10 code implementations • ICLR 2020 • Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.
Ranked #3 on Molecular Property Prediction on ToxCast
no code implementations • 24 Jul 2018 • Rishi Sharma, Shane Barratt, Stefano Ermon, Vijay Pande
We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation.
no code implementations • 24 Jul 2018 • Rishi Sharma, Amir Barati Farimani, Joe Gomes, Peter Eastman, Vijay Pande
In typical machine learning tasks and applications, it is necessary to obtain or create large labeled datasets in order to to achieve high performance.
2 code implementations • NeurIPS 2018 • Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.
no code implementations • 6 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.
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.
no code implementations • 10 Nov 2016 • Han Altae-Tran, Bharath Ramsundar, Aneesh S. Pappu, Vijay Pande
Recent advances in machine learning have made significant contributions to drug discovery.
Ranked #2 on Molecular Property Prediction on Tox21
no code implementations • 28 Jun 2016 • Steven Kearnes, Brian Goldman, Vijay Pande
Deep learning methods such as multitask neural networks have recently been applied to ligand-based virtual screening and other drug discovery applications.
1 code implementation • 6 Jun 2016 • Steven Kearnes, Vijay Pande
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity.
2 code implementations • 2 Mar 2016 • Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications.
Ranked #4 on Graph Regression on Lipophilicity
no code implementations • 6 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.