hitHilite&quot;&gt;Aza&lt;/span&gt;-&lt;span class=&quot;hitHilite&quot;&gt;chromone&lt;/span&gt; compound showed &lt;span class=&quot;hitHilite&quot;&gt;Src&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;kinase&lt;/span&gt; inhibition with an IC50 value &lt;span class=&quot;hitHilite&quot;&gt;of&lt;/span&gt; 15.8 mu M. The compounds were subjected to &lt;span class=&quot;hitHilite&quot;&gt;molecular&lt;/span&gt; docking and dynamics simulations to study the atomic level interactions with an unphosphorylated proto-oncogenic tyrosine protein &lt;span class=&quot;hitHilite&quot;&gt;kinase&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;Src&lt;/span&gt; (PDB code 1Y57) &lt;span class=&quot;hitHilite&quot;&gt;as&lt;/span&gt; well &lt;span class=&quot;hitHilite&quot;&gt;as&lt;/span&gt; phosphorylated tyrosine protein &lt;span class=&quot;hitHilite&quot;&gt;kinase&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;Src&lt;/span&gt; (PDB code 2H8H). Docking and &lt;span class=&quot;hitHilite&quot;&gt;molecular&lt;/span&gt; dynamic results revealed phosphorylated &lt;span class=&quot;hitHilite&quot;&gt;Src&lt;/span&gt; tyrosine &lt;span class=&quot;hitHilite&quot;&gt;kinase&lt;/span&gt; protein better results than unphosphorylated tyrosine &lt;span class=&quot;hitHilite&quot;&gt;Src&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;kinase&lt;/span&gt; protein. Chemoinformatics study revealed the compounds had lead like properties. Machine learning (SVR) models were built to study the structure activity correlations. A CC &lt;span class=&quot;hitHilite&quot;&gt;of&lt;/span&gt; 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 &lt;span class=&quot;hitHilite&quot;&gt;Src&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;kinase&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;inhibitors&lt;/span&gt;.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span class=&quot;style1  style7&quot;&gt;&lt;font face=&quot;Verdana&quot;&gt;0.735&lt;/font&gt;&lt;/span&gt;&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kothari, Sonali</style></author><author><style face="normal" font="default" size="100%">Sharma, Shivanandana</style></author><author><style face="normal" font="default" size="100%">Shejwal, Sanskruti</style></author><author><style face="normal" font="default" size="100%">Kazi, Aqsa</style></author><author><style face="normal" font="default" size="100%">D'Silva, Michela</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Explainable AI-assisted web application in cancer drug value prediction</style></title><secondary-title><style face="normal" font="default" size="100%">MethodsX</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUN</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;u-margin-s-bottom&quot; id=&quot;spara003&quot; style=&quot;box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-left: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, &amp;quot;Times New Roman&amp;quot;, Times, STIXGeneral, &amp;quot;Cambria Math&amp;quot;, &amp;quot;Lucida Sans Unicode&amp;quot;, &amp;quot;Microsoft Sans Serif&amp;quot;, &amp;quot;Segoe UI Symbol&amp;quot;, &amp;quot;Arial Unicode MS&amp;quot;, serif, sans-serif; font-size: 16px; margin-bottom: 16px !important;&quot;&gt;
	In recent years, there has been an increase in the interest in adopting Explainable Artificial Intelligence (XAI) for healthcare. The proposed system includes
	&lt;ul class=&quot;list&quot; style=&quot;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;&quot;&gt;
		&lt;li class=&quot;react-xocs-list-item&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; display: contents;&quot;&gt;
			&lt;span class=&quot;list-label&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; text-align: right;&quot;&gt;•&lt;/span&gt;
			&lt;div class=&quot;u-margin-s-bottom&quot; id=&quot;para0001a&quot; style=&quot;box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-bottom: 16px !important; margin-left: 0px; padding: 0px;&quot;&gt;
				&lt;span style=&quot;box-sizing: border-box; margin: 0px; padding: 0px;&quot;&gt;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.&lt;/span&gt;&lt;/div&gt;
		&lt;/li&gt;
		&lt;li class=&quot;react-xocs-list-item&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; display: contents;&quot;&gt;
			&lt;span class=&quot;list-label&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; text-align: right;&quot;&gt;•&lt;/span&gt;
			&lt;div class=&quot;u-margin-s-bottom&quot; id=&quot;para0001a1&quot; style=&quot;box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-bottom: 16px !important; margin-left: 0px; padding: 0px;&quot;&gt;
				&lt;span style=&quot;box-sizing: border-box; margin: 0px; padding: 0px;&quot;&gt;A model outperformed existing models due to extensive training and evaluation on a large cancer medication chemical compounds dataset.&lt;/span&gt;&lt;/div&gt;
		&lt;/li&gt;
		&lt;li class=&quot;react-xocs-list-item&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; display: contents;&quot;&gt;
			&lt;span class=&quot;list-label&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; text-align: right;&quot;&gt;•&lt;/span&gt;
			&lt;div class=&quot;u-margin-s-bottom&quot; id=&quot;para0001a2&quot; style=&quot;box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-bottom: 16px !important; margin-left: 0px; padding: 0px;&quot;&gt;
