Dr. Mushtaq Ahmad Wani

His research work focused on computational modelling for the design and optimization of novel small molecules as potential next-generation inhibitors of mycobacterium cytochrome bc1-aa3 super complex. His research interests include the development of machine learning models, artificial intelligence, molecular modelling, and Pharmacophore mapping.

EDUCATION

(2017-2023) Doctor of Philosophy (Pharmacoinformatics) at National Institute of Pharmaceutical Education & Research, Kolkata, West Bengal, India
(2015-2017) Masters of pharmacy (Pharmacoinformatics) from National Institute of Pharmaceutical Education and Research, S.A.S Nagar, Punjab, India
(2010-2015) Bachelor of Pharmacy from University of Kashmir: Srinagar, Jammu, and Kashmir, India

RESEARCH, TEACHING, or OTHER INTERESTS

Pharmacy, Molecular Biology, Multidisciplinary, Multidisciplinary

Scopus Publications

An integrated machine learning and computational framework with experimental validation for the identification of novel CXCR4 inhibitors
Mushtaq Ahmad Wani, Pooja Kumari, Faisal Irshad, Yashi Gupta, Monika Gupta, Anindya Goswami, Zabeer Ahmed, Amit Nargotra
European Journal of Medicinal Chemistry, 2026
Integrating in silico and in vitro approaches for identifying potential TNF-α inhibitors
Manas Ranjan Barik, Mushtaq Ahmad Wani, Sweta, Gurleen Kour, Zabeer Ahmed, Amit Nargotra
International Journal of Biological Macromolecules, 2025
Targeting tryparedoxin-dependent peroxidase (TXNPx) enzyme to identify repurposing drug candidates from FDA-approved drugs and natural products using virtual screening, ADME/Tox and MD simulations
Eman Shorog, Sabina Yasmin, Rani Mansuri, Arpit Raj, Mohammad Ovais Dar, Sumel Ashique, Qazi Mohammad Sajid Jamal, Ali H. Alharbi, Mushtaq Ahmad Wani, Mohammad Yousuf Ansari
Biochemistry and Biophysics Reports, 2025
Protozoan are parasitic organisms that can cause significant diseases worldwide, such as Chagas disease, African sleeping sickness, and Leishmaniasis. In this study, we performed docking studies on type II tryparedoxin-dependent peroxidase (PDB ID: 2VUP) using a Zinc database having natural products library and FDA-approved drugs. The top compounds identified are F1762–0560, F1855-0030, FDA_339, FDA_461, tetrahydrobenzo-tetraphenoxirene, and ketoconazole. These compounds were further performed the molecular dynamics simulations studies. The docking results has suggested that top Docking Scores compounds are F17620560 (−8.6 kcal/mol), F1855-0030(−7.8 kcal/mol), FDA_339 (−7.0 kcal/mol), FDA_461 (−7.0 kcal/mol), tetrahydrobenzo-tetraphenoxirene (−7.5 kcal/mol) and, ketoconazole (−6.7 kcal/mol). The common binding affinity amino acids are SER 37, LYS 38, CYS 39, LYS 43, GLU 81 and PHE 85. The top scoring compounds (F1762-0560) has showed interactions with the target protein through hydrogen bonding and stacking interactions, particularly with SER 37, CYS 39, and LYS 43. We further investigated the stability of six ligand-TXNPx complexes over 200 ns. The results indicated good structural stability (RMSD: 0.05–0.20 nm; Rg: 1.45–1.52 nm), with