no code implementations • 28 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.
no code implementations • 12 Mar 2018 • Evan N. Feinberg, Debnil Sur, Zhenqin Wu, Brooke E. Husic, Huanghao Mai, Yang Li, Saisai Sun, Jianyi Yang, Bharath Ramsundar, Vijay S. Pande
The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales.
no code implementations • 12 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.
3 code implementations • 30 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.
5 code implementations • 2 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.