All Publications

  1. Dissecting Errors in Machine Learning for Retrosynthesis: A Granular Metric Framework and Transformer-Based Model for More Informative Predictions. Tadanki, A. S., Rao, H. S. P., & Priyakumar, U. D. (2024). chemrxiv
  2. TorRNA-Improved Prediction of Backbone Torsion Angles of RNA by Leveraging Large Language Models. Devata, S., & Priyakumar, U.D. (2024). chemrxiv
  3. Generative artificial intelligence for small molecule drug design. Kanakala, G. C., Devata, S., Chatterjee, P., & Priyakumar, U. D. (2024). Current Opinion in Biotechnology, 89, 103175.
  4. GraphDDI: Graph Neural Network for Prediction of Drug-Drug Interaction. Gupta, S., Laghuvarapu, S., & Priyakumar, U. D. (2024, August). International Conference on AI in Healthcare (pp. 17-30), Springer Nature Switzerland.
  5. Self-Supervised Modality-Agnostic Pre-Training of Swin Transformers. Talasila, A., Maity, M., & Priyakumar, U. D. (2024, May). IEEE International Symposium on Biomedical Imaging (ISBI) (pp. 1-5).
  6. Deep reinforcement learning in chemistry: A review. Sridharan, B., Sinha, A., Bardhan, J., Modee, R., Ehara, M., & Priyakumar, U. D. (2024). Journal of Computational Chemistry.
  7. Plas-20k: Extended dataset of protein-ligand affinities from md simulations for machine learning applications. Korlepara, D. B., CS, V., Srivastava, R., Pal, P. K., Raza, S. H., Kumar, V., ... & Priyakumar, U. D. (2024). Scientific Data, 11, 180.
  8. Spectra to Structure: Contrastive Learning Framework for Library Ranking and Generating Molecular Structures for Infrared Spectra. Kanakala, G. C., Sridharan, B., & Priyakumar, U. D. (2024). Digital Discovery.
  9. Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): an open-source disease-specific drug discovery portal. Priyadarsinee, L., Jamir, E., Nagamani, S., Mahanta, H. J., Kumar, N., John, L., Sarma,H., Kumar, A., Gaur, A.S., Sahoo, R., Vaikundamani, S., Murugan A.N., Priyakumar, U.D., ...... & Sastry, G. N. (2024). GigaByte, 2024.
  10. DeepSPInN–deep reinforcement learning for molecular structure prediction from infrared and 13 C NMR spectra. Devata, S., Sridharan, B., Mehta, S., Pathak, Y., Laghuvarapu, S., Varma, G., & Priyakumar, U. D. (2024). Digital Discovery, 3, 818-829.
  11. Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines. Viswanathan, K., Goel, M., Laghuvarapu, S., Varma, G., & Priyakumar, U. D. (2023). Scientific Reports, 13, 21069.
  12. MolOpt: Autonomous Molecular Geometry Optimization Using Multiagent Reinforcement Learning. Modee, R., Mehta, S., Laghuvarapu, S., & Priyakumar, U. D. (2023). The Journal of Physical Chemistry B, 127, 10295-10303.
  13.  A Machine Learning Approach for Outcome Prediction in Postanoxic Coma Patients Using Frequency Domain Features. Venkataramani, V. V., Garg, A., Maity, M., & Priyakumar, U. D. (2023, October). Computing in Cardiology (CinC) (Vol. 50, pp. 1-4). IEEE.
  14. Self-Supervision and Weak Supervision for Accurate and Interpretable Chest X-Ray Classification Models. Talasila, A., Karthikeyan, A., Alle, S., Maity, M., & Priyakumar, U. D. (2023, June). International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
  15. Single-Lead to Multi-Lead Electrocardiogram Reconstruction Using a Modified Attention U-Net Framework. Garg, A., Venkataramani, V. V., & Priyakumar, U. D. (2023, June). International Joint Conference on Neural Networks (IJCNN) IEEE, (pp. 1-8).
  16. MeGen-generation of gallium metal clusters using reinforcement learning. Modee, R., Verma, A., Joshi, K., & Priyakumar, U. D. (2023). Machine Learning: Science and Technology, 4, 025032.
  17. Latent biases in machine learning models for predicting binding affinities using popular data sets. Kanakala, G. C., Aggarwal, R., Nayar, D., & Priyakumar, U. D. (2023). ACS omega, 8, 2389-2397.
  18. Carbodicarbenes and striking redox transitions of their conjugate acids: influence of NHC versus CAAC as donor substituents. Dolai, R., Kumar, R., Elvers, B. J., Pal, P. K., Joseph, B., Sikari, R., Nayak, M.K., Maity, A., Singh, T., Chrysochos, N., Jayaraman, A., Krummenacher, I., Mondal, J., Priyakumar, U. D., ... & Jana, A. (2023). Chemistry–A European Journal, 29, e202202888.
  19. PREHOST: Host prediction of coronaviridae family using machine learning. Chaturvedi, A., Borkar, K., Priyakumar, U. D., & Vinod. P. (2023). Heliyon, 9.
  20. Efficient and enhanced sampling of drug-like chemical space for virtual screening and molecular design using modern machine learning methods. Goel, M., Aggarwal, R., Sridharan, B., Pal, P. K., & Priyakumar, U. D. (2023). Wiley Interdisciplinary Reviews: Computational Molecular Science, 13, e1637.
  21. Staufen‐2 functions as a cofactor for enhanced Rev‐mediated nucleocytoplasmic trafficking of HIV‐1 genomic RNA via the CRM1 pathway. Balakrishnan, K., Munusami, P., Mohareer, K., Priyakumar, U. D., Banerjee, A., Luedde, T., ... & Banerjee, S. (2022). The FEBS Journal, 289, 6731-6751.
