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RESEARCH PAPER · Journal of Computational Chemistry · 2019

BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules

Siddhartha Laghuvarapu, Yashaswi Pathak, U. Deva Priyakumar

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BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small MoleculesOriginalDeep learning enabled inorganic material generator52Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-Like Molecules37Assessing conformer energies using electronic structure and machine learning methods82Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations79Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints42Choosing the right molecular machine learning potential181Computer-Aided Drug Design51DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers13MEMES: Machine learning framework for Enhanced MolEcular Screening39Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks52Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization9Choosing the right molecular machine learning potential8SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation26Benchmark study on deep neural network potentials for small organic molecules8Artificial intelligence: machine learning for chemical sciences104Molecular representations for machine learning applications in chemistry93Deep dive into machine learning density functional theory for materials science and chemistry111Predicting structure‐dependent properties directly from the three dimensional molecular images via convolutional neural networks8Deep Convolutional Neural Network for Molecular EnergyPrediction0MO-MEMES: A method for accelerating virtual screening using multi-objective Bayesian optimization7Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials51Exploring the potential of AI-Chatbots in organic chemistry: An assessment of ChatGPT and Bard37Data intelligence for molecular science1Intramolecular proton transfer reaction dynamics using machine-learned ab initio potential energy surfaces1Open-Source Machine Learning in Computational Chemistry59A novel method for atomization energy prediction based on natural-parameter network3MolOpt: Autonomous Molecular Geometry Optimization Using Multiagent Reinforcement Learning12DeepSPInN – deep reinforcement learning for molecular structure prediction from infrared and 13 C NMR spectra26Quantum-Inspired Machine Learning Models for Cyber Threat Intelligence0MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows72Ab initio dispersion potentials based on physics-based functional forms with machine learning19Integration of evolutionary automated machine learning with structural sensitivity analysis for composite pipelines2

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  1. Deep learning enabled inorganic material generator

    Physical Chemistry Chemical Physics · 2020-01-01

    Yashaswi Pathak, Karandeep Singh Juneja, Girish Varma, Masahiro Ehara, U. Deva Priyakumar

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  2. Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-Like Molecules

    Proceedings of the AAAI Conference on Artificial Intelligence · 2020-04-03

    Yashaswi Pathak, Siddhartha Laghuvarapu, Sarvesh Mehta, U. Deva Priyakumar

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  3. Assessing conformer energies using electronic structure and machine learning methods

    International Journal of Quantum Chemistry · 2020-07-09

    Dakota L. Folmsbee, Geoffrey Hutchison

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  4. Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations

    The Journal of Physical Chemistry A · 2020-07-29

    Punyaslok Pattnaik, Shampa Raghunathan, Tarun Kalluri, Prabhakar Bhimalapuram, C. V. Jawahar, U. Deva Priyakumar

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  5. Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints

    Journal of Chemical Information and Modeling · 2020-10-29

    Obaidur Rahaman, Alessio Gagliardi

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  6. Choosing the right molecular machine learning potential

    Chemical Science · 2021-01-01

    Max Pinheiro, Fuchun Ge, Nicolas Ferré, Pavlo O. Dral, Mario Barbatti

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  7. Computer-Aided Drug Design

    Drug Discovery and Development · 2021-01-01

    Prasad V. Bharatam

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  8. DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers

    Physical Chemistry Chemical Physics · 2021-01-01

    Rohit Modee, Sheena Agarwal, Ashwini Verma, Kavita Joshi, U. Deva Priyakumar

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  9. MEMES: Machine learning framework for Enhanced MolEcular Screening

    Chemical Science · 2021-01-01

    Sarvesh Mehta, Siddhartha Laghuvarapu, Yashaswi Pathak, Aaftaab Sethi, Mallika Alvala, U. Deva Priyakumar

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  10. Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks

    Journal of Chemical Information and Modeling · 2021-02-05

    Yashaswi Pathak, Sarvesh Mehta, U. Deva Priyakumar

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  11. Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization

    The Journal of Physical Chemistry A · 2021-02-25

    Dakota L. Folmsbee, David Ryan Koes, Geoffrey Hutchison

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  12. Choosing the right molecular machine learning potential

    Unknown journal · 2021-08-16

    Max Pinheiro, Fuchun Ge, Nicolas Ferré, Pavlo O. Dral, Mario Barbatti

    Read original paper ↗ ·

  13. SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

    The Journal of Physical Chemistry B · 2021-09-21

    Yashas Samaga B L, Shampa Raghunathan, U. Deva Priyakumar

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  14. Benchmark study on deep neural network potentials for small organic molecules

