U. Deva Priyakumar, PhD

Professor, CCNSB, IIIT-Hyderabad

Ph.D (Pondicherry University)

Research Areas:

Computational chemistry to study chemical molecules and reaction mechanisms, biomolecular simulations to investigate DNA, RNA, proteins and protein interactions and computer aided drug design

Email:deva@iiit.ac.in

Address: International Institute of Information Technology
Gachibowli
Hyderabad - 500 032
India

Phone: (91) (40) 6653 1000 Ext: 1161

Current Research

Resources

CIGIN

Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-like Molecules. This method allows prediction of solvation free energies of drug like molecules in any organic solvent and to obtain interaction maps.

delNetFF

Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulation. delNetFF method supplements the forces calculated using classical force field for running DFT level simulations at low computational cost.

BAND-NN

A deep learning architecture for prediction of atomization energy and for geometry optimization. The deep neural network allows prediction of DFT level molecular energies of small organic molecules. This can also be used to perform geometry optimization.

DING

Deep learning enabled for INorganic material Generator. This method is developed based on conditional variation auto encoder(CVAE) generates novel inorganic molecules with certain desired properties.

rex_md_kinetic

A probabilistic framework for visualizing “most reactive pathways” in molecular trajectories. This method allows calculation of qualitative kinetic properties without using temporal information (for eg: replica change molecular trajectories).

ml4science_tut

two simple machine learning tasks that may be useful for beginners in Machine Learning applications in fundamental sciences.

Spectra to Structure: Deep Reinforcement Learning for Molecular Inverse Problem

BiRDS – Binding Residue Detection from Protein Sequences using Deep ResNets

MolGPT: Molecular Generation Using a Transformer-Decoder Model

DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks

A Model of Graph Transactional Coverage Patterns with Applications to Drug Discovery

MOLEGULAR: Molecule Generation Using Reinforcement Learning with Alternating Rewards

Mining Subgraph Coverage Patterns from Graph transactions.

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

DART – Deep Learning Enabled Topological Interaction Model for Energy Prediction of Metal Clusters and its Application in Identifying Unique Low Energy Isomers

IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification.

MMBERT: Multimodal BERT Pretraining for Improved Medical VQA,

Linear Prediction Residual for Efficient Diagnosis of Parkinson’s Disease from Gait

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

Deep learning enabled inorganic material generator.

BAND NN: A deep learning framework for energy prediction and geometry optimization of organic small molecules

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