  22. Tetra-coordinated boron-functionalized phenanthroimidazole-based zinc salen as a photocatalyst for the cycloaddition of CO2 and epoxides. Nayak, P., Murali, A. C., Pal, P. K., Priyakumar, U. D., Chandrasekhar, V., & Venkatasubbaiah, K. (2022). Inorganic Chemistry, 61, 14511-14516.
  23. Plas-5k: Dataset of protein-ligand affinities from molecular dynamics for machine learning applications. Korlepara, D. B., Vasavi, C. S., Jeurkar, S., Pal, P. K., Roy, S., Mehta, S., ... & Priyakumar, U. D. (2022). Scientific data, 9, 548.
  24. Deep reinforcement learning for molecular inverse problem of nuclear magnetic resonance spectra to molecular structure. Sridharan, B., Mehta, S., Pathak, Y., & Priyakumar, U. D. (2022). The Journal of Physical Chemistry Letters, 13, 4924-4933.
  25. Birds-binding residue detection from protein sequences using deep resnets. Chelur, V. R., & Priyakumar, U. D. (2022). Journal of Chemical Information and Modeling, 62, 1809-1818.
  26. Synthesis of α‐Aryl Ketones by Harnessing the Non‐Innocence of Toluene and its Derivatives: Enhancing the Acidity of Methyl Arenes by a Brønsted Base and their Mechanistic Aspects. Sreedharan, R., Pal, P. K., Panyam, P. K. R., Priyakumar, U. D., & Gandhi, T. (2022). Asian Journal of Organic Chemistry, 11, e202200372.
  27. COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits. Alle, S., Kanakan, A., Siddiqui, S., Garg, A., Karthikeyan, A., Mehta, P., ... & Priyakumar, U. D. (2022). PLoS One, 17, e0264785.
  28. Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods. Choudhury, C., Murugan, N. A., & Priyakumar, U. D. (2022). Drug discovery today, 27, 1847-1861.
  29. Artificial intelligence: machine learning for chemical sciences. Karthikeyan, A., & Priyakumar, U. D. (2022). Journal of Chemical Sciences, 134, 1-20.
  30. Benchmark study on deep neural network potentials for small organic molecules. Modee R, Laghuvarapu S, Priyakumar UD. (2022). J Comput Chem, 43, 308-318.
  31. Modern AI/ML methods for healthcare: Opportunities and challenges.  Garg, A., Venkataramani, V. V., Karthikeyan, A., & Priyakumar, U. D. (2022, January). International Conference on Distributed Computing and Internet Technology (pp. 3-25). Cham: Springer International Publishing.
  32. Modified Variable Kernel Length ResNets for Heart Murmur Detection and Clinical Outcome Prediction using Multi-positional Phonocardiogram Recording. Venkataramani, V. V., Garg, A., & Priyakumar, U. D. (2022). 2022 Computing in Cardiology (CinC) (Vol. 49, pp. 1-4). IEEE.
  33. Mo-memes: A method for accelerating virtual screening using multi-objective bayesian optimization. Mehta, S., Goel, M., & Priyakumar, U. D. (2022). Frontiers in Medicine, 9, 916481.
  34. Modern machine learning for tackling inverse problems in chemistry: molecular design to realization. Sridharan, B., Goel, M., & Priyakumar, U. D. (2022). Chemical Communications, 58, 5316-5331.
  35. Molecular representations for machine learning applications in chemistry. Raghunathan, S., & Priyakumar, U. D. (2022). International Journal of Quantum Chemistry, 122, e26870.
  36. Mining subgraph coverage patterns from graph transactions. Reddy, A. S., Reddy, P. K., Mondal, A., & Priyakumar, U. D. (2022). International Journal of Data Science and Analytics, 1-17.
  37. DeepPocket: ligand binding site detection and segmentation using 3D convolutional neural networks. Aggarwal, R., Gupta, A., Chelur, V., Jawahar, C. V., & Priyakumar, U. D. (2021). Journal of Chemical Information and Modeling, 62, 5069-5079.
  38. MolGPT: molecular generation using a transformer-decoder model. Bagal, V., Aggarwal, R., Vinod, P. K., & Priyakumar, U. D. (2021). Journal of Chemical Information and Modeling, 62, 2064-2076.
  39. A Model of Graph Transactional Coverage Patterns with Applications to Drug Discovery. Reddy, A. S., Reddy, P. K., Mondal, A., & Priyakumar, U. D. (2021, December). 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC) (pp. 21-30). IEEE.
  40. MoleGuLAR: molecule generation using reinforcement learning with alternating rewards. Goel, M., Raghunathan, S., Laghuvarapu, S., & Priyakumar, U. D. (2021). Journal of Chemical Information and Modeling, 61, 5815-5826.
  41. Desolvation of peptide bond by O to S substitution impacts protein stability. Khatri, B., Raghunathan, S., Chakraborti, S., Rahisuddin, R., Kumaran, S., Tadala, R., Wagh, P., Priyakumar, U. D., & Chatterjee, J. (2021). Angewandte Chemie International Edition, 60, 24870-24874.
  42. Imle-net: An interpretable multi-level multi-channel model for ecg classification. Reddy, L., Talwar, V., Alle, S., Bapi, R. S., & Priyakumar, U. D. (2021, October). 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 1068-1074). IEEE.
