Search Results for author: Vijay Pande

Found 11 papers, 6 papers with code

Strategies for Pre-training Graph Neural Networks

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.

Graph Classification Molecular Property Prediction +4

Improved Training with Curriculum GANs

no code implementations24 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.

Image Generation

Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss

no code implementations24 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.

BIG-bench Machine Learning

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

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.

Graph Generation Molecular Graph Generation

Retrosynthetic reaction prediction using neural sequence-to-sequence models

1 code implementation6 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.

Decoder Machine Translation +2

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

Modeling Industrial ADMET Data with Multitask Networks

no code implementations28 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.

Drug Discovery

ROCS-Derived Features for Virtual Screening

1 code implementation6 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.

BIG-bench Machine Learning

Molecular Graph Convolutions: Moving Beyond Fingerprints

2 code implementations2 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.

BIG-bench Machine Learning Drug Discovery +1

Massively Multitask Networks for Drug Discovery

no code implementations6 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.

Drug Discovery

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