Experienced Assistant Professor with a demonstrated history of working in the education management industry. Skilled in Customer Service, Leadership, data analysis, bioinformatics and tools and algorithm development.
Strong education professional with a Master of Science (M.Sc.) focused in Microbiology, General from Dept. Of Microbiology, Amravati University, Advanced Diploma in Bioinformatics (Pune University), doing Ph.D. in the field of meta-genomics.
EDUCATION
M.Sc., ADB,
RESEARCH, TEACHING, or OTHER INTERESTS
Biotechnology, Multidisciplinary, Structural Biology, Cancer Research
Reshaping the Immune Landscape in Women, Elderly, and Diseased Individuals with Innovative Personalized Nutritional Interventions Pritee Chunarkar Patil, Bipinraj Nirichan Kunchiraman Clinical Nutrition in Special Populations Women Elderly Children and Ill Patients, 2026 The escalating prevalence of immune-related disorders and the pressing need for sustainable healthcare strategies underscore the importance of individualized nutritional interventions for bolstering immune resilience. Recent advancements in research, elucidating the intricate relationship between diet and immune health, have catalyzed a paradigm shift in health and disease management. This shift advocates for a redefinition of approaches to nutrition and immunity, emphasizing the necessity of personalized nutrition tailored to individual dietary requirements and health conditions. Personalized nutrition represents a bespoke strategy for developing dietary plans based on unique nutritional needs, personal preferences, genetic predispositions, gut microbiome composition, and metabolic profiles, aiming to enhance health outcomes, mitigate nutritional deficiencies, and prevent diseases. This approach relies on sophisticated diagnostic tools and biomarkers to accurately assess and monitor an individual's health status. Given that various demographic factors, such as health status, age, and gender, significantly influence immune system functionality and responsiveness, it is imperative to understand their effects to devise effective nutritional interventions. This chapter explores the multifaceted aspects of nutritional immunology, the factors influencing it, and the methodologies for designing personalized nutrition plans.
Integrated machine-learning methods for identification of Hepatitis C viral IRES sub-domain-IIa inhibitors Omkar Shinde, Pritee Chunarkar Patil, Gaber E. Eldesoky, Rupesh V. Chikhale Canadian Journal of Chemistry, 2026 Hepatitis C virus (HCV) is a positive-strand RNA virus with an envelope, classified under the genus Hepacivirus within the Flaviviridae family. The virus's genome contains a 5′ untranslated region (UTR), which includes the Internal Ribosome Entry Site (IRES). Inhibition of this region may represent a crucial approach to developing effective treatments for HCV infection. In the current study, FDA-approved drug libraries were collected, curated, and processed using the Mol2Mol package of Reinvent4 to generate a pool of new molecules. The resulting compounds were then sorted using the Tanimoto similarity score. Further refinement was achieved through molecular docking, pharmacokinetic analysis, and toxicity assessment, ultimately identifying four compounds with significant potential for RNA binding. The ligands, identified as 6-Methoxy-2-methyl-1,2,3,4,4a,9,10,10a-octahydrophenanthrene-2,7-diol (M1), 2-Hydroxy-5-((R)-1-hydroxy-2-(((S)-1-phenylethyl)amino)ethyl)benzamide (M2), (S)-2-Amino-N-((S)-2-(2,5-dimethoxyphenyl)-2-hydroxyethyl)-3-methylbutanamide (M3), and 1-(Isopropyl-D-alanyl)octahydro-1H-indole-2-carboxylic acid (M4), demonstrated excellent binding to the IRES subdomain IIa. Their respective binding scores were −31.7912, −52.3670, −29.2841, and −35.2013 kcal/mol. A 200 ns period of molecular dynamics simulation was conducted to explore the dynamic behavior of RNA and the four proposed compounds. Various statistical analyses substantiated the stability and rigidity of the complexes formed between the RNA and the proposed compounds. The free energy landscape and Molecular Mechanics with Generalized Born and Surface Area solvation (MM-GBSA) analyses further demonstrated the potential of these molecules as inhibitors. In conclusion, the final four compounds identified in this study show promise as potential inhibitors targeting the Hepatitis C Virus Internal Ribosome Entry Site, warranting further investigation for their therapeutic potential in HCV treatment.