  43. Stereomutation in Tetracoordinate Centers via Stabilization of Planar Tetracoordinated Systems. Yadav, K., Lourderaj, U., & Priyakumar, U. D. (2021). Atoms, 9, 79.
  44. SCONES: self-consistent neural network for protein stability prediction upon mutation. Samaga, Y. B., Raghunathan, S., & Priyakumar, U. D. (2021). The Journal of Physical Chemistry B, 125, 10657-10671.
  45. Clinico-genomic analysis reveals mutations associated with COVID-19 disease severity: possible modulation by RNA structure. Mehta, P., Alle, S., Chaturvedi, A., Swaminathan, A., Saifi, S., Maurya, R., Chattopadhyay, P., Devi, P., Chauhan, R., Kanakan, A., Vasudevan, J.S., Sethuraman, R., Chidambaram, S., Srivastava, M., Chakravarthi, A., Jacob, J., Namagiri, M., Konala, V., Jha, S., Priyakumar, U. D., Vinod, P. K., & Pandey, R. (2021). Pathogens, 10, 1109.
  46. Apobind: a dataset of ligand unbound protein conformations for machine learning applications in de novo drug design. Aggarwal, R., Gupta, A., & Priyakumar, U. D., (2021). ICML workshop in Computational Biology 2021.
  47. Ion Selectivity and Permeation Mechanism in a Cyclodextrin-Based Channel. Musunuru, P., Padhi, S., & Priyakumar, U. D. (2021). The Journal of Physical Chemistry B, 125, 8028-8037.
  48. Synthesis and reactivity of NHC-coordinated phosphinidene oxide. Dhara, D., Pal, P. K., Dolai, R., Chrysochos, N., Rawat, H., Elvers, B. J., Krummenacher, I., Braunschweig, H., Schulzke, C., Chandrasekhar. V., Priyakumar, U. D., & Jana, A. (2021). Chemical Communications, 57, 9546-9549.
  49. Linear prediction residual for efficient diagnosis of Parkinson’s disease from gait. Alle, S., & Priyakumar, U. D. (2021). Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Part V 24 (pp. 614-623). 
  50. Memes: Machine learning framework for enhanced molecular screening. Mehta, S., Laghuvarapu, S., Pathak, Y., Sethi, A., Alvala, M., & Priyakumar, U. D. (2021). Chemical science, 12, 11710-11721.
  51. Mmbert: Multimodal bert pretraining for improved medical vqa. Khare, Y., Bagal, V., Mathew, M., Devi, A., Priyakumar, U. D., & Jawahar, C. V. (2021, April). 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (pp. 1033-1036). IEEE.
  52. Learning atomic interactions through solvation free energy prediction using graph neural networks. Pathak, Y., Mehta, S., & Priyakumar, U. D. (2021). Journal of Chemical Information and Modeling, 61, 689-698.
  53. DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers. Modee, R., Agarwal, S., Verma, A., Joshi, K., & Priyakumar, U. D. (2021). Physical Chemistry Chemical Physics, 23, 21995-22003.
  54. Multiscale Modeling of Wobble to Watson–Crick-Like Guanine–Uracil Tautomerization Pathways in RNA. Chandorkar, S., Raghunathan, S., Jaganade, T., & Priyakumar, U. D. (2021). International journal of molecular sciences, 22, 5411.
  55. Machine learning based clinical decision support system for early COVID-19 mortality prediction. Karthikeyan, A., Garg, A., Vinod, P. K., & Priyakumar, U. D. (2021). Frontiers in public health, 9, 626697.
  56. Host metabolic reprogramming in response to SARS-CoV-2 infection: A systems biology approach. Moolamalla, S. T. R., Balasubramanian, R., Chauhan, R., Priyakumar, U. D., & Vinod, P. K. (2021). Microbial pathogenesis, 158, 105114.
  57. Deep learning enabled inorganic material generator. Pathak, Y., Juneja, K. S., Varma, G., Ehara, M., & Priyakumar, U. D. (2020). Physical Chemistry Chemical Physics, 22, 26935-26943.
  58. The HIV-1 Vpu transmembrane domain topology and formation of a hydrophobic interface with BST-2 are critical for Vpu-mediated BST-2 downregulation. Khan, N., Padhi, S., Patel, P., Priyakumar, U. D., & Jameel, S. (2020). bioRxiv.
  59. Machine learning for accurate force calculations in molecular dynamics simulations. Pattnaik, P., Raghunathan, S., Kalluri, T., Bhimalapuram, P., Jawahar, C. V., & Priyakumar, U. D. (2020). The Journal of Physical Chemistry A, 124, 6954-6967.
  60. Transition between [R]-and [S]-stereoisomers without bond breaking. Raghunathan, S., Yadav, K., Rojisha, V. C., Jaganade, T., Prathyusha, V., Bikkina, S., ... & Priyakumar, U. D. (2020). Physical Chemistry Chemical Physics, 22, 14983-14991.
  61. Enantioseparation and chiral induction in Ag 29 nanoclusters with intrinsic chirality. Yoshida, H., Ehara, M., Priyakumar, U. D., Kawai, T., & Nakashima, T. (2020). Chemical Science, 11, 2394-2400.
  62. Urea-water solvation of protein side chain models. Jaganade, T., Chattopadhyay, A., Raghunathan, S., & Priyakumar, U. D. (2020). Journal of Molecular Liquids, 311, 113191.
  63. Band nn: A deep learning framework for energy prediction and geometry optimization of organic small molecules. Laghuvarapu, S., Pathak, Y., & Priyakumar, U. D. (2020). Journal of computational chemistry, 41, 790-799.