F0556–0242 and F1762-0560 showing the least fluctuations. FDA_461 has the most hydrogen bonds (up to 5), while Ketoconazole was more flexible (RMSD peak: 0.25 nm). These findings suggest that F0556–0242, F1762-0560, and FDA_461 are promising candidates. • Molecular docking identified F1762-0560 as the top compound with the highest docking score (−8.6 kcal/mol) 2VUP • Common interacting amino acid residues across top ligands include SER37, LYS38, CYS39, LYS43, GLU81, and PHE85. • Compound FDA_461 demonstrated the highest QED percentile among the top candidates from both the libraries. • MD simulations revealed that FDA_461 formed the most hydrogen bonds (up to 5) and, along with F1762–0560 and F0556-0242.
Machine learning framework coupled with CADD for predicting sphingosine kinase 1 inhibitors
Mushtaq Ahmad Wani, Pooja Kumari, Amit Nargotra
Computers in Biology and Medicine, 2025
Computer-aided drug design approaches for the identification of potent inhibitors targeting elongation factor G of Mycobacterium tuberculosis
Mushtaq Ahmad Wani, Aritra Banerjee, Prabha Garg
Journal of Molecular Graphics and Modelling, 2025
Discovery of novel hybrid tryptamine-rivastigmine molecules as potent AChE and BChE inhibitors exhibiting multifunctional properties for the management of Alzheimer's disease
Gauri Shankar, Prabhat Kumar, Sanskriti Rai, Aparajita Ghosh, Tanmaykumar Varma, Mushtaq Ahmad Wani, Sunil Kumar, Upesh Mandloi, Gireesh Kumar Singh, Prabha Garg, Onkar Kulkarni, Saripella Srikrishna, Saroj Kumar, Gyan Modi
European Journal of Medicinal Chemistry, 2025
Predictive Modeling and Drug Repurposing for Type-II Diabetes
Nitin Wankhade, Anju Sharma, Mushtaq Ahmad Wani, Aritra Banerjee, Prabha Garg
ACS Medicinal Chemistry Letters, 2024
Diabetes mellitus (DM) is a global health concern, and dipeptidyl peptidase-4 (DPP-4) is a key therapeutic target. The study used three machine learning and deep learning models to predict potential DPP-4 inhibitors using a curated data set of 6,750 compounds. The models included support vector machine (SVM), random forest (RF), naive Bayes (NB), and multitask deep neural network (MTDNN). The MTDNN model demonstrated strong predictive performance, achieving 98.62% train accuracy and 98.42% test accuracy for predicting DPP-4 inhibitors and a correlation coefficient of 0.979 for training and 0.977 for the test data set, with low training and test errors while predicting corresponding IC50 values. The MTDNN model predicted potential inhibitors using an external data set of FDA-approved drugs, identifying 100 compounds. Among these, five compounds stood out with promising molecular docking and dynamic profiles, suggesting their potential as repurposed drugs for targeting DPP-4 and offering hope for the future of diabetes treatment.
Unlocking translational machinery for antitubercular drug development
Navneet Kumar, Mushtaq Ahmad Wani, Chaaya Iyengar Raje, Prabha Garg
Trends in Biochemical Sciences, 2024
