Bharath Ramsundar | DeepAI

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∙ 09/05/2022

ChemBERTa-2: Towards Chemical Foundation Models

Large pretrained models such as GPT-3 have had tremendous impact on mode...

14 Walid Ahmad, et al. ∙

∙ 03/07/2022

Score-Based Generative Models for Molecule Generation

Recent advances in generative models have made exploring design spaces e...

0 Dwaraknath Gnaneshwar, et al. ∙

∙ 01/28/2022

FastFlows: Flow-Based Models for Molecular Graph Generation

We propose a framework using normalizing-flow based models, SELF-Referen...

0 Nathan C. Frey, et al. ∙

∙ 12/09/2021

Bringing Atomistic Deep Learning to Prime Time

Artificial intelligence has not yet revolutionized the design of materia...

21 Nathan C. Frey, et al. ∙

∙ 09/14/2021

Differentiable Physics: A Position Piece

Differentiable physics provides a new approach for modeling and understa...

0 Bharath Ramsundar, et al. ∙

∙ 10/19/2020

ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

GNNs and chemical fingerprints are the predominant approaches to represe...

13 Seyone Chithrananda, et al. ∙

∙ 11/13/2019

AMPL: A Data-Driven Modeling Pipeline for Drug Discovery

One of the key requirements for incorporating machine learning into the ...

51 Amanda J. Minnich, et al. ∙

∙ 07/02/2019

Secure Computation in Decentralized Data Markets

Decentralized data markets gather data from many contributors to create ...

0 Fattaneh Bayatbabolghani, et al. ∙

∙ 05/31/2018

Tokenized Data Markets

We formalize the construction of decentralized data markets by introduci...

0 Bharath Ramsundar, et al. ∙

∙ 03/12/2018

Spatial Graph Convolutions for Drug Discovery

Predicting the binding free energy, or affinity, of a small molecule for...

0 Evan N. Feinberg, et al. ∙

∙ 06/06/2017

Retrosynthetic reaction prediction using neural sequence-to-sequence models

We describe a fully data driven model that learns to perform a retrosynt...

0 Bowen Liu, et al. ∙

∙ 03/30/2017

Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity

Empirical scoring functions based on either molecular force fields or ch...

0 Joseph Gomes, et al. ∙

∙ 03/02/2017

MoleculeNet: A Benchmark for Molecular Machine Learning

Molecular machine learning has been maturing rapidly over the last few y...

0 Zhenqin Wu, et al. ∙

∙ 11/10/2016

Low Data Drug Discovery with One-shot Learning

Recent advances in machine learning have made significant contributions ...

0 Han Altae-Tran, et al. ∙

∙ 10/05/2016

Learning Protein Dynamics with Metastable Switching Systems

We introduce a machine learning approach for extracting fine-grained rep...

0 Bharath Ramsundar, et al. ∙

∙ 02/06/2015

Massively Multitask Networks for Drug Discovery

Massively multitask neural architectures provide a learning framework fo...

0 Bharath Ramsundar, et al. ∙

∙ 05/06/2014

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

We present a machine learning framework for modeling protein dynamics. O...

0 Robert T. McGibbon, et al. ∙

∙ 05/08/2013

The Extended Parameter Filter

The parameters of temporal models, such as dynamic Bayesian networks, ma...

0 Yusuf Erol, et al. ∙

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