  64. Chemically interpretable graph interaction network for prediction of pharmacokinetic properties of drug-like molecules. Pathak, Y., Laghuvarapu, S., Mehta, S., & Priyakumar, U. D. (2020, April). AAAI Conference on Artificial Intelligence (Vol. 34, No. 01, pp. 873-880).
  65. Urea-aromatic interactions in biology. Raghunathan, S., Jaganade, T., & Priyakumar, U. D. (2020). Biophysical Reviews, 12, 65-84.
  66. Selectivity and transport in aquaporins from molecular simulation studies. Padhi, S., & Priyakumar, U. D. (2020). Vitamins and Hormones, 112, 47-70.
  67. Energetic, structural and dynamic properties of nucleobase-urea interactions that aid in urea assisted RNA unfolding. Jaganade, T., Chattopadhyay, A., Pazhayam, N. M., & Priyakumar, U. D. (2019). Scientific Reports, 9, 8805.
  68. Cholic acid-derived amphiphile which combats gram-positive bacteria-mediated infections via disintegration of lipid clusters. Kumar, S., Thakur, J., Yadav, K., Mitra, M., Pal, S., Ray, A., Gupta, S., Medatwal, N., Gupta, R., Mishra, D., Rani, P., Padhi, S., Sharma, P., Kapil, A., Priyakumar, U. D., Dasgupta, U., Thukral, L., & Bajaj, A. (2019). ACS Biomaterials Science & Engineering, 5, 4764-4775.
  69. Gold‐Palladium Nanocluster Catalysts for Homocoupling: Electronic Structure and Interface Dynamics. Gurtu, S., Rai, S., & Priyakumar, U. D. (2019). The Chemical Record, 19, 947-959.
  70. Comparative study of the efficiency of Au, Ag, Pd and Pt based mono and bimetallic trimer clusters for the CO oxidation reaction. Gurtu, S., Rai, S., & Priyakumar, U. D. (2019). J. Indian Chem. Soc, 96, 921-931.
  71. Computational modeling of the catalytic mechanism of hydroxymethylbilane synthase. Bung, N., Roy, A., Priyakumar, U. D., & Bulusu, G. (2019). Physical Chemistry Chemical Physics, 21, 7932-7940.
  72. Recent Advancements in Computing Reliable Binding Free Energies in Drug Discovery Projects. Murugan, N. A., Poongavanam, V., & Priyakumar, U. D. (2019). Structural Bioinformatics: Applications in Preclinical Drug Discovery Process, 27, 221-246.
  73. Quantum mechanical investigation of the nature of nucleobase-urea stacking interaction, a crucial driving force in RNA unfolding in aqueous urea. Alodia, N., Jaganade, T., & Priyakumar, U. D. (2018). Journal of Chemical Sciences, 130, 1-13.
  74. A probabilistic framework for constructing temporal relations in replica exchange molecular trajectories. Chattopadhyay, A., Zheng, M., Waller, M. P., & Priyakumar, U. D. (2018). Journal of chemical theory and computation, 14, 3365-3380.
  75. Model molecules to classify CH⋯ O hydrogen-bonds. Vibhute, A. M., Priyakumar, U. D., Ravi, A., & Sureshan, K. M. (2018). Chemical Communications, 54, 4629-4632.
  76. pH-mediated gating and formate transport mechanism in the Escherichia coli formate channel. Padhi, S., Reddy, L. K., & Priyakumar, U. D. (2017). Molecular Simulation, 43, 1300–1306.
  77. Role of urea–aromatic stacking interactions in stabilizing the aromatic residues of the protein in urea-induced denatured state. Goyal, S., Chattopadhyay, A., Kasavajhala, K., & Priyakumar, U. D. (2017). Journal of the American Chemical Society, 139, 14931-14946.
  78. Temperature dependence of the stability of ion pair interactions, and its implications on the thermostability of proteins from thermophiles. Bikkina, S., Bhati, A. P., Padhi, S., & Priyakumar, U. D. (2017). Journal of Chemical Sciences, 129, 405-414.
  79. Modeling complex biomolecular systems and processes using molecular mechanics force fields and molecular dynamics simulations. Padhi, S., & Priyakumar, U. D. (2017). Theoretical and computational advances: From atoms to molecules to materials, 105-127.
  80. Microsecond simulation of human aquaporin 2 reveals structural determinants of water permeability and selectivity. Padhi, S., & Priyakumar, U. D. (2017). Biochimica et Biophysica Acta (BBA)-Biomembranes, 1859, 10-16.
  81. Urea mimics nucleobases by preserving the helical integrity of B-DNA duplexes via hydrogen bonding and stacking interactions. Suresh, G., Padhi, S., Patil, I., & Priyakumar, U. D. (2016). Biochemistry, 55, 5653-5664.
  82. Urea–aromatic stacking and concerted urea transport: conserved mechanisms in urea transporters revealed by molecular dynamics. Padhi, S., & Priyakumar, U. D. (2016). Journal of Chemical Theory and Computation, 12, 5190-5200.
  83. Ligand-induced stabilization of a duplex-like architecture is crucial for the switching mechanism of the SAM-III riboswitch. Suresh, G., Srinivasan, H., Nanda, S., & Priyakumar, U. D. (2016). Suresh, G., Srinivasan, H., Nanda, S., & Priyakumar, U. D. (2016).
  84. Structure, interaction, and dynamics of Au/Pd bimetallic nanoalloys dispersed in aqueous ethylpyrrolidone, a monomeric moiety of polyvinylpyrrolidone. Gupta, A., Boekfa, B., Sakurai, H., Ehara, M., & Priyakumar, U. D. (2016). The Journal of Physical Chemistry C, 120, 17454-17464.