Identification of potential 3CLpro inhibitors-modulators for human norovirus infections through an advanced virtual screening approach Shovonlal Bhowmick, Tapan Kumar Mistri, Mohammad K. Okla, Ibrahim A. Saleh, Achintya Saha, Pritee Chunarkar Patil Journal of Biomolecular Structure and Dynamics, 2026 The present study aimed to screen small molecular compounds such as human noroviruses (HuNoV) inhibitors/modulators that could potentially be responsible for exhibiting some magnitude of inhibitory/modulatory activity against HuNoV 3CLPro. The structural similarity-based screening against the ChEMBL database is performed against known chemical entities that are presently under pre-clinical trial. After the similarity search, remaining molecules were considered for molecular docking using SCORCH and PLANTS. On detailed analyses and comparisons with the control molecule, three hits (CHEMBL393820, CHEMBL2028556, and CHEMBL3747799) were found to have the potential for HuNoV 3CLpro inhibition/modulation. The binding interaction analysis revealed several critical amino acids responsible to hold the molecules tightly at the close proximity site of the catalytic residues of HuNoV 3CLpro. Further, MD simulation study was performed in triplicate to understand the binding stability and potentiality of the proposed molecule toward HuNov 3CLpro. The binding free energy based on MM-GBSA has revealed their strong interaction affinity with 3CLpro.
Identification of Acetylcholinesterase Inhibitors Through a Pharmacophore-Guided Deep Learning Approach for Therapeutic Applications in Alzheimer's Disease Vikramsinh Sardarsinh Suryawanshi, Md Lutful Islam, Gaber E. Eldesoky, Pritee Chunarkar Patil, Shovonlal Bhowmick, Md Ataul Islam Chemistryselect, 2025 Acetylcholinesterase (AChE) metabolizes the neurotransmitter acetylcholine (ACh), vital for signal transmission in neurons and the central nervous system (CNS). Decreased ACh leads to Alzheimer's disease (AD) and cognitive dysfunction syndromes. This research identified novel AChE inhibitors that deplete ACh, hindering AChE protein activity. Advanced chemoinformatics approaches, including de novo design, molecular docking, and pharmacokinetic analysis, were used to design potential AChE inhibitors. New chemical entities were generated from known drug pharmacophoric features, followed by molecular docking and pharmacokinetic analyses, resulting in four potential AChE molecules: AChE_M1, AChE_M2, AChE_M3, and AChE_M4. The molecular docking revealed binding energies of −10.80, −11.30, −9.80, and ‐10.70 kcal/mol for AChE_M1, AChE_M2, AChE_M3, and AChE_M4, respectively, which is better than the cocrystal ligand and Donepezil. Several binding interactions were observed between the proposed molecules and the AChE protein. All molecules exhibited acceptable pharmacokinetic profiles and were nontoxic. The MDS metrics indicated stability at the AChE active site. Low‐energy basins and atomic mobility in principal component analyses of AChE bound to the final molecules confirmed their strong affinity for the protein, highlighting the molecules' potential. The final compounds may represent promising candidates for CNS‐related healthcare, subjected to validation through in vitro and in vivo studies.
Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors Atul Pawar, Hemchandra Deka, Monishka Battula, Hossam M. Aljawdah, Preeti Chunarkar Patil, Rupesh Chikhale Expert Opinion on Drug Discovery, 2025 BACKGROUND Tuberculosis is an infectious disease that has become endemic worldwide. The causative bacteria Mycobacterium tuberculosis (Mtb) is targeted via several exciting drug targets. One newly discovered target is the Fatty Acyl-CoA synthase, which plays a significant role in activating the long-chain fatty acids. RESEARCH DESIGN & METHODS This study aims to generate novel compounds using Machine Learning (ML) algorithms to inhibit this synthase. Experimentally derived bioactive compounds were chosen from ChEMBL and used as inputs for effective molecule generation by Reinvent4. The library of new molecules generated was subjected to a two-tiered molecular docking protocol, and the results were further studied to obtain a binding free energy check. RESULTS The ML-based de novo drug design (DNDD) approach successfully generated a diverse library of novel molecules targeting Fatty Acyl-CoA synthase. After rigorous molecular docking and binding free energy analysis, four new compounds were identified as potential lead candidates with promising inhibitory effects on Mtb lipid metabolism. CONCLUSIONS The study demonstrated the effectiveness of a machine-learning approach in generating novel drug candidates against Mtb. The identified hit compounds show potential as inhibitors of Fatty Acyl-CoA synthase, offering a new avenue for developing treatments for tuberculosis, particularly in combating drug-resistant strains.
