| Learning to navigate the synthetically accessible chemical space using reinforcement learning SK Gottipati, B Sattarov, S Niu, Y Pathak, H Wei, S Liu, S Blackburn, ... International conference on machine learning, 3668-3679, 2020 | 127 | 2020 |
| Deep learning enabled inorganic material generator Y Pathak, KS Juneja, G Varma, M Ehara, UD Priyakumar Physical Chemistry Chemical Physics 22 (46), 26935-26943, 2020 | 44 | 2020 |
| Chemically interpretable graph interaction network for prediction of pharmacokinetic properties of drug-like molecules Y Pathak, S Laghuvarapu, S Mehta, UD Priyakumar Proceedings of the AAAI Conference on Artificial Intelligence 34 (01), 873-880, 2020 | 43 | 2020 |
| Band nn: A deep learning framework for energy prediction and geometry optimization of organic small molecules S Laghuvarapu, Y Pathak, UD Priyakumar Journal of computational chemistry 41 (8), 790-799, 2020 | 36 | 2020 |
| Memes: Machine learning framework for enhanced molecular screening S Mehta, S Laghuvarapu, Y Pathak, A Sethi, M Alvala, UD Priyakumar Chemical science 12 (35), 11710-11721, 2021 | 34 | 2021 |
| Learning atomic interactions through solvation free energy prediction using graph neural networks Y Pathak, S Mehta, UD Priyakumar Journal of Chemical Information and Modeling 61 (2), 689-698, 2021 | 28 | 2021 |
| Deep reinforcement learning for molecular inverse problem of nuclear magnetic resonance spectra to molecular structure B Sridharan, S Mehta, Y Pathak, UD Priyakumar The Journal of Physical Chemistry Letters 13 (22), 4924-4933, 2022 | 12 | 2022 |
| Maximum reward formulation in reinforcement learning SK Gottipati, Y Pathak, R Nuttall, R Chunduru, A Touati, SG Subramanian, ... arXiv preprint arXiv:2010.03744, 2020 | 8 | 2020 |
| Towered actor critic for handling multiple action types in reinforcement learning for drug discovery SK Gottipati, Y Pathak, B Sattarov, R Nuttall, M Amini, ME Taylor, ... Proceedings of the AAAI Conference on Artificial Intelligence 35 (1), 142-150, 2021 | 6 | 2021 |
| Enhanced Sampling of Chemical Space for High Throughput Screening Applications using Machine Learning S Mehta, S Laghuvarapu, Y Pathak, A Sethi, M Alvala, UD Priyakumar | 3 | 2021 |
| DeepSPInN-multimodal Deep learning for molecular Structure Prediction from Infrared and NMR spectra S Devata, B Sridharan, S Mehta, Y Pathak, S Laghuvarapu, G Varma, ... | 1 | 2023 |
| Spectra to Structure: Deep Reinforcement Learning for Molecular Inverse Problem B Sridharan, S Mehta, Y Pathak, UD Priyakumar | 1 | 2021 |
| Maximum reward formulation in reinforcement learning S Krishna Gottipati, Y Pathak, R Nuttall, R Chunduru, A Touati, ... arXiv e-prints, arXiv: 2010.03744, 2020 | 1 | 2020 |
| DeepSPInN-Deep reinforcement learning for molecular Structure Prediction from Infrared and 13C NMR spectra S Devata, B Sridharan, S Mehta, Y Pathak, S Laghuvarapu, G Varma, ... | | 2024 |
| System and method for learning to generate chemical compounds with desired properties B Sattarov, VSK Gottipati, Y Pathak, K Thomas US Patent App. 17/796,826, 2023 | | 2023 |
| System and method for exploring chemical space during molecular design using a machine learning model UD Priyakumar, S Mehta, S Laghuvarapu, Y Pathak US Patent App. 17/526,712, 2022 | | 2022 |
| MAXIMUM REWARD FORMULATION IN REINFORCE-MENT LEARNING SGS Touati, ME Taylor, S Chandar | | |
| SUPPLEMENTARY MATERIAL-LEARNING TO NAVIGATE THE SYNTHETICALLY ACCESSIBLE CHEMICAL SPACE USING REINFORCEMENT LEARNING SK Gottipati, B Sattarov, S Niu10, Y Pathak, H Wei, S Liu, KJ Thomas, ... | | |
U. Deva PriyakumarProfessor, IIIT HyderabadVerified email at iiit.ac.in