				&lt;span style=&quot;box-sizing: border-box; margin: 0px; padding: 0px;&quot;&gt;Insights into the causation and correlation between the dependent and independent actors in the chemical composition of the cancer cell.&lt;/span&gt;&lt;/div&gt;
		&lt;/li&gt;
	&lt;/ul&gt;
&lt;/div&gt;
&lt;div class=&quot;u-margin-s-bottom&quot; id=&quot;spara007&quot; style=&quot;box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-left: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, &amp;quot;Times New Roman&amp;quot;, Times, STIXGeneral, &amp;quot;Cambria Math&amp;quot;, &amp;quot;Lucida Sans Unicode&amp;quot;, &amp;quot;Microsoft Sans Serif&amp;quot;, &amp;quot;Segoe UI Symbol&amp;quot;, &amp;quot;Arial Unicode MS&amp;quot;, serif, sans-serif; font-size: 16px; margin-bottom: 16px !important;&quot;&gt;
	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.&lt;/div&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;
	Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;
	1.7&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kothari, Sonali</style></author><author><style face="normal" font="default" size="100%">Sharma, Shivanandana</style></author><author><style face="normal" font="default" size="100%">Shejwal, Sanskruti</style></author><author><style face="normal" font="default" size="100%">Kazi, Aqsa</style></author><author><style face="normal" font="default" size="100%">D'Silva, Michela</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An explainable AI-assisted web application in cancer drug value prediction</style></title><secondary-title><style face="normal" font="default" size="100%">MethodsX</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUN</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">102696</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;u-margin-s-bottom&quot; id=&quot;spara003&quot; style=&quot;box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-left: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, &amp;quot;Times New Roman&amp;quot;, Times, STIXGeneral, &amp;quot;Cambria Math&amp;quot;, &amp;quot;Lucida Sans Unicode&amp;quot;, &amp;quot;Microsoft Sans Serif&amp;quot;, &amp;quot;Segoe UI Symbol&amp;quot;, &amp;quot;Arial Unicode MS&amp;quot;, serif, sans-serif; font-size: 16px; margin-bottom: 16px !important;&quot;&gt;
	In recent years, there has been an increase in the interest in adopting Explainable Artificial Intelligence (XAI) for healthcare. The proposed system includes
	&lt;ul class=&quot;list&quot; style=&quot;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;&quot;&gt;
		&lt;li class=&quot;react-xocs-list-item&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; display: contents;&quot;&gt;
			&lt;span class=&quot;list-label&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; text-align: right;&quot;&gt;•&lt;/span&gt;
			&lt;div class=&quot;u-margin-s-bottom&quot; id=&quot;para0001a&quot; style=&quot;box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-bottom: 16px !important; margin-left: 0px; padding: 0px;&quot;&gt;
				&lt;span class=&quot;list-content&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; min-width: 0px;&quot;&gt;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.&lt;/span&gt;&lt;/div&gt;
		&lt;/li&gt;
		&lt;li class=&quot;react-xocs-list-item&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; display: contents;&quot;&gt;
			&lt;span class=&quot;list-label&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; text-align: right;&quot;&gt;•&lt;/span&gt;
			&lt;div class=&quot;u-margin-s-bottom&quot; id=&quot;para0001a1&quot; style=&quot;box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-bottom: 16px !important; margin-left: 0px; padding: 0px;&quot;&gt;
				&lt;span class=&quot;list-content&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; min-width: 0px;&quot;&gt;A model outperformed existing models due to extensive training and evaluation on a large cancer medication chemical compounds dataset.&lt;/span&gt;&lt;/div&gt;
		&lt;/li&gt;
		&lt;li class=&quot;react-xocs-list-item&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; display: contents;&quot;&gt;
			&lt;span class=&quot;list-label&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; text-align: right;&quot;&gt;•&lt;/span&gt;
			&lt;div class=&quot;u-margin-s-bottom&quot; id=&quot;para0001a2&quot; style=&quot;box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-bottom: 16px !important; margin-left: 0px; padding: 0px;&quot;&gt;
				&lt;span class=&quot;list-content&quot; style=&quot;box-sizing: border-box; margin: 0px; padding: 0px; min-width: 0px;&quot;&gt;Insights into the causation and correlation between the dependent and independent actors in the chemical composition of the cancer cell.&lt;/span&gt;&lt;/div&gt;
		&lt;/li&gt;
	&lt;/ul&gt;
&lt;/div&gt;
&lt;div class=&quot;u-margin-s-bottom&quot; id=&quot;spara007&quot; style=&quot;box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-left: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, &amp;quot;Times New Roman&amp;quot;, Times, STIXGeneral, &amp;quot;Cambria Math&amp;quot;, &amp;quot;Lucida Sans Unicode&amp;quot;, &amp;quot;Microsoft Sans Serif&amp;quot;, &amp;quot;Segoe UI Symbol&amp;quot;, &amp;quot;Arial Unicode MS&amp;quot;, serif, sans-serif; font-size: 16px; margin-bottom: 16px !important;&quot;&gt;
	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.&lt;/div&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;
	Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;
	1.7&lt;/p&gt;
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