Molecular simulations and machine learning methods for the identification of novel aurora A kinase inhibitors Surbhi Pravin Pawar, Mahima Sudhir Kolpe, Vikramsinh Sardarsinh Suryawanshi, Sonali Chikhale, Ammar M. Tighezza, Pritee Chunarkar Patil, Shovonlal Bhowmick Journal of Biomolecular Structure and Dynamics, 2025 Aurora A kinase (AAK) is a serine/threonine kinase that stands out as a crucial regulator of mitosis, the complex process of cell division. Notably, the protein AAK plays vital roles in cell cycle regulation and encompasses centrosome maturation, spindle assembly, and chromosome segregation. All such functionalities are essential for ensuring accurate daughter cell formation. Deregulation of AAK expression and activity has been linked to various human diseases, particularly cancer. However, AAK's significance extends beyond normal cellular function. Increased expression or activity of AAK has been implicated in the development and progression of several human cancers. AAK's critical role in cell division and its association with cancer make it a prominent drug target. Herein, series of advance computational approaches was utilized including multi-step molecular docking through AutoDock Vina and PLANTS docking to screen ChemDiv kinase-specific inhibitor library against AAK. Absolute binding energy was estimated, and finally, a molecular dynamics simulation study was conducted to screen out three hit compounds. Both docking studies revealed perfect binding of all identified ligands in active site pockets of AAK protein with similar amino acids of active sites as compared with standard BindingDB_50433632 compound and co-crystal ligand VX-680 binding mode of AAK protein. Therefore, it can be concluded that computational drug discovery approaches are meticulously implemented to identify potential AAKs inhibitors/modulators, and credential of the work was substantiated through the identification of three potential AAKs inhibitors/modulators that may hold significant promise for improving cancer management, however, need extensive biological assays or pre-clinical trials for assessing the efficacy profile of the identified compounds.
Insilico analysis of gyrase subunits A and B in prokaryotes International Journal of Pharmacy and Pharmaceutical Sciences, 2013
RECENT SCHOLAR PUBLICATIONS
Identification of potential allosteric inhibitors-modulators for the heterodimer CDC34-UBC protein-protein complex H Deka, HTM Abdelghani, A Gangopadhyay, AD Pawar, PC Patil, ... 3 Biotech 16 (5), 160 , 2026 2026
Identification of potential MenT3 inhibitors for Mycobacterium tuberculosis using the generative artificial intelligence and SilicoXplore platform IA Alsarra, VS Suryawanshi, AM Al-Mohizea, PC Patil, R Chikhale, ... Scientific Reports , 2026 2026
Identification of potential 3CLpro inhibitors-modulators for human norovirus infections through an advanced virtual screening approach S Bhowmick, TK Mistri, MK Okla, IA Saleh, A Saha, PC Patil Journal of Biomolecular Structure and Dynamics 44 (4), 1756-1772 , 2026 2026 Citations: 2
Integrated machine-learning methods for identification of Hepatitis C viral IRES sub-domain-IIa inhibitors O Shinde, PC Patil, GE Eldesoky, RV Chikhale Canadian Journal of Chemistry 104 (3), 289-302 , 2026 2026
201 Reshaping the Immune Landscape in Women, Elderly, and Diseased Individuals with Innovative Personalized Nutritional Interventions PC Patil, BN Kunchiraman Clinical Nutrition in Special Populations: Women, Elderly, Children, and Ill … , 2026 2026
Breast cancer diagnosis: an overview of cellular, molecular, and genetic testing techniques P Chunarkar-Patil, M Kaleem, MA Mujtaba, M Alhosin, B Abdurrazzaque Current Trends in Breast Cancer Pathology, Screening, Diagnosis and … , 2026 2026
Prevention strategies and public health: breast cancer prevention, including lifestyle changes, risk assessment, and early intervention M Kaleem, RA Gupta, S Mendhi, R Fule, A Kayali, W Ahmad, AA Kalanton, ... Current Trends in Breast Cancer Pathology, Screening, Diagnosis and … , 2026 2026
