Search Results for author: Vijay S. Pande

Found 18 papers, 4 papers with code

Folding@home: achievements from over twenty years of citizen science herald the exascale era

no code implementations15 Mar 2023 Vincent A. Voelz, Vijay S. Pande, Gregory R. Bowman

Simulations of biomolecules have enormous potential to inform our understanding of biology but require extremely demanding calculations.

Distributed Computing Protein Folding

Classical Quantum Optimization with Neural Network Quantum States

1 code implementation Second Workshop on Machine Learning and the Physical Sciences (NeurIPS 2019) 2019 Joseph Gomes, Keri A. McKiernan, Peter Eastman, Vijay S. Pande

The classical simulation of quantum systems typically requires exponential resources.

Disordered Systems and Neural Networks Strongly Correlated Electrons Quantum Physics

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

Deep Learning Phase Segregation

no code implementations23 Mar 2018 Amir Barati Farimani, Joseph Gomes, Rishi Sharma, Franklin L. Lee, Vijay S. Pande

Phase segregation, the process by which the components of a binary mixture spontaneously separate, is a key process in the evolution and design of many chemical, mechanical, and biological systems.

Note: Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelation

no code implementations17 Mar 2018 Hannah K. Wayment-Steele, Vijay S. Pande

We thus recommend leveraging the autocorrelation of the latent space while training neural network models of biomolecular simulation data to better represent slow processes.

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

SentRNA: Improving computational RNA design by incorporating a prior of human design strategies

no code implementations8 Mar 2018 Jade Shi, Rhiju Das, Vijay S. Pande

Here we present a novel approach to the RNA design problem, SentRNA, a design agent consisting of a fully-connected neural network trained end-to-end using human-designed RNA sequences.

Using Deep Learning for Segmentation and Counting within Microscopy Data

1 code implementation28 Feb 2018 Carlos X. Hernández, Mohammad M. Sultan, Vijay S. Pande

Cell counting is a ubiquitous, yet tedious task that would greatly benefit from automation.

Automated design of collective variables using supervised machine learning

no code implementations28 Feb 2018 Mohammad M. Sultan, Vijay S. Pande

In particular, we show how the decision functions in supervised machine learning (SML) algorithms can be used as initial CVs (SML_cv) for accelerated sampling.

BIG-bench Machine Learning

Transferable neural networks for enhanced sampling of protein dynamics

no code implementations2 Jan 2018 Mohammad M. Sultan, Hannah K. Wayment-Steele, Vijay S. Pande

In this work, we illustrate how this non-linear latent embedding can be used as a collective variable for enhanced sampling, and present a simple modification that allows us to rapidly perform sampling in multiple related systems.

Unsupervised learning of dynamical and molecular similarity using variance minimization

no code implementations20 Dec 2017 Brooke E. Husic, Vijay S. Pande

Then, we extend the method to partition two chemoinformatic datasets according to structural similarity to motivate a train/validation/test split for supervised learning that avoids overfitting.

BIG-bench Machine Learning Clustering

Variational Encoding of Complex Dynamics

2 code implementations23 Nov 2017 Carlos X. Hernández, Hannah K. Wayment-Steele, Mohammad M. Sultan, Brooke E. Husic, Vijay S. Pande

Recent work in the field of deep learning has led to the development of variational autoencoders (VAE), which are able to compress complex datasets into simpler manifolds.

Protein Folding Time Series +1

Deep Learning the Physics of Transport Phenomena

no code implementations7 Sep 2017 Amir Barati Farimani, Joseph Gomes, Vijay S. Pande

We have developed a new data-driven paradigm for the rapid inference, modeling and simulation of the physics of transport phenomena by deep 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

Learning Protein Dynamics with Metastable Switching Systems

no code implementations5 Oct 2016 Bharath Ramsundar, Vijay S. Pande

We apply our EM algorithm to learn accurate dynamics from large simulation datasets for the opioid peptide met-enkephalin and the proto-oncogene Src-kinase.

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

no code implementations6 May 2014 Robert T. McGibbon, Bharath Ramsundar, Mohammad M. Sultan, Gert Kiss, Vijay S. Pande

We present an EM algorithm for learning and introduce a model selection criteria based on the physical notion of convergence in relaxation timescales.

Distributed Computing Model Selection

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