  85. Cooperation of hydrophobic gating, knock-on effect, and ion binding determines ion selectivity in the p7 Channel. Padhi, S., & Priyakumar, U. D. (2016). The Journal of Physical Chemistry B, 120, 4351-4356.
  86. Dynamic ligand-based pharmacophore modeling and virtual screening to identify mycobacterial cyclopropane synthase inhibitors. Choudhury, C., Priyakumar, U. D., & Sastry, G. N. (2016). Journal of Chemical Sciences, 128, 719-732.
  87. Structural and Functional Diversities of the Hexadecahydro‐1H‐cyclopenta [a] phenanthrene Framework, a Ubiquitous Scaffold in Steroidal Hormones. Choudhury, C., Priyakumar, U. D., & Sastry, G. N. (2016). Molecular Informatics, 35, 145-157.
  88. Ability of density functional theory methods to accurately model the reaction energy pathways of the oxidation of CO on gold cluster: A benchmark study. Gurtu, S., Rai, S., Ehara, M., & Priyakumar, U. D. (2016). Theoretical Chemistry Accounts, 135, 1-12.
  89. Sumoylation of Sir2 differentially regulates transcriptional silencing in yeast. Hannan, A., Abraham, N. M., Goyal, S., Jamir, I., Priyakumar, U. D., & Mishra, K. (2015). Nucleic acids research, 43, 10213-10226.
  90. Modeling the structure of SARS 3a transmembrane protein using a minimum unfavorable contact approach. Ramakrishna, S., Padhi, S., & Priyakumar, U. D. (2015). Journal of Chemical Sciences, 127, 2159-2169.
  91. Molecular dynamics study of the structure, flexibility, and hydrophilicity of PETIM dendrimers: a comparison with PAMAM dendrimers. Kanchi, S., Suresh, G., Priyakumar, U. D., Ayappa, K. G., & Maiti, P. K. (2015). The Journal of Physical Chemistry B, 119, 12990-13001.
  92. Atomistic details of the molecular recognition of DNA-RNA hybrid duplex by ribonuclease H enzyme. Suresh, G., & Priyakumar, U. D. (2015). Journal of Chemical Sciences, 127, 1701-1713.
  93. Inclusion of methoxy groups inverts the thermodynamic stabilities of DNA–RNA hybrid duplexes: A molecular dynamics simulation study. Suresh, G., & Priyakumar, U. D. (2015). Journal of Molecular Graphics and Modelling, 61, 150-159.
  94. Ion hydration dynamics in conjunction with a hydrophobic gating mechanism regulates ion permeation in p7 viroporin from hepatitis C virus. Padhi, S., & Priyakumar, U. D. (2015). The Journal of Physical Chemistry B, 119, 6204-6210.
  95. Small-molecule inhibitors of ERK-mediated immediate early gene expression and proliferation of melanoma cells expressing mutated BRaf. Samadani, R., Zhang, J., Brophy, A., Oashi, T., Priyakumar, U. D., Raman, E. P., ... & Shapiro, P. (2015). The Biochemical Journal, 467, 425.
  96. Dynamics based pharmacophore models for screening potential inhibitors of mycobacterial cyclopropane synthase. Choudhury, C., Priyakumar, U. D., & Sastry, G. N. (2015). Journal of chemical information and modeling, 55, 848-860.
  97. Prediction of the structures of helical membrane proteins based on a minimum unfavorable contacts approach. Padhi, S., Ramakrishna, S., & Priyakumar, U. D. (2015). Journal of computational chemistry, 36, 539-552.
  98. Dispersion interactions between urea and nucleobases contribute to the destabilization of RNA by urea in aqueous solution. Kasavajhala, K., Bikkina, S., Patil, I., MacKerell Jr, A. D., & Priyakumar, U. D. (2015). The Journal of Physical Chemistry B, 119, 3755-3761.
  99. Nucleobases tagged to gold nanoclusters cause a mechanistic crossover in the oxidation of CO. Rai, S., Ehara, M., & Priyakumar, U. D. (2015). Physical Chemistry Chemical Physics, 17, 24275-24281.
  100. Binding to gold nanoclusters alters the hydrogen bonding interactions and electronic properties of canonical and size-expanded DNA base pairs. Rai, S., Singh, H., & Priyakumar, U. D. (2015). RSC Advances, 5, 49408-49419.
  101. Double zipper helical assembly of deoxyoligonucleotides: mutual templating and chiral imprinting to form hybrid DNA ensembles. Narayanaswamy, N., Suresh, G., Priyakumar, U. D., & Govindaraju, T. (2015). Chemical Communications, 5, 5493-5496.
  102. Molecular dynamics investigation of the active site dynamics of mycobacterial cyclopropane synthase during various stages of the cyclopropanation process. Choudhury, C., Priyakumar, U. D., & Sastry, G. N. (2014). Journal of structural biology, 187, 38-48.
  103. Atomistic investigation of the effect of incremental modification of deoxyribose sugars by locked nucleic acid (β-d-LNA and α-l-LNA) moieties on the structures and thermodynamics of DNA–RNA hybrid duplexes. Suresh, G., & Priyakumar, U. D. (2014). The Journal of Physical Chemistry B, 118, 5853-5863.
  104. Atomistic detailed mechanism and weak cation-conducting activity of HIV-1 Vpu revealed by free energy calculations. Padhi, S., Burri, R. R., Jameel, S., & Priyakumar, U. D. (2014). PloS one, 9, e112983.