Identification of Phosphodiesterase 10 A Modulators for Neurodegenerative and Psychiatric Disorders: Combination of Physics-based Virtual Screening and Machine Learning Approaches VA Parekh, M Amina, ML Islam, PC Patil, MA Ali, SM Wabaidur, MA Islam Computational Biology and Chemistry, 108875 , 2026 2026 Citations: 2
Molecular simulations and machine learning methods for the identification of novel aurora A kinase inhibitors SP Pawar, MS Kolpe, VS Suryawanshi, S Chikhale, AM Tighezza, ... Journal of Biomolecular Structure and Dynamics 43 (14), 7797-7810 , 2025 2025
Force field-based de novo finds of anti-breast cancer drug via in-vitro analysis of fluoro-containing triazole-synthesized hybrid molecules M Nasibullah, AA Shah, Z Feroz, PC Patil, P Sharma, ZM Almarhoon, ... Next Research, 100684 , 2025 2025
Identification of Acetylcholinesterase Inhibitors Through a Pharmacophore‐Guided Deep Learning Approach for Therapeutic Applications in Alzheimer's Disease VS Suryawanshi, ML Islam, GE Eldesoky, PC Patil, S Bhowmick, MA Islam ChemistrySelect 10 (25), e00492 , 2025 2025 Citations: 1
Integrated Artificial Intelligence and Physics-Based Methods for the De novo Design of Spleen Tyrosine Kinase (SYK) Inhibitors AD Pawar, HTM Abdelghani, H Deka, MS Battula, S Maiti, PC Patil, ... Medicinal Chemistry 21 (6), 566-581 , 2025 2025 Citations: 2
Machine learning-integrated and fingerprint-based similarity search against immuno oncology library for identification of novel ERK2 inhibitors VS Suryawanshi, SP Pawar, MS Kolpe, HTM Abdelghani, S Chikhale, ... Structural Chemistry 36 (2), 681-700 , 2025 2025
Pharmacophore guided deep learning approach to identify novel inhibitors targeting mycobacterial polyketide synthase Pks13-TE domain R Choudhary, S Bhowmick, HTM Abdelghani, PC Patil, RV Chikhale Journal of Molecular Structure 1319, 139360 , 2025 2025 Citations: 5
Integrated machine learning and physics-based methods assisted de novo design of Fatty Acyl-CoA synthase inhibitors A Pawar, H Deka, M Battula, HM Aljawdah, PC Patil, R Chikhale Expert Opinion on Drug Discovery 20 (1), 123-135 , 2025 2025 Citations: 2
Identification of Aurora A kinase allosteric inhibitors: a comprehensive virtual screening through fingerprint-based similarity search, molecular docking, machine learning and … MS Kolpe, SP Pawar, VS Suryawanshi, HTM Abdelghani, PC Patil, ... Journal of Molecular Liquids 414, 126115 , 2024 2024 Citations: 2
Investigating the Ribosomal‐RNA: Protein Interactions and AI‐Assisted Discovery of Novel Inhibitors M Battula, S Bhowmick, PC Patil, GE Eldesoky, RV Chikhale ChemistrySelect 9 (43), e202403459 , 2024 2024
Identification of Potent CHK2 Inhibitors‐Modulators for Therapeutic Application in Cancer: A Machine Learning Integrated Fragment‐Based Drug Design Approach M Sudhir Kolpe, V Sardarsinh Suryawanshi, GE Eldesoky, D Hossain, ... ChemistrySelect 9 (39), e202403302 , 2024 2024 Citations: 1
Identification of Peregrin inhibitors-modulators by harnessing the computational prowess of molecular simulation and machine learning algorithms H Deka, AD Pawar, MS Battula, GE Eldesoky, OD Shinde, PC Patil, ... Journal of Molecular Liquids 411, 125782 , 2024 2024
Species annotation using a k-mer based KNN model S Srushti, K Prathamesh, CP Pritee Bioinformation 20 (9), 986-989 , 2024 2024
MOST CITED SCHOLAR PUBLICATIONS
Anticancer drug discovery based on natural products: from computational approaches to clinical studies P Chunarkar-Patil, M Kaleem, R Mishra, S Ray, A Ahmad, D Verma, ... Biomedicines 12 (1), 201 , 2024 2024 Citations: 403
Computational screening of promising beta-secretase 1 inhibitors through multi-step molecular docking and molecular dynamics simulations-Pharmacoinformatics approach S Gupta, D Parihar, M Shah, S Yadav, H Managori, S Bhowmick, PC Patil, ... Journal of Molecular Structure 1205, 127660 , 2020 2020 Citations: 40
Multi-step molecular docking and dynamics simulation-based screening of large antiviral specific chemical libraries for identification of Nipah virus glycoprotein inhibitors MS Kalbhor, S Bhowmick, AM Alanazi, PC Patil, MA Islam Biophysical Chemistry 270, 106537 , 2021 2021 Citations: 32
