Representative Publications

  1. PLAS-20k: Extended Dataset of Protein-Ligand1 Affinities from MD Simulations for Machine Learning Applications, Korlepara D.B., Vasavi C. S., Srivastava R., Pal P.K., Raza S.H., Kumar V., Pandit S., Nair A.G., Pandey S., Sharma S., Jeurkar S., Thakran K., Jaglan R., Verma S., Ramachandran I., Chatterjee P., Nayar D., and Priyakumar U.D., Nature Scientific Data, 2023
  2. Latent biases in machine learning models for predicting binding affinities using popular data sets, Kanakala, G. C., Aggarwal, R., Nayar, D., & Priyakumar, U. D ACS Omega 2023, 8, 2, 2389–2397
  3. Birds - binding residue detection from protein sequences using deep resnets, Chelur, V. R. & Priyakumar, U. D., J. Chem. Inf. Model. 2022, 62, 8, 1809–1818.
  4. Deeppocket: ligand binding site detection and segmentation using 3d convolutional neural networks, Aggarwal, R., Gupta, A., Chelur, V., Jawahar, C. V., & Priyakumar, U. D., J Chem Inf Model. 2022 Nov 14;62(21):5069-5079
  5. Molgpt: molecular generation using a transformer-decoder model, Bagal, V., Aggarwal, R., Vinod, P. K., & Priyakumar, U. D., J Chem Inf Model. 2022 May 9;62(9):2064-2076
  6. MoleGuLar: molecule generation using reinforcement learning with alternating rewards, Goel, M., Raghunathan, S., Laghuvarapu, S., & Priyakumar, U. D., J. Chem. Inf. Model. 2021, 61, 12, 5815–5826
  7. Scones: self-consistent neural network for protein stability prediction upon mutation, Samaga, Y. B. L., Raghunathan, S., & Priyakumar, U. D., J. Phys. Chem. B 2021, 125, 38, 10657–10671
  8. Memes: machine learning framework for enhanced molecular screening, Mehta, S., Laghuvarapu, S., Pathak, Y., Sethi, A., Alvala, M., & Priyakumar, U. D., Chem Sci. 2021 Jul 26;12(35):11710-11721
  9. 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). ieee international conference on systems, man, and cybernetics (smc) (pp. 1068–1074)
  10. Band nn: a deep learning framework for energy prediction and geometry optimization of organic small molecules. Siddhartha Laghuvarapu, Yashaswi Pathak, U. Deva Priyakumar, J. Comput. Chem., 2020, 41, 790–799
  11. Synthesis and reactivity of nhc-coordinated phosphinidene oxide, Debabrata Dhara D., Pal P. K., Dolai R., Chrysochos N., Rawat H., Elvers, B. J., Krummenachar I., Braunschweig H., Schulzke C., Chandrashekhar V., Priyakumar U. D., Jana, A., Chem. Commun., 2021, 57, 9546-9549
  12. Modern machine learning for tackling inverse problems in chemistry: molecular design to realization, Sridharan, B., Goel, M., & Priyakumar, U. D. (2022), Chem. Commun., 2022,58, 5316-5331
  13. Deep reinforcement learning for molecular inverse problem of nuclear magnetic resonance spectra to molecular structure, Sridharan, B., Mehta, S., Pathak, Y., & Priyakumar, U. D., J. Phys. Chem. Lett. 2022, 13, 22, 4924–4933
  14. Role of Urea Aromatic Stacking Interactions in Stabilizing the Aromatic Residues of the Protein in Urea-Induced Denatured State, Siddharth Goyal, Aditya Chattopadhyay, Koushik Kasavajhala, U. Deva Priyakumar, J. Am. Chem. Soc., 2017, 139, 14931–14946 

Books

  1. Padhi, S. & Priyakumar, U. D. (2020). Selectivity and transport in aquaporins from molecular simulation studies. In G. Litwack (Ed.), Aquaporin regulation (Vol. 112, pp. 221–243). New York: Academic Press.
  2. Murugan, N. A., Poongavanam, V., & Priyakumar, U. D. (2019). Recent advancements in computing reliable binding free energies in drug discovery projects. In C. G. Mohan (Ed.), Structural bioinformatics: applications in preclinical drug discovery process (pp. 221–246). Switzerland: Springer.
  3. Priyakumar, U. D. & MacKerell, A. (2009). Molecular modeling of base flipping in dna. In H. Grosjean (Ed.), Dna and rna modification enzymes: structure, mechanism, function and evolution. CRC Press, Taylor and Francis Group.

Patents

  1. SYSTEM AND METHOD FOR EXPLORING CHEMICALSPACE DURING MOLECULAR DESIGN USING A MACHINE LEARNING MODEL 
    Inventor List: Prof. U.Deva Priyakumar, Sarvesh Mehta, Siddhartha Laghuvarapu, Yashaswi Pathak  Research lab : CCNSB
    Application number: 202041050608 
    Filled on: 20-Nov-21 
    Status: US patent filed

Complete List of Publications

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