no code implementations • 4 Oct 2023 • Peter Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio, Anton Gorenko, Michael M. Henry, Frank Hu, Jing Huang, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João PGLM Rodrigues, Jaime Rodriguez-Guerra, Andrew C. Simmonett, Sukrit Singh, Jason Swails, Philip Turner, Yuanqing Wang, Ivy Zhang, John D. Chodera, Gianni de Fabritiis, Thomas E. Markland
Machine learning plays an important and growing role in molecular simulation.
no code implementations • 15 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.
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
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 • 23 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.
no code implementations • 17 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.
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.
no code implementations • 8 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.
no code implementations • 28 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.
1 code implementation • 28 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.
no code implementations • 2 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.
no code implementations • 20 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.
2 code implementations • 23 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.
no code implementations • 7 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.
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.
no code implementations • 5 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.
no code implementations • 6 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.