Big data analytics P Chunarkar-Patil, A Bhosale Open Access J Sci 2 (5), 326-335 , 2018 2018 Citations: 28
Pharmacoinformatics-based identification of anti-bacterial catalase-peroxidase enzyme inhibitors CS Jangam, S Bhowmick, RD Chorge, LD Bharatrao, PC Patil, ... Computational Biology and Chemistry 83, 107136 , 2019 2019 Citations: 21
Pharmacoinformatics approach based identification of potential Nsp15 endoribonuclease modulators for SARS-CoV-2 inhibition RU Savale, S Bhowmick, SM Osman, FA Alasmary, TM Almutairi, ... Archives of biochemistry and biophysics 700, 108771 , 2021 2021 Citations: 20
In silico identification of small molecule modulators for disruption of Hsp90–Cdc37 protein–protein interaction interface for cancer therapeutic application PP Dike, S Bhowmick, GE Eldesoky, SM Wabaidur, PC Patil, MA Islam Journal of Biomolecular Structure and Dynamics 40 (5), 2082-2098 , 2022 2022 Citations: 18
Identification of novel hit molecules targeting M. tuberculosis polyketide synthase 13 by combining generative AI and physics-based methods RV Chikhale, R Choudhary, J Malhotra, GE Eldesoky, P Mangal, PC Patil Computers in Biology and Medicine 176, 108573 , 2024 2024 Citations: 17
Identification of Mycobacterium Tuberculosis Transcriptional Repressor EthR Inhibitors: Shape Based Search and Machine Learning Studies RV Chikhale, GE Eldesoky, MS Kolpe, VS Suryawanshi, PC Patil, ... Heliyon 10 ((5) e26802) , 2024 2024 Citations: 14
De novo design based identification of potential HIV-1 integrase inhibitors: a pharmacoinformatics study PB Shinde, S Bhowmick, E Alfantoukh, PC Patil, SM Wabaidur, ... Computational biology and chemistry 88, 107319 , 2020 2020 Citations: 11
Identification of mycobacterial Thymidylate kinase inhibitors: a comprehensive pharmacophore, machine learning, molecular docking, and molecular dynamics simulation studies RV Chikhale, SP Pawar, MS Kolpe, OD Shinde, KA Dahlous, ... Molecular Diversity 28 (4), 1947-1964 , 2024 2024 Citations: 10
Identification of bio-active food compounds as potential SARS-CoV-2 PLpro inhibitors-modulators via negative image-based screening and computational simulations S Bhowmick, NA AlFaris, JZ ALTamimi, ZA ALOthman, PC Patil, ... Computers in Biology and Medicine 145, 105474 , 2022 2022 Citations: 9
Machine learning assisted methods for the identification of low toxicity inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA) RV Chikhale, HTM Abdelghani, H Deka, AD Pawar, PC Patil, S Bhowmick Computational Biology and Chemistry 110, 108034 , 2024 2024 Citations: 8
Structure-based screening of DNA gyraseb inhibitors for therapeutic applications in tuberculosis: a pharmacoinformatics study PM Tambe, S Bhowmick, SK Chaudhary, MR Khan, SM Wabaidur, ... Applied biochemistry and biotechnology 192 (4), 1107-1123 , 2020 2020 Citations: 8
Identification of potential cruzain inhibitors using de novo design, molecular docking and dynamics simulations studies S Bhowmick, RD Chorge, CS Jangam, LD Bharatrao, PC Patil, ... Journal of Biomolecular Structure and Dynamics 38 (13), 4005-4015 , 2020 2020 Citations: 7
Hepatitis C Virus (HCV) and the Role of Phytochemicals in the Antiviral Effects of Different Medicinal Plants Against Infection AS Moghe, MM Deshpande, SS Kamyab, P Chunarkar-Patil, SS Nandi, ... Anti-Viral Metabolites from Medicinal Plants, 1-31 , 2023 2023 Citations: 6
Pharmacophore guided deep learning approach to identify novel inhibitors targeting mycobacterial polyketide synthase Pks13-TE domain R Choudhary, S Bhowmick, HTM Abdelghani, PC Patil, RV Chikhale Journal of Molecular Structure 1319, 139360 , 2025 2025 Citations: 5
An analysis of non-cultivable bacteria using WEKA PC Patil, PS Panchal, S Madiwale, VS Tale Bioinformation 16 (8), 620 , 2020 2020 Citations: 5
Virtual reality in bioinformatics T Batra, P Chunarkar-Patil Open Access J Sci 3 (2), 63-70 , 2019 2019 Citations: 5
Identification of structural requirements of estrogen receptor modulators using pharmacoinformatics techniques for application to estrogen therapy TSP Md. Ataul Islam, Darshakkumar Ashokbhai Patel, Savansinh Ghanshyamsinh ... Medicinal Chemistry Research , 2016 2016 Citations: 4