    Journal of Computational Chemistry · 2021-12-06

    Rohit Modee, Siddhartha Laghuvarapu, U. Deva Priyakumar

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  15. Artificial intelligence: machine learning for chemical sciences

    Journal of Chemical Sciences · 2021-12-21

    Akshaya Karthikeyan, U. Deva Priyakumar

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  16. Molecular representations for machine learning applications in chemistry

    International Journal of Quantum Chemistry · 2021-12-27

    Shampa Raghunathan, U. Deva Priyakumar

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  17. Deep dive into machine learning density functional theory for materials science and chemistry

    Physical Review Materials · 2022-04-05

    Lenz Fiedler, Karan Shah, Michael Bußmann, Attila Cangi

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  18. Predicting structure‐dependent properties directly from the three dimensional molecular images via convolutional neural networks

    AIChE Journal · 2022-04-08

    Yunhao Xu, Xun Huang, Cunpu Li, Zidong Wei, Meng Wang

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  19. Deep Convolutional Neural Network for Molecular EnergyPrediction

    Unknown journal · 2022-05-27

    Omid Tarkhaneh

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  20. MO-MEMES: A method for accelerating virtual screening using multi-objective Bayesian optimization

    Frontiers in Medicine · 2022-09-23

    Sarvesh Mehta, Manan Goel, U. Deva Priyakumar

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  21. Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials

    Chemical Science · 2023-01-01

    Benjamin W. J. Chen, Xinglong Zhang, Jia Zhang

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  22. Exploring the potential of AI-Chatbots in organic chemistry: An assessment of ChatGPT and Bard

    Computers and Education Artificial Intelligence · 2023-01-01

    Kassem Hallal, Rasha Hamdan, Sami Tlais

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  23. Data intelligence for molecular science

    Chinese Science Bulletin (Chinese Version) · 2023-01-30

    Yanbo Li, Jun Jiang, Yi Luo

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  24. Intramolecular proton transfer reaction dynamics using machine-learned ab initio potential energy surfaces

    Machine Learning Science and Technology · 2023-06-05

    Shampa Raghunathan, Sai Ajay Kashyap Nakirikanti

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  25. Open-Source Machine Learning in Computational Chemistry

    Journal of Chemical Information and Modeling · 2023-07-19

    Alexander Hagg, Karl N. Kirschner

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  26. A novel method for atomization energy prediction based on natural-parameter network

    Chinese Chemical Letters · 2023-10-17

    Chaoqin Chu, Qinkun Xiao, Chaozheng He, Chen Chen, Lu Li, Junyan Zhao, Jinzhou Zheng, Yinhuan Zhang

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  27. MolOpt: Autonomous Molecular Geometry Optimization Using Multiagent Reinforcement Learning

    The Journal of Physical Chemistry B · 2023-11-28

    Rohit Modee, Sarvesh Mehta, Siddhartha Laghuvarapu, U. Deva Priyakumar

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  28. DeepSPInN – deep reinforcement learning for molecular structure prediction from infrared and 13 C NMR spectra

    Digital Discovery · 2024-01-01

    Sriram Devata, Bhuvanesh Sridharan, Sarvesh Mehta, Yashaswi Pathak, Siddhartha Laghuvarapu, Girish Varma, U. Deva Priyakumar

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  29. Quantum-Inspired Machine Learning Models for Cyber Threat Intelligence

    Communications in computer and information science · 2024-01-01

    Sana Pavan Kumar Reddy, Niladri Sekhar Dey, A. SrujanGoud, U. Rakshitha

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  30. MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows

    Journal of Chemical Theory and Computation · 2024-01-25

    Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Peikun Zheng, Yuxinxin Chen, Mario Barbatti, Olexandr Isayev, Cheng Wang, Bao-Xin Xue, Max Pinheiro, Yuming Su, Yiheng Dai, Yangtao Chen, Lina Zhang, Shuang Zhang, Arif Ullah, Quanhao Zhang, Yanchi Ou

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  31. Ab initio dispersion potentials based on physics-based functional forms with machine learning

    The Journal of Chemical Physics · 2024-05-08

    Corentin Villot, Ka Un Lao

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  32. Integration of evolutionary automated machine learning with structural sensitivity analysis for composite pipelines

    Knowledge-Based Systems · 2024-08-08

    Nikolay O. Nikitin, Maiia Pinchuk, Valerii Pokrovskii, Peter Shevchenko, Andrey Getmanov, Yaroslav Aksenkin, Ilia Revin, Andrey Stebenkov, Vladimir Latypov, Ekaterina Poslavskaya, Anna V. Kalyuzhnaya

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