Journal

2024

"Help! I need some music!”: Analysing music discourse & depression on Reddit

"Bhavyajeet Singh,KunalVaswani ,SreeharshaParuchuri ,SuviSaarikallio, PonnurangamKumaraguru , Vinoo Alluri "

PLOS ONE

Deep reinforcement learning in chemistry: A review

Bhuvanesh Sridharan, Animesh Sinha, Jai Bardhan, Rohit Modee, Masahiro Ehara and U. Deva Priyakumar

Journal of Computational Chemistry

Role of Amyloidogenic and Non-Amyloidogenic Protein Spaces in Neurodegenerative Diseases and Their Mitigation Using Theranostic Agents

Kapali Suri, Madhu Ramesh, Mansi Bhandari, Vishakha Gupta, Virendra Kumar, Thimmaiah Govindaraju and N. Arul Murugan

ChemBioChem

DeepSPInN - deep reinforcement learning for molecular struture prediction from infrared and NMR spectra

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

Digital Discovery

PLAS-20k: Extended Dataset of Data Descriptor Protein-Ligand Affinities from MD Simulations for Machine Learning Applications

Divya B Korlepara, Vasavi CS, Rakesh Srivastava, Pradeep Kumar Pal, Saalim H Raza, Vishal Kumar, Shivam Pandit, Aathira G Nair, Sanjana Pandey, Shubham Sharma, Shruti Jeurkar, Kavita Thakran, Reena Jaglan, Shivangi Verma, Indhu Ramachandran, Prathit Chatterjee, Divya Nayar and U Deva Priyakumar

Scientific Data

2023

MolOpt: Autonomous Molecular Geometry Optimization Using Multi-Agent Reinforcement Learning​

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

The Journal OF Physical Chemistry B

Streamlining Pipeline Efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines

Karthik Viswanathan , Manan Goel , Siddhartha Laghuvarapu , Girish Varma & U. Deva Priyakumar

Scientific Reports

MeGen - generation of gallium metal clusters using reinforcement learning

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

Machine Learning: Science and Technology

Latent Biases in Machine Learning Models for Predicting Binding Affinities Using Popular Data Sets

Ganesh Chandan Kanakala, Rishal Aggarwal, Divya Nayar, and U. Deva Priyakumar

ACS OMEGA

2022

Efficient and enhanced sampling of drug-like chemical space for virtual screening and molecular design using modern machine learning methods

Manan Goel, Rishal Aggarwal, Bhuvanesh Sridharan, Pradeep Kumar Pal, U. Deva Priyakumar

WIRES

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

Sarvesh Mehta, Manan Goel, U Deva Priyakumar

Frontiers in Medicine

PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications

Divya B. Korlepara, C. S. Vasavi, Shruti Jeurkar, Pradeep Kumar Pal, Subhajit Roy, Sarvesh Mehta, Shubham Sharma, Vishal Kumar, Charuvaka Muvva, Bhuvanesh Sridharan, Akshit Garg, Rohit Modee, Agastya P. Bhati, Divya Nayar & U. Deva Priyakumar

Scientific Data

Deep Reinforcement Learning for Molecular Inverse Problem of Nuclear Magnetic Resonance Spectra to Molecular Structure

Bhuvanesh Sridharan, Sarvesh Mehta, Yashaswi Pathak, U. Deva Priyakumar

The Journal of Physical Chemistry

Birds - binding residue detection from protein sequences using deep resnets

VR Chelur, UD Priyakumar

Journal of Chemical Information and Modelling

Benchmark study on deep neural network potentials for small organic molecules

Rohit Modee, Siddhartha Laghuvarapu, U. Deva Priyakumar

Journal of Computational Chemistry

Modern Machine Learning for tackling inverse problems in Chemistry: Molecular Design to realization

Bhuvanesh Sridharan, Manan Goel, U. Deva Priyakumar

ChemComm

2021

MolGPT: Molecular Generation using a Transformer-Decoder Model

V Bagal, R Aggarwal, PK Vinod, UD Priyakumar

Journal of Chemical Information and Modelling

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

R Aggarwal, A Gupta, V Chelur, CV Jawahar, UD Priyakumar

Journal of Chemical Information and Modelling

Artificial Intelligence:Machine Learning for Chemical Sciences

Akshaya Karthikeyan, U. Deva Priyakumar

Journal of Chemical Sciences

Mining subgraph coverage patterns from graph transactions

AS Reddy, PK Reddy, A Mondal, UDPriyakumar

International Journal of Data Science and Analytics

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

AS Reddy, PK Reddy, A Mondal, UDPriyakumar

IEEE

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

 Yashaswi Pathak, Sarvesh Mehta, U Deva Priyakumar,

Journal of Chemical Information and Modelling

MoleGuLAR: Molecule Generation using Reinforcement Learning with Alternating Rewards

M Goel, S Raghunathan, S Laghuvarapu, UD Priyakumar

Journal of Chemical Information and Modelling

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

YS BL, S Raghunathan, UD Priyakumar

The Journal of Physical Chemistry B

MEMES: Machine learning framework for Enhanced MolEcular Screening

S Mehta, S Laghuvarapu, Y Pathak, A Sethi, M Alvala, UD Priyakumar

Chemical Science, Royal Society of Chemistry

APObind: A Dataset of Ligand Unbound Protein Conformations for Machine

R Aggarwal, A Gupta, UD Priyakumar

ICML Workshop on Computational Biology

Molecular Representations for Machine Learning Applications in Chemistry

Shampa Raghunathan, U. Deva Priyakumar

International Journal of Quantum Chemistry

2020

  Chemically interpretable graph interaction network for prediction of pharmacokinetic properties of drug-like molecules

Y Pathak, S Laghuvarapu, S Mehta, U Priyakumar,

AAAI Conference on Artificial Intelligence

2019

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