Search Results for author: Bharath Ramsundar

Found 21 papers, 6 papers with code

Open-Source Fermionic Neural Networks with Ionic Charge Initialization

no code implementations16 Jan 2024 Shai Pranesh, Shang Zhu, Venkat Viswanathan, Bharath Ramsundar

Finding accurate solutions to the electronic Schr\"odinger equation plays an important role in discovering important molecular and material energies and characteristics.

Variational Monte Carlo

Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning

no code implementations3 Oct 2023 Shang Zhu, Bharath Ramsundar, Emil Annevelink, Hongyi Lin, Adarsh Dave, Pin-Wen Guan, Kevin Gering, Venkatasubramanian Viswanathan

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces.

Open Source Infrastructure for Differentiable Density Functional Theory

no code implementations27 Sep 2023 Advika Vidhyadhiraja, Arun Pa Thiagarajan, Shang Zhu, Venkat Viswanathan, Bharath Ramsundar

Learning exchange correlation functionals, used in quantum chemistry calculations, from data has become increasingly important in recent years, but training such a functional requires sophisticated software infrastructure.

ChemBERTa-2: Towards Chemical Foundation Models

2 code implementations5 Sep 2022 Walid Ahmad, Elana Simon, Seyone Chithrananda, Gabriel Grand, Bharath Ramsundar

Large pretrained models such as GPT-3 have had tremendous impact on modern natural language processing by leveraging self-supervised learning to learn salient representations that can be used to readily finetune on a wide variety of downstream tasks.

Molecular Property Prediction Self-Supervised Learning

Score-Based Generative Models for Molecule Generation

no code implementations7 Mar 2022 Dwaraknath Gnaneshwar, Bharath Ramsundar, Dhairya Gandhi, Rachel Kurchin, Venkatasubramanian Viswanathan

Recent advances in generative models have made exploring design spaces easier for de novo molecule generation.

Benchmarking

FastFlows: Flow-Based Models for Molecular Graph Generation

3 code implementations28 Jan 2022 Nathan C. Frey, Vijay Gadepally, Bharath Ramsundar

We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules.

Graph Generation Molecular Graph Generation +1

Differentiable Physics: A Position Piece

no code implementations14 Sep 2021 Bharath Ramsundar, Dilip Krishnamurthy, Venkatasubramanian Viswanathan

Differentiable physics provides a new approach for modeling and understanding the physical systems by pairing the new technology of differentiable programming with classical numerical methods for physical simulation.

Position

Identification and Development of Therapeutics for COVID-19

no code implementations3 Mar 2021 Halie M. Rando, Nils Wellhausen, Soumita Ghosh, Alexandra J. Lee, Anna Ada Dattoli, Fengling Hu, James Brian Byrd, Diane N. Rafizadeh, Ronan Lordan, Yanjun Qi, Yuchen Sun, Christian Brueffer, Jeffrey M. Field, Marouen Ben Guebila, Nafisa M. Jadavji, Ashwin N. Skelly, Bharath Ramsundar, Jinhui Wang, Rishi Raj Goel, YoSon Park, the COVID-19 Review Consortium, Simina M. Boca, Anthony Gitter, Casey S. Greene

A number of potential therapeutics against SARS-CoV-2 and the resultant COVID-19 illness were rapidly identified, leading to a large number of clinical trials investigating a variety of possible therapeutic approaches being initiated early on in the pandemic.

AMPL: A Data-Driven Modeling Pipeline for Drug Discovery

2 code implementations13 Nov 2019 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

The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of machine learning and molecular featurization tools.

BIG-bench Machine Learning Drug Discovery +2

Retrosynthetic reaction prediction using neural sequence-to-sequence models

no code implementations6 Jun 2017 Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender, Vijay Pande

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem.

Machine Translation Translation

Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity

3 code implementations30 Mar 2017 Joseph Gomes, Bharath Ramsundar, Evan N. Feinberg, Vijay S. Pande

The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose.

Drug Discovery Molecular Docking

MoleculeNet: A Benchmark for Molecular Machine Learning

5 code implementations2 Mar 2017 Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, Vijay Pande

However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods.

BIG-bench Machine Learning imbalanced classification

Learning Protein Dynamics with Metastable Switching Systems

no code implementations5 Oct 2016 Bharath Ramsundar, Vijay S. Pande

We apply our EM algorithm to learn accurate dynamics from large simulation datasets for the opioid peptide met-enkephalin and the proto-oncogene Src-kinase.

Massively Multitask Networks for Drug Discovery

no code implementations6 Feb 2015 Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande

Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources.

Drug Discovery

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

no code implementations6 May 2014 Robert T. McGibbon, Bharath Ramsundar, Mohammad M. Sultan, Gert Kiss, Vijay S. Pande

We present an EM algorithm for learning and introduce a model selection criteria based on the physical notion of convergence in relaxation timescales.

Distributed Computing Model Selection

The Extended Parameter Filter

no code implementations8 May 2013 Yusuf Erol, Lei LI, Bharath Ramsundar, Stuart J. Russell

Drawing on an analogy to the extended Kalman filter, we develop and analyze, both theoretically and experimentally, a Taylor approximation to the parameter posterior that allows Storvik's method to be applied to a broader class of models.

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