Targeting translational factor proteins (TFPs) presents significant promise for the development of innovative antitubercular drugs. Previous insights from antibiotic binding mechanisms and recently solved 3D crystal structures of Mycobacterium tuberculosis (Mtb) elongation factor thermo unstable-GDP (EF-Tu-GDP), elongation factor thermo stable-EF-Tu (EF-Ts-EF-Tu), and elongation factor G-GDP (EF-G-GDP) have opened up new avenues for the design and development of potent antituberculosis (anti-TB) therapies.
An update on ATP synthase inhibitors: A unique target for drug development in M. tuberculosis
Lakshmi Mounika Kelam, Mushtaq Ahmad Wani, Devendra K. Dhaked
Progress in Biophysics and Molecular Biology, 2023
Targeting the cytochrome bc1 complex for drug development in M. tuberculosis: review
Mushtaq Ahmad Wani, Devendra Kumar Dhaked
Molecular Diversity, 2022
The terminal oxidases of the oxidative phosphorylation pathway play a significant role in the survival and growth of M. tuberculosis, targeting these components lead to inhibition of M. tuberculosis. Many drug candidates targeting various components of the electron transport chain in M. tuberculosis have recently been discovered. The cytochrome bc1-aa3 supercomplex is one of the most important components of the electron transport chain in M. tuberculosis, and it has emerged as the novel target for several promising candidates. There are two cryo-electron microscopy structures (PDB IDs: 6ADQ and 6HWH) of the cytochrome bc1-aa3 supercomplex that aid in the development of effective and potent inhibitors for M. tuberculosis. In recent years, a number of potential candidates targeting the QcrB subunit of the cytochrome bc1 complex have been developed. In this review, we describe the recently identified inhibitors that target the electron transport chain's terminal oxidase enzyme in M. tuberculosis, specifically the QcrB subunit of the cytochrome bc1 complex.
Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents
Mushtaq Ahmad Wani, Kuldeep K. Roy
Molecular Diversity, 2022
Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides
Mushtaq Ahmad Wani, Prabha Garg, Kuldeep K. Roy
Medical and Biological Engineering and Computing, 2021
Effect of environmental toxicants on neuronal functions
Supriya Sharma, Sharad Wakode, Anjali Sharma, Nisha Nair, Mahaveer Dhobi, Mushtaq Ahmad Wani, Faheem Hyder Pottoo
Environmental Science and Pollution Research, 2020
Novel Pyrimidinone Derivatives Show Anticancer Activity and Induce Apoptosis: Synthesis, SAR and Putative Binding Mode
Ashis Roy, Mrinalkanti Kundu, Pranab Dhar, Arnish Chakraborty, Soumen Mukherjee, Jayatri Naskar, Chhanda Rarhi, Rajib Barik, Susanta Kumar Mondal, Mushtaq Ahmad Wani, Rahul Gajbhiye, Kuldeep K. Roy, Arup Maiti, Priyadarshi Manna, Susanta Adhikari
Chemistryselect, 2020
Emerging opportunities of exploiting mycobacterial electron transport chain pathway for drug-resistant tuberculosis drug discovery
Kuldeep K. Roy, Mushtaq Ahmad Wani
Expert Opinion on Drug Discovery, 2020
Denouement of chemicals on amyotrophic lateral sclerosis: Is green chemistry the answer
Faizana Fayaz, Faheem H. Pottoo, Sadat Shafi, Mushtaq A. Wani, Sharad Wakode, Anjali Sharma
Medicinal Chemistry, 2020

