Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

20 May 2020

Papers citing "Uncertainty Quantification Using Neural Networks for Molecular Property Prediction"

21 / 21 papers shown

Title
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design A. N. M. N. Abeer Sanket R. Jantre Nathan M. Urban Byung-Jun Yoon 44 1 0 30 Apr 2024
Kermut: Composite kernel regression for protein variant effects Peter Mørch Groth Mads Herbert Kerrn Lars Olsen Jesper Salomon Wouter Boomsma 36 2 0 09 Apr 2024
Pareto Optimization to Accelerate Multi-Objective Virtual Screening Jenna C. Fromer David E. Graff Connor W. Coley 23 7 0 16 Oct 2023
Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening Zhonglin Cao Simone Sciabola Ye Wang 32 1 0 20 Sep 2023
Development and Evaluation of Conformal Prediction Methods for QSAR Yuting Xu Andy Liaw R. Sheridan V. Svetnik 21 2 0 03 Apr 2023
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials Albert J. W. Zhu Simon L. Batzner Albert Musaelian Boris Kozinsky 22 45 0 17 Nov 2022
Confidence-Nets: A Step Towards better Prediction Intervals for regression Neural Networks on small datasets M. Altayeb A. Elamin Hozaifa Ahmed Eithar Elfatih Elfadil Ibrahim Omer Haydar Saba Abdulaziz Najlaa H. M. Mohamed UQCV 13 0 0 31 Oct 2022
Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity Nikhil V. S. Avula S. K. Veesam Sudarshan Behera S. Balasubramanian 24 8 0 23 Aug 2022
Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design Hengrui Zhang WeiWayneChen Akshay Iyer D. Apley Wei-Neng Chen AI4CE 31 11 0 11 Jul 2022
Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring Hans Weytjens Jochen De Weerdt 19 17 0 13 Jun 2022
Self-focusing virtual screening with active design space pruning David E. Graff Matteo Aldeghi Joseph A. Morrone K. E. Jordan Edward O. Pyzer-Knapp Connor W. Coley 24 24 0 03 May 2022
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification Maximilian Stadler Bertrand Charpentier Simon Geisler Daniel Zügner Stephan Günnemann UQCV BDL 28 80 0 26 Oct 2021
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions Nicholas Gao Stephan Günnemann 19 36 0 11 Oct 2021
An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions Afshar Shamsi Hamzeh Asgharnezhad AmirReza Tajally Saeid Nahavandi Henry Leung UQCV 38 6 0 07 Oct 2021
Few-shot Conformal Prediction with Auxiliary Tasks Adam Fisch Tal Schuster Tommi Jaakkola Regina Barzilay 181 53 0 17 Feb 2021
Accelerating high-throughput virtual screening through molecular pool-based active learning David E. Graff E. Shakhnovich Connor W. Coley 76 142 0 13 Dec 2020
Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and a Path to Best Practices for Machine Learning in Chemistry G. Vishwakarma Aditya Sonpal J. Hachmann 4 46 0 30 Sep 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 25 625 0 01 Jul 2020
Deep Learning and Knowledge-Based Methods for Computer Aided Molecular Design -- Toward a Unified Approach: State-of-the-Art and Future Directions Abdulelah S. Alshehri R. Gani Fengqi You AI4CE 22 83 0 18 May 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 270 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015