Search Results for author: Evan N. Feinberg

Found 5 papers, 2 papers with code

Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization

no code implementations28 Mar 2019 Evan N. Feinberg, Robert Sheridan, Elizabeth Joshi, Vijay S. Pande, Alan C. Cheng

The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) properties of drug candidates are estimated to account for up to 50% of all clinical trial failures.

molecular representation

Machine Learning Harnesses Molecular Dynamics to Discover New $μ$ Opioid Chemotypes

no code implementations12 Mar 2018 Evan N. Feinberg, Amir Barati Farimani, Rajendra Uprety, Amanda Hunkele, Gavril W. Pasternak, Susruta Majumdar, Vijay S. Pande

Computational chemists typically assay drug candidates by virtually screening compounds against crystal structures of a protein despite the fact that some targets, like the $\mu$ Opioid Receptor and other members of the GPCR family, traverse many non-crystallographic states.

BIG-bench Machine Learning

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

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