RECENT SCHOLAR PUBLICATIONS

An integrated machine learning and computational framework with experimental validation for the identification of novel CXCR4 inhibitors
MA Wani, P Kumari, F Irshad, Y Gupta, M Gupta, A Goswami, Z Ahmed, ...
European Journal of Medicinal Chemistry, 118918 , 2026
2026
Targeting tryparedoxin-dependent peroxidase (TXNPx) enzyme to identify repurposing drug candidates from FDA-approved drugs and natural products using virtual screening, ADME …
E Shorog, S Yasmin, R Mansuri, A Raj, MO Dar, S Ashique, QMS Jamal, ...
Biochemistry and Biophysics Reports 43, 102096 , 2025
2025
Citations: 1
Integrating in silico and in vitro approaches for identifying potential TNF-α inhibitors
MR Barik, MA Wani, G Kour, Z Ahmed, A Nargotra
International Journal of Biological Macromolecules, 146634 , 2025
2025
Citations: 1
Machine learning framework coupled with CADD for predicting sphingosine kinase 1 inhibitors
MA Wani, P Kumari, A Nargotra
Computers in Biology and Medicine 194, 110448 , 2025
2025
Citations: 1
Drugs repurposed against morphine and heroin dependence: molecular docking, DFT, MM-GBSA-based MD simulation studies
JA Malik, MA Wani, MO Dar, P Garg, JN Agrewala
In Silico Pharmacology , 2025
2025
Citations: 3
Discovery of novel hybrid tryptamine-rivastigmine molecules as potent AChE and BChE inhibitors exhibiting multifunctional properties for the management of Alzheimer's disease
G Shankar, P Kumar, S Rai, A Ghosh, T Varma, MA Wani, S Kumar, ...
European Journal of Medicinal Chemistry 283, 117066 , 2025
2025
Citations: 19
Computer-Aided Drug Design Approaches for the Identification of Potent Inhibitors Targeting Elongation Factor G of Mycobacterium tuberculosis
MA Wani, A Banerjee, P Garg
Journal of Molecular Graphics and Modelling 136, 108954 , 2025
2025
Citations: 9
Predictive modeling and drug repurposing for type-II diabetes
N Wankhade, A Sharma, MA Wani, A Banerjee, P Garg
ACS Medicinal Chemistry Letters 15 (11), 1907-1917 , 2024
2024
Citations: 5
Unlocking translational machinery for antitubercular drug development
N Kumar, MA Wani, CI Raje, P Garg
Trends in Biochemical Sciences 49 (3), 195-198. , 2024
2024
Citations: 3
An update on ATP synthase inhibitors: a unique target for drug development in M. tuberculosis
LM Kelam, MA Wani, DK Dhaked
Progress in Biophysics and Molecular Biology 180, 87-104 , 2023
2023
Citations: 16
Targeting the cytochrome bc 1 complex for drug development in M. tuberculosis
MA Wani, DK Dhaked
Molecular Diversity 26 (5), 2949-2965 , 2022
2022
Citations: 12
Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents
MA Wani, KK Roy
Molecular Diversity 26 (3), 1345-1356 , 2022
2022
Citations: 21
Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides
MA Wani, P Garg, KK Roy
Medical & Biological Engineering & Computing 59 (11), 2397-2408 , 2021
2021
Citations: 39
Denouement of chemicals on amyotrophic lateral sclerosis: is green chemistry the answer
F Fayaz, FH Pottoo, S Shafi, MA Wani, S Wakode, A Sharma
Medicinal Chemistry 16 (8), 1058-1068 , 2020
2020
Citations: 5
Effect of environmental toxicants on neuronal functions
S Sharma, S Wakode, A Sharma, N Nair, M Dhobi, MA Wani, FH Pottoo
Environmental Science and Pollution Research 27 (36), 44906-44921 , 2020
2020
Citations: 71
Novel pyrimidinone derivatives show anticancer activity and induce apoptosis: Synthesis, SAR and putative binding mode
A Roy, M Kundu, P Dhar, A Chakraborty, S Mukherjee, J Naskar, C Rarhi, ...
ChemistrySelect 5 (15), 4559-4566 , 2020
2020
Citations: 9
Using Cryo-EM Structure of the Mycobacterial Cytochrome bcc-aa3 Supercomplex to Decrypt the Binding Mechanism of its Substrate (s) and Known Inhibitors
MA Wani, KK Roy
Available at SSRN 3530856 , 2020
2020
Emerging opportunities of exploiting mycobacterial electron transport chain pathway for drug-resistant tuberculosis drug discovery
KK Roy, MA Wani
Expert opinion on drug discovery 15 (2), 231-241 , 2020
2020
Citations: 27