  105. Modulation of structural, energetic and electronic properties of DNA and size-expanded DNA bases upon binding to gold clusters. Rai, S., Ranjan, S., Singh, H., & Priyakumar, U. D. (2014). RSC advances, 4, 29642-29651.
  106. DNA–RNA hybrid duplexes with decreasing pyrimidine content in the DNA strand provide structural snapshots for the A-to B-form conformational transition of nucleic acids. Suresh, G., & Priyakumar, U. D. (2014). Physical Chemistry Chemical Physics, 16, 18148-18155.
  107. Solvent‐induced helical assembly and reversible chiroptical switching of chiral cyclic‐dipeptide‐functionalized naphthalenediimides. Manchineella, S., Prathyusha, V., Priyakumar, U. D., & Govindaraju, T. (2013). Chemistry–A European Journal, 19, 16615-16624.
  108. Structures, dynamics, and stabilities of fully modified locked nucleic acid (β-D-LNA and α-L-LNA) duplexes in comparison to pure DNA and RNA duplexes. Suresh, G., & Priyakumar, U. D. (2013). The Journal of Physical Chemistry B, 117, 5556-5564.
  109. Synthesis and Reactivity Studies of Dicationic Dihydrogen Complexes Bearing Sulfur‐Donor Ligands: A Combined Experimental and Computational Study. Gandhi, T., Rajkumar, S., Prathyusha, V., Priyakumar, U.D. (2013). European Journal of Inorganic Chemistry, 9, 1434-1443.
  110. Role of conformational properties on the transannular Diels–Alder reactivity of macrocyclic trienes with varying linker lengths. Prathyusha, V., & Priyakumar, U. D. (2013). RSc Advances, 3, 15892-15899.
  111. Molecular dynamics simulations reveal the HIV-1 Vpu transmembrane protein to form stable pentamers. Padhi, S., Khan, N., Jameel, S., & Priyakumar, U. D. (2013). PloS one, 8, e79779.
  112. Crenarchaeal chromatin proteins Cren7 and Sul7 compact DNA by inducing rigid bends. Driessen, R. P., Meng, H., Suresh, G., Shahapure, R., Lanzani, G., Priyakumar, U. D., ... & Dame, R. T. (2013). Nucleic acids research, 41, 196-205.
  113. Inter-versus intra-molecular cyclization of tripeptides containing tetrahydrofuran amino acids: a density functional theory study on kinetic control. Kumar, N. S., Priyakumar, U. D., Singh, H., Roy, S., & Chakraborty, T. K. (2012). Journal of molecular modeling, 18, 3181-3197.
  114. Transannular Diels–Alder Reactivities of 14-Membered Macrocylic Trienes and Their Relationship with the Conformational Preferences of the Reactants: A Combined Quantum Chemical and Molecular Dynamics Study. Prathyusha, V., Ramakrishna, S., & Priyakumar, U. D. (2012). The Journal of Organic Chemistry, 77, 5371-5380.
  115. Computational investigation of the effect of thermal perturbation on the mechanical unfolding of titin I27. Bung, N., & Priyakumar, U. D. (2012). Journal of molecular modeling, 18, 2823-2829.
  116. Role of Hydrophobic Core on the Thermal Stability of Proteins—Molecular Dynamics Simulations on a Single Point Mutant of Sso7d. Priyakumar, U. D. (2012). Journal of Biomolecular Structure and Dynamics, 29, 961-971.
  117. Trienes, and their Relationship with the Conformational Preferences of the Reactants. A Combined Quantum Chemical and Molecular Dynamics Study. Prathyusha, V., Ramakrishna, S., & Priyakumar, U. D. (2012).  J. Org. Chem, 77, 5371-5380.
  118. Characterization of ERK docking domain inhibitors that induce apoptosis by targeting Rsk-1 and caspase-9. Boston, S. R., Deshmukh, R., Strome, S., Priyakumar, U. D., MacKerell, A. D., & Shapiro, P. (2011). BMC cancer, 11, 1-12.
  119. Impact of 2′‐hydroxyl sampling on the conformational properties of RNA: update of the CHARMM all‐atom additive force field for RNA. Denning, E. J., Priyakumar, U. D., Nilsson, L., & Mackerell Jr, A. D. (2011). Journal of computational chemistry, 32, 1929-1943.
  120. Molecular simulations on the thermal stabilization of DNA by hyperthermophilic chromatin protein Sac7d, and associated conformational transitions. Priyakumar, U. D., Harika, G., & Suresh, G. (2010). The journal of physical chemistry B, 114, 16548-16557.
  121.     Atomistic details of the ligand discrimination mechanism of SMK/SAM-III riboswitch. Priyakumar, U. D. (2010). The Journal of Physical Chemistry B, 114, 9920-9925.
  122. Role of the adenine ligand on the stabilization of the secondary and tertiary interactions in the adenine riboswitch. Priyakumar, U. D., & MacKerell Jr, A. D. (2010). Journal of Molecular Biology, 396, 1422-1438.
  123. Structural and energetic determinants of thermal stability and hierarchical unfolding pathways of hyperthermophilic proteins, Sac7d and Sso7d. Priyakumar, U. D., Ramakrishna, S., Nagarjuna, K. R., & Reddy, S. K. (2010). The Journal of Physical Chemistry B, 114, 1707-1718.
  124. Urea destabilizes RNA by forming stacking interactions and multiple hydrogen bonds with nucleic acid bases. Priyakumar, U. D., Hyeon, C., Thirumalai, D., & MacKerell Jr, A. D. (2009). Journal of the American Chemical Society, 131, 17759-17761.
