98 citations · 182 across the 7 of their papers we have counts for
7 papers
Pearl: A Foundation Model for Placing Every Atom in the Right Location
Genesis Research Team, Alejandro Dobles, Nina Jovic +37
Accurately predicting the three-dimensional structures of protein-ligand complexes remains a fundamental challenge in computational drug discovery that limits the pace and success…
Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization
Evan N. Feinberg, Robert Sheridan, Elizabeth Joshi +2
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. Pre…
Binding Pathway of Opiates to Opioid Receptors Revealed by Unsupervised Machine Learning
Amir Barati Farimani, Evan N. Feinberg, Vijay S. Pande
Many important analgesics relieve pain by binding to the -Opioid Receptor (OR), which makes the OR among the most clinically relevant proteins of the G Protein Coupled Rec…
Machine Learning Harnesses Molecular Dynamics to Discover New Opioid Chemotypes
Evan N. Feinberg, Amir Barati Farimani, Rajendra Uprety +4
Computational chemists typically assay drug candidates by virtually screening compounds against crystal structures of a protein despite the fact that some targets, like the Opi…
PotentialNet for Molecular Property Prediction
Evan N. Feinberg, Debnil Sur, Zhenqin Wu +7
The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales. They key parameters range from solubility (angstroms) to protein-ligand binding…
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
Joseph Gomes, Bharath Ramsundar, Evan N. Feinberg +1
Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of…
MoleculeNet: A Benchmark for Molecular Machine Learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg +5
Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make…