MOST CITED SCHOLAR PUBLICATIONS

Effect of environmental toxicants on neuronal functions
S Sharma, S Wakode, A Sharma, N Nair, M Dhobi, MA Wani, FH Pottoo
Environmental Science and Pollution Research 27 (36), 44906-44921 , 2020
2020
Citations: 71
Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides
MA Wani, P Garg, KK Roy
Medical & Biological Engineering & Computing 59 (11), 2397-2408 , 2021
2021
Citations: 39
Emerging opportunities of exploiting mycobacterial electron transport chain pathway for drug-resistant tuberculosis drug discovery
KK Roy, MA Wani
Expert opinion on drug discovery 15 (2), 231-241 , 2020
2020
Citations: 27
Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents
MA Wani, KK Roy
Molecular Diversity 26 (3), 1345-1356 , 2022
2022
Citations: 21
Discovery of novel hybrid tryptamine-rivastigmine molecules as potent AChE and BChE inhibitors exhibiting multifunctional properties for the management of Alzheimer's disease
G Shankar, P Kumar, S Rai, A Ghosh, T Varma, MA Wani, S Kumar, ...
European Journal of Medicinal Chemistry 283, 117066 , 2025
2025
Citations: 19
An update on ATP synthase inhibitors: a unique target for drug development in M. tuberculosis
LM Kelam, MA Wani, DK Dhaked
Progress in Biophysics and Molecular Biology 180, 87-104 , 2023
2023
Citations: 16
Targeting the cytochrome bc 1 complex for drug development in M. tuberculosis
MA Wani, DK Dhaked
Molecular Diversity 26 (5), 2949-2965 , 2022
2022
Citations: 12
Computer-Aided Drug Design Approaches for the Identification of Potent Inhibitors Targeting Elongation Factor G of Mycobacterium tuberculosis
MA Wani, A Banerjee, P Garg
Journal of Molecular Graphics and Modelling 136, 108954 , 2025
2025
Citations: 9
Novel pyrimidinone derivatives show anticancer activity and induce apoptosis: Synthesis, SAR and putative binding mode
A Roy, M Kundu, P Dhar, A Chakraborty, S Mukherjee, J Naskar, C Rarhi, ...
ChemistrySelect 5 (15), 4559-4566 , 2020
2020
Citations: 9
Predictive modeling and drug repurposing for type-II diabetes
N Wankhade, A Sharma, MA Wani, A Banerjee, P Garg
ACS Medicinal Chemistry Letters 15 (11), 1907-1917 , 2024
2024
Citations: 5
Denouement of chemicals on amyotrophic lateral sclerosis: is green chemistry the answer
F Fayaz, FH Pottoo, S Shafi, MA Wani, S Wakode, A Sharma
Medicinal Chemistry 16 (8), 1058-1068 , 2020
2020
Citations: 5
Drugs repurposed against morphine and heroin dependence: molecular docking, DFT, MM-GBSA-based MD simulation studies
JA Malik, MA Wani, MO Dar, P Garg, JN Agrewala
In Silico Pharmacology , 2025
2025
Citations: 3
Unlocking translational machinery for antitubercular drug development
N Kumar, MA Wani, CI Raje, P Garg
Trends in Biochemical Sciences 49 (3), 195-198. , 2024
2024
Citations: 3
Targeting tryparedoxin-dependent peroxidase (TXNPx) enzyme to identify repurposing drug candidates from FDA-approved drugs and natural products using virtual screening, ADME …
E Shorog, S Yasmin, R Mansuri, A Raj, MO Dar, S Ashique, QMS Jamal, ...
Biochemistry and Biophysics Reports 43, 102096 , 2025
2025
Citations: 1
Integrating in silico and in vitro approaches for identifying potential TNF-α inhibitors
MR Barik, MA Wani, G Kour, Z Ahmed, A Nargotra
International Journal of Biological Macromolecules, 146634 , 2025
2025
Citations: 1
Machine learning framework coupled with CADD for predicting sphingosine kinase 1 inhibitors
MA Wani, P Kumari, A Nargotra
Computers in Biology and Medicine 194, 110448 , 2025
2025
Citations: 1
An integrated machine learning and computational framework with experimental validation for the identification of novel CXCR4 inhibitors
MA Wani, P Kumari, F Irshad, Y Gupta, M Gupta, A Goswami, Z Ahmed, ...
European Journal of Medicinal Chemistry, 118918 , 2026
2026
Using Cryo-EM Structure of the Mycobacterial Cytochrome bcc-aa3 Supercomplex to Decrypt the Binding Mechanism of its Substrate (s) and Known Inhibitors
MA Wani, KK Roy
Available at SSRN 3530856 , 2020
2020