  125. Molecular Modeling of Base Flipping in DNA. Priyakumar, U. D., & MacKerell Jr, A. D. (2009). DNA and RNA Modification Enzymes, 51.
  126. Atomic Detail Investigation of the Structure and Dynamics of DNA• RNA Hybrids: A Molecular Dynamics Study. Priyakumar, U. D., & MacKerell, A. D. (2008). The Journal of Physical Chemistry B, 112, 1515-1524.
  127. Computational approaches for investigating base flipping in oligonucleotides. Priyakumar, U. D., & MacKerell, A. D. (2006). Chemical reviews, 106, 489-505.
  128. NMR imino proton exchange experiments on duplex DNA primarily monitor the opening of purine bases. Priyakumar, U. D., & MacKerell, A. D. (2006). Journal of the American Chemical Society, 128, 678-679.
  129. Base flipping in a GCGC containing DNA dodecamer: A comparative study of the performance of the nucleic acid force fields, CHARMM, AMBER, and BMS. Priyakumar, U. D., & MacKerell, A. D. (2006). Journal of Chemical Theory and Computation, 2, 187-200.
  130. A lipophilic hexaporphyrin assembly supported on a stannoxane core. Chandrasekhar, V., Nagendran, S., Azhakar, R., Kumar, M. R., Srinivasan, A., Ray, K., ... , Priyakumar, U. D., & Sastry, G. N. (2005). Journal of the American Chemical Society, 127, 2410-2411.
  131. Conformational determinants of tandem GU mismatches in RNA: insights from molecular dynamics simulations and quantum mechanical calculations. Pan, Y., Priyakumar, U. D., & MacKerell, A. D. (2005). Biochemistry, 44, 1433-1443.
  132. Exploration of C6H6 potential energy surface: A computational effort to unravel the relative stabilities and synthetic feasibility of new benzene isomers. Dinadayalane, T. C., Priyakumar, U. D., & Sastry, G. N. (2004). The Journal of Physical Chemistry A, 108, 11433-11448.
  133. Development of predictive models of π-facial selectivity; a critical study of nucleophilic addition to sterically unbiased ketones. Priyakumar, U. D., Sastry, G. N., & Mehta, G. (2004). Tetrahedron, 60, 3465-3472.
  134. C21H9Z (Z=− 3 to+ 3): a theoretical study on the redox behaviour of C3 symmetric fragment of C60. Priyakumar, U. D., & Sastry, G. N. (2004). Journal of Molecular Structure: THEOCHEM, 674, 69-75.
  135. Facile valence isomerization among bis (silacyclopropenyl), disila (Dewar benzene) and disilabenzvalene. Priyakumar, U. D., Punnagai, M., & Sastry, G. N. (2004). Journal of organometallic chemistry, 689, 1284-1287.
  136. A computational study of cation–π interactions in polycyclic systems: exploring the dependence on the curvature and electronic factors. Priyakumar, U. D., Punnagai, M., Mohan, G. K., & Sastry, G. N. (2004). Tetrahedron, 60, 3037-3043.
  137. The design of molecules containing planar tetracoordinate carbon. Priyakumar, U. D., Reddy, A. S., & Sastry, G. N. (2004). Tetrahedron letters, 45, 2495-2498.
  138. A system with three contiguous planar tetracoordinate carbons is viable: a computational study on a C6H62= isomer. Priyakumar, U. D., & Sastry, G. N. (2004). Tetrahedron letters, 45, 1515-1517.
  139. Basis set and method dependence of the relative energies of C2S2H2 isomers. Vijay, D., Priyakumar, U. D., & Sastry, G. N. (2004). Chemical physics letters, 383, 192-197.
  140. Design of neutral hydrocarbons having a planar tetracoordinate carbon. Priyakumar, U. D., & Sastry, G. N. (2004).  International Journal of Computer Applications, 43A, 455-457.
  141. On the use of NICS criterion to evaluate aromaticity in heteroaromatics involving III and IV row main group elements. Saieswari, A., Priyakumar, U. D., & Sastry, G. N. (2003). Journal of Molecular Structure: THEOCHEM, 663, 145-148.
  142. Cation-π interactions of curved polycyclic systems: M+ (M= Li and Na) ion complexation with buckybowls. Priyakumar, U. D., & Sastry, G. N. (2003). Tetrahedron letters, 44, 6043-6046.
  143. The tricyclo [2.1. 0.02, 5] pentan-3-one system: a new probe for the study of π-facial selectivity in nucleophilic additions. Mehta, G., Singh, S. R., Priyakumar, U. D., & Sastry, G. N. (2003). Tetrahedron letters, 44, 3101-3104.
  144. Silaaromaticity in polycyclic systems: A computational study. Dhevi, D. M., Priyakumar, U. D., & Sastry, G. N. (2003). The Journal of Organic Chemistry, 68, 1168-1171.
  145. Measures to evaluate heteroaromaticity and their limitations: Story of skeletally substituted benzenes. Priyakumar, U. D., & Sastry, G. N. (2003). Journal of Chemical Sciences, 115, 49-66.
  146. The effect of bulky group substitution on the skeleton, geometries, relative energies and the reactivities of silabenzene valence isomers. Dhevi, D. M., Priyakumar, U. D., & Sastry, G. N. (2002). Journal of Molecular Structure: THEOCHEM, 618, 173-179.
  147. Isomers of disilabenzene (C4Si2H6): A computational study. Priyakumar, U. D., Saravanan, D., & Sastry, G. N. (2002). Organometallics, 21, 4823-4832.
  148. π-Facial selectivities in nucleophilic additions to 4-hetero-tricyclo [5.2. 1.02, 6] decan-10-ones and 4-hetero-tricyclo [5.2. 1.02, 6] dec-8-en-10-ones: an experimental and computational study. Mehta, G., Gagliardini, V., Priyakumar, U. D., & Sastry, G. N. (2002). Tetrahedron letters, 43, 2487-2490.
  149. Theoretical study of silabenzene and its valence isomers. Priyakumar, U. D., & Sastry, G. N. (2002). Organometallics, 21, 1493-1499.
  150. A theoretical study of the structures, energetics, stabilities, reactivities, and out-of-plane distortive tendencies of skeletally substituted benzenes (CH) 5XH and (CH) 4 (XH) 2 (X= B-, N+, Al-, Si, P+, Ga-, Ge, and As+). Priyakumar, U. D., & Sastry, G. N. (2002). The Journal of Organic Chemistry, 67, 271-281.
  151. Ring closure synthetic strategies toward buckybowls: benzannulation versus cyclopentannulation. Dinadayalane, T. C., Priyakumar, U. D., & Sastry, G. N. (2002). Journal of the Chemical Society, Perkin Transactions 2, 94-101.
  152. A computational study of the valence isomers of benzene and their group V hetero analogs. Priyakumar, U. D., Dinadayalane, T. C., & Sastry, G. N. (2002). New Journal of Chemistry, 26, 347-353.
  153. The bicyclo [2.1. 1] hexan-2-one system: a new probe for the experimental and computational study of electronic effects in π-facial selectivity in nucleophilic additions. Mehta, G., Singh, S. R., Gagliardini, V., Priyakumar, U. D., & Sastry, G. N. (2001). Tetrahedron Letters, 42, 8527-8530.
  154. Heterobuckybowls: A theoretical study on the structure, bowl-to-bowl inversion barrier, bond length alternation, structure-inversion barrier relationship, stability, and synthetic feasibility. Priyakumar, U. D., & Sastry, G. N. (2001). The Journal of organic chemistry, 66, 6523-6530.
  155. Theoretical studies on the effect of sequential benzannulation to corannulene. Dinadayalane, T. C., Priyakumar, U. D., & Sastry, G. N. (2001). Journal of Molecular Structure: THEOCHEM, 543, 1-10.
  156. First ab initio and density functional study on the structure, bowl-to-bowl inversion barrier, and vibrational spectra of the elusive C 3 v-Symmetric Buckybowl: Sumanene, C21H12. Priyakumar, U. D., & Sastry, G. N. (2001). The Journal of Physical Chemistry A, 105, 4488-4494.
  157. An ab initio and DFT study of the valence isomers of pyridine. Priyakumar, U. D., Dinadayalane, T. C., & Sastry, G. N. (2001). Chemical physics letters, 337, 361-367.
  158. Tailoring the curvature, bowl rigidity and stability of heterobuckybowls: theoretical design of synthetic strategies towards heterosumanenes. Priyakumar, U. D., & Sastry, G. N. (2001). Journal of Molecular Graphics and Modelling, 19, 266-269.
  159. Structures, energetics and vibrational spectra of the valence isomers of phosphinine. An ab initio and DFT study. Priyakumar, U. D., Dinadayalane, T. C., & Sastry, G. N. (2001). Chemical physics letters, 336, 343-348.
  160. Theory provides a clue to accomplish the synthesis of sumanene, C21H12, the prototypical C3v-buckybowl. Priyakumar, U. D., & Sastry, G. N. (2001). Tetrahedron Letters, 42, 1379-1381.
  161. MOLECULAR STRUCTURE, BONDING, QUANTUM CHEMISTRY, AND GENERAL THEORY-First ab Initio and Density Functional Study on the Structure, Bowl-to-Bowl Inversion Barrier, and Vibrational Spectra. Priyakumar, U. D., & Sastry, G. N. (2001). Journal of Physical Chemistry A, 105, 4488-4494.
  162. Ring closure synthetic strategies toward buckybowls: benzannulation versus cyclopentannulation. Dinadayalane, T. C., Priyakumar, U., Sastry, G. N., Dinadayalane, T. C., Priyakumar, U. D., & Sastry, G. N. (2001). Journal of the Chemical Society, Perkin Transactions 2, 94-101.
  163. The role of heteroatom substitution in the rigidity and curvature of buckybowls. A theoretical study. Sastry, G. N., & Priyakumar, U. D. (2001). Journal of the Chemical Society, Perkin Transactions 2, 30-40.
  164. Structures, Energetics, Relative Stabilities, and Out-of-Plane Distortivities of Skeletally Disubstituted Benzenes,(CH) 4X2 (X= N, P, C-, Si-, O+, and S+): An ab Initio and DFT Study. Priyakumar, U. D., & Sastry, G. N. (2000). Journal of the American Chemical Society, 122, 11173-11181.
  165. Structure, stability and reactivity parameters of (CH) 8 isomers and their cation and anion radical counterparts: A theoretical study. Priyakumar, U. D., & Sastry, G. N. (2000). Indian Journal of Chemistry Section A-Inorganic Bio - Inorganic Physical Theoretical & Analytical Chemistry, 39, 92-99.
  166. Effect of substitution on the curvature and bowl-to-bowl inversion barrier of bucky-bowls. Study of mono-substituted corannulenes (C19XH10, X= B−, N+, P+ and Si). Sastry, G. N., Rao, H. S. P., Bednarek, P., & Priyakumar, U. D. (2000). Chemical Communications, 10, 843-844.

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