Dynamic Scaled Sampling for Deterministic Constraints. - Lei Li 005; Bharath Ramsundar; Stuart Russell 001 (AISTATS, 2013)

The Extended Parameter Filter. - Yusuf Erol; Lei Li 005; Bharath Ramsundar; Stuart Russell 001 (ICML (3), 2013)

NVMKV: A Scalable and Lightweight Flash Aware Key-Value Store. - Leonardo Mármol; Swaminathan Sundararaman; Nisha Talagala; Raju Rangaswami; Sushma Devendrappa; Bharath Ramsundar; Sriram Ganesan (HotStorage, 2014)

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models. - Robert McGibbon; Bharath Ramsundar; Mohammad Sultan; Gert Kiss; Vijay S. Pande (ICML, 2014)

Massively Multitask Networks for Drug Discovery. - Bharath Ramsundar; Steven Kearnes; Patrick Riley 001; Dale Webster; David E. Konerding; Vijay S. Pande (CoRR, 2015)

Low Data Drug Discovery with One-shot Learning. - Han Altae-Tran; Bharath Ramsundar; Aneesh S. Pappu; Vijay S. Pande (CoRR, 2016)

Learning Protein Dynamics with Metastable Switching Systems. - Bharath Ramsundar; Vijay S. Pande (CoRR, 2016)

Computational Modeling of β-Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches. - Govindan Subramanian; Bharath Ramsundar; Vijay S. Pande; Rajiah Aldrin Denny (J. Chem. Inf. Model., 2016)

Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity. - Joseph Gomes; Bharath Ramsundar; Evan N. Feinberg; Vijay S. Pande (CoRR, 2017)

Retrosynthetic reaction prediction using neural sequence-to-sequence models. - Bowen Liu 014; Bharath Ramsundar; Prasad Kawthekar; Jade Shi; Joseph Gomes; Quang Luu Nguyen; Stephen Ho; Jack Sloane; Paul Wender; Vijay S. Pande (CoRR, 2017)

MoleculeNet: A Benchmark for Molecular Machine Learning. - Zhenqin Wu; Bharath Ramsundar; Evan N. Feinberg; Joseph Gomes; Caleb Geniesse; Aneesh S. Pappu; Karl Leswing; Vijay S. Pande (CoRR, 2017)

Is Multitask Deep Learning Practical for Pharma? - Bharath Ramsundar; Bowen Liu 014; Zhenqin Wu; Andreas Verras; Matthew Tudor; Robert P. Sheridan; Vijay S. Pande (J. Chem. Inf. Model., 2017)

Spatial Graph Convolutions for Drug Discovery. - Evan N. Feinberg; Debnil Sur; Brooke E. Husic; Doris Mai; Yang Li 042; Jianyi Yang; Bharath Ramsundar; Vijay S. Pande (CoRR, 2018)

Tokenized Data Markets. - Bharath Ramsundar; Roger Chen; Alok Vasudev; Rob Robbins; Artur Gorokh (CoRR, 2018)

Solving the RNA design problem with reinforcement learning. - Peter K. Eastman; Jade Shi; Bharath Ramsundar; Vijay S. Pande (PLoS Comput. Biol., 2018)

Molecular machine learning with DeepChem. - Bharath Ramsundar (2018)

Secure Computation in Decentralized Data Markets. - Fattaneh Bayatbabolghani; Bharath Ramsundar (CoRR, 2019)

From Data Science to Production ML: Introducing USENIX OpML. - Nisha Talagala; Bharath Ramsundar; Swaminathan Sundararaman (login Usenix Mag., 2019)

ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction. - Seyone Chithrananda; Gabriel Grand; Bharath Ramsundar (CoRR, 2020)

