• Murugan N.A., Poongavanam V., Priyakumar U.D. (2019) - Recent Advancements in Computing Reliable Binding Free Energies in Drug Discovery Projects, In: Mohan C. (eds) Structural Bioinformatics: Applications in Preclinical Drug Discovery Process. Challenges and Advances in Computational Chemistry and Physics, vol 27. Springer, Cham.
  • Tabibu, S.; Vinod, P. K.; Jawahar, C. V. - Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning, Sci. Rep. 2019, 9, 10509
  • Kumar, S.; Vinod, P. K - Single-cell transcriptomic analysis of pancreatic islets in health and type 2 diabetes, Int. J. Adv. Eng. Sci. Appl. Math (springer) 2019, 11, 105-118.
  • Laghuvarapu, L.; Pathak, Y.; Priyakumar, U. D. - BAND NN: A deep learning framework for energy prediction and geometry optimization of organic small molecules, J. Comp. Chem. 2019 (under review).
  • Singh, N. P.; Vinod, P. K. - Integrative analysis of DNA methylation and gene expression in Papillary Renal Cell Carcinoma, Mol. Genet. Genomics 2019, (under revision).
  • Pathak, Y.; Laghuvarapu, S.; Mehta, S.; Priyakumar, U. D. - Novel deep learning architecture for solvation free energy prediction, 2019 (under review)
  • Patnaik, P.; Kalluri, T.; Bhimalapuram, P.; Jawahar, C. V.; Priyakumar, U. D. - On the fly machine learning modeling for DFT level molecular dynamics simulations of liquid Argon, 2019 (to be submitted).


  • Singh, N. P.; Bapi, R. S.; Vinod, P. K. - Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma, Comput. Biol. Med. 2018, 100, 92.
  • Chattopadhyay, A.; Zheng, M.; Waller, M.; Priyakumar, U. D. - A probabilistic framework for constructing temporal relations in replica exchange molecular trajectories, J. Chem. Theory Comput. 2018, 14, 3365.