AMPL: A Data-Driven Modeling Pipeline for Drug Discovery. - Amanda J. Minnich; Kevin McLoughlin; Margaret Tse; Jason Deng; Andrew Weber; Neha Murad; Benjamin D. Madej; Bharath Ramsundar; Tom Rush; Stacie Calad-Thomson; Jim Brase; Jonathan E. Allen (J. Chem. Inf. Model., 2020)

Differentiable Physics: A Position Piece. - Bharath Ramsundar; Dilip Krishnamurthy; Venkatasubramanian Viswanathan (CoRR, 2021)

Bringing Atomistic Deep Learning to Prime Time. - Nathan C. Frey; Siddharth Samsi; Bharath Ramsundar; Connor W. Coley; Vijay Gadepally (CoRR, 2021)

FastFlows: Flow-Based Models for Molecular Graph Generation. - Nathan C. Frey; Vijay Gadepally; Bharath Ramsundar (CoRR, 2022)

Score-Based Generative Models for Molecule Generation. - Dwaraknath Gnaneshwar; Bharath Ramsundar; Dhairya Gandhi; Rachel C. Kurchin; Venkatasubramanian Viswanathan (CoRR, 2022)

ChemBERTa-2: Towards Chemical Foundation Models. - Walid Ahmad; Elana Simon; Seyone Chithrananda; Gabriel Grand; Bharath Ramsundar (CoRR, 2022)

Open Source Infrastructure for Differentiable Density Functional Theory. - Advika Vidhyadhiraja; Arun Pa Thiagarajan; Shang Zhu; Venkat Viswanathan; Bharath Ramsundar (CoRR, 2023)

Differentiable Chemical Physics by Geometric Deep Learning for Gradient-based Property Optimization of Mixtures. - Shang Zhu; Bharath Ramsundar; Emil Annevelink; Hongyi Lin; Adarsh Dave; Pin-Wen Guan; Kevin Gering; Venkatasubramanian Viswanathan (CoRR, 2023)

Scientific discovery in the age of artificial intelligence. - Hanchen Wang 002; Tianfan Fu; Yuanqi Du; Wenhao Gao 001; Kexin Huang; Ziming Liu; Payal Chandak; Shengchao Liu; Peter Van Katwyk; Andreea Deac; Anima Anandkumar; Karianne Bergen; et al. (Nat., 2023)

Poster: Ensemble Methods for ADR Prediction. - Danika Gupta; Bharath Ramsundar (CHASE, 2024)

Open-Source Fermionic Neural Networks with Ionic Charge Initialization. - Shai Pranesh; Shang Zhu; Venkat Viswanathan; Bharath Ramsundar (CoRR, 2024)

Self-supervised Pretraining for Partial Differential Equations. - Varun Madhavan; Amal S. Sebastian; Bharath Ramsundar; Venkatasubramanian Viswanathan (CoRR, 2024)

Open-Source Molecular Processing Pipeline for Generating Molecules. - V. Shreyas; Jose Siguenza; Karan Bania; Bharath Ramsundar (CoRR, 2024)

Open Source Infrastructure for Automatic Cell Segmentation. - Aaron Rock Menezes; Bharath Ramsundar (CoRR, 2024)

A Modular Open Source Framework for Genomic Variant Calling. - Ankita Vaishnobi Bisoi; Bharath Ramsundar (CoRR, 2024)

Open source Differentiable ODE Solving Infrastructure. - Rakshit Kr. Singh; Aaron Rock Menezes; Rida Irfan; Bharath Ramsundar (CoRR, 2024)

Open-Source Protein Language Models for Function Prediction and Protein Design. - Shivasankaran Vanaja Pandi; Bharath Ramsundar (CoRR, 2024)

DeepRetro: Retrosynthetic Pathway Discovery using Iterative LLM Reasoning. - Shreyas Vinaya Sathyanarayana; Rahil Shah; Sharanabasava D. Hiremath; Rishikesh Panda; Rahul Jana; Riya Singh; Rida Irfan; Ashwin Murali; Bharath Ramsundar